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Xiaojun Chang

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AAAI Conference 2026 Conference Paper

Correspondence Coverage Matters for Multi-Modal Dataset Distillation

  • Zhuohang Dang
  • Minnan Luo
  • Chengyou Jia
  • Hangwei Qian
  • Xinyu Zhang
  • Xiaojun Chang
  • Ivor Tsang

Multi-modal dataset distillation (DD) condenses large datasets into compact ones that retain task efficacy by capturing correspondence patterns, i.e., shared semantics between paired modalities. However, such patterns rely on cross-modal similarity and cannot be faithfully captured by intra-modal similarity of current unimodal strategies. As a result, current multi-modal DD methods tend to over-concentrate, redundantly encoding similar correspondence patterns and thus limiting generalizability. To this end, we propose a novel multi-modal DD framework to systematically Promote Correspondence coverage, i.e., ProCo. Initially, we develop a correspondence consistency metric based on cross-modal retrieval distributions to cluster correspondence patterns. These clusters capture the underlying correspondence distribution, enabling ProCo to initialize distilled data with representative patterns while regularizing optimization to promote correspondence representativeness and diversity. Moreover, we employ conditional neural fields for efficient distilled data parameterization, enhancing fine-grained pattern capture while allowing more distilled data under a fixed budget to boost correspondence coverage. Extensive experiments verify that our ProCo achieves superior and elastic budget-efficacy trade-offs, surpassing prior methods by over 15% with 10x distillation budget reduction, highlighting its real-world practicality.

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AAAI Conference 2026 Conference Paper

MDK12-Bench: A Multi-Discipline Benchmark for Evaluating Reasoning in Multimodal Large Language Models

  • Pengfei Zhou
  • Xiaopeng Peng
  • Fanrui Zhang
  • Zhaopan Xu
  • Jiaxin Ai
  • Yansheng Qiu
  • Wangbo Zhao
  • Jiajun Song

Multimodal large language models (MLLMs), which integrate language and visual cues for problem-solving, are crucial for advancing artificial general intelligence (AGI). However, current benchmarks for measuring the intelligence of MLLMs suffer from limited scale, narrow coverage, and unstructured knowledge, offering only static and undifferentiated evaluations. To bridge this gap, we introduce MDK12-Bench, a large-scale multidisciplinary benchmark built from real-world K–12 exams spanning six disciplines with 141K instances and 6,225 knowledge points organized in a six-layer taxonomy. Covering five question formats with difficulty and year annotations, it enables comprehensive evaluation to capture the extent to which MLLMs perform over four dimensions: 1) difficulty levels, 2) temporal (cross-year) shifts, 3) contextual shifts, and 4) knowledge-driven reasoning. We propose a novel dynamic evaluation framework that introduces unfamiliar visual, textual, and question form shifts to challenge model generalization while improving benchmark objectivity and longevity by mitigating data contamination. We further evaluate knowledge-point reference-augmented generation (KP-RAG) to examine the role of knowledge in reasoning. Key findings reveal limitations in current MLLMs in multiple aspects and provide guidance for enhancing model reasoning, robustness, and AI-assisted education.

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TIST Journal 2026 Journal Article

Mitigating Data Redundancy to Revitalize Transformer-Based Long-Term Time Series Forecasting System

  • Mingjie Li
  • Rui Liu
  • Guangsi Shi
  • Mingfei Han
  • Changlin Li
  • Lina Yao
  • Xiaojun Chang
  • Ling Chen

Long-term time series forecasting (LTSF) is fundamental to various real-world applications, where Transformer-based models have become the dominant framework due to their ability to capture long-range dependencies. However, these models often experience overfitting due to data redundancy in rolling forecasting settings, limiting their generalization ability particularly evident in longer sequences with highly similar adjacent data. In this work, we introduce CLMFormer, a novel framework that mitigates redundancy through curriculum learning and a memory-driven decoder. Specifically, we progressively introduce Bernoulli noise to the training samples, which effectively breaks the high similarity between adjacent data points. This curriculum-driven noise introduction aids the memory-driven decoder by supplying more diverse and representative training data, enhancing the decoder’s ability to model seasonal tendencies and dependencies in the time series data. To further enhance forecasting accuracy, we introduce a memory-driven decoder. This component enables the model to capture seasonal tendencies and dependencies in the time series data and leverages temporal relationships to facilitate the forecasting process. Extensive experiments on six real-world LTSF benchmarks show that CLMFormer consistently improves Transformer-based models by up to 30%, demonstrating its effectiveness in long-horizon forecasting.

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TMLR Journal 2026 Journal Article

Order from Chaos: Physical World Understanding from Glitchy Gameplay Videos

  • Meng Cao
  • Haoran Tang
  • Haoze Zhao
  • Mingfei Han
  • Ruyang Liu
  • Qiang Sun
  • Xiaojun Chang
  • Ian Reid

Understanding the physical world, including object dynamics, material properties, and causal interactions, remains a core challenge in artificial intelligence. Although recent multi-modal large language models (MLLMs) have demonstrated impressive general reasoning capabilities, they still fall short of achieving human-level understanding of physical principles. Existing datasets for physical reasoning either rely on real-world videos, which incur high annotation costs, or on synthetic simulations, which suffer from limited realism and diversity. In this paper, we propose a novel paradigm that leverages glitches in gameplay videos, referring to visual anomalies that violate predefined physical laws, as a rich and scalable supervision source for physical world understanding. We introduce PhysGame, an instruction-tuning dataset containing 140,057 glitch-centric question–answer pairs across five physical domains and sixteen fine-grained categories. To ensure data accuracy, we design a meta-information–guided prompting strategy that utilizes gameplay metadata such as titles and descriptions to guide high-quality QA generation. Complementing PhysGame, we construct GameBench, an expert-annotated benchmark with 880 glitch-identified gameplay videos designed to evaluate physical reasoning capabilities. Extensive experiments show that PhysGame significantly enhances both Game2Real transferability, improving the real-world physical reasoning performance of Qwen2.5-VL by 2.5% on PhysBench, and Game2General transferability, yielding a 1.9% gain on the MVBench benchmark. Moreover, PhysGame-tuned models achieve a 3.7% absolute improvement on GameBench, demonstrating enhanced robustness in detecting physical implausibilities. These results indicate that learning from gameplay anomalies offers a scalable and effective pathway toward advancing physical world understanding in multimodal intelligence.

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AAAI Conference 2026 Conference Paper

Think Before You Segment: An Object-aware Reasoning Agent for Referring Audio-Visual Segmentation

  • Jinxing Zhou
  • Yanghao Zhou
  • Mingfei Han
  • Tong Wang
  • Xiaojun Chang
  • Hisham Cholakkal
  • Rao Muhammad Anwer

Referring Audio-Visual Segmentation (Ref-AVS) aims to segment target objects in audible videos based on given reference expressions. Prior works typically rely on learning latent embeddings via multimodal fusion to prompt a tunable SAM/SAM2 decoder for segmentation, which requires strong pixel-level supervision and lacks interpretability. From a novel perspective of explicit reference understanding, we propose TGS-Agent, which decomposes the task into a Think-Ground-Segment process, mimicking the human reasoning procedure by first identifying the referred object through multimodal analysis, followed by coarse-grained grounding and precise segmentation. To this end, we first propose Ref-Thinker, a multimodal language model capable of reasoning over textual, visual, and auditory cues. We construct an instruction-tuning dataset with explicit object-aware think-answer chains for Ref-Thinker fine-tuning. The object description inferred by Ref-Thinker is used as an explicit prompt for Grounding-DINO and SAM2, which perform grounding and segmentation without relying on pixel-level supervision. Additionally, we introduce R2-AVSBench, a new benchmark with linguistically diverse and reasoning-intensive references for better evaluating model generalization. Our approach achieves state-of-the-art results on both standard Ref-AVSBench and proposed R2-AVSBench.

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AAAI Conference 2026 Conference Paper

Token Painter: Training-Free Text-Guided Image Inpainting via Mask Autoregressive Models

  • Longtao Jiang
  • Jie Huang
  • Mingfei Han
  • Lei Chen
  • Yongqiang Yu
  • Feng Zhao
  • Xiaojun Chang
  • Zhihui Li

Text-guided image inpainting aims to inpaint masked image regions based on a textual prompt while preserving the background. Although diffusion-based methods have become dominant, their property of modeling the entire image in latent space makes it challenging for the results to align well with prompt details and maintain a consistent background. To address these issues, we explore Mask AutoRegressive (MAR) models for this task. MAR naturally supports image inpainting by generating latent tokens corresponding to mask regions, enabling better local controllability without altering the background. However, directly applying MAR to this task makes the inpainting content either ignore the prompts or be disharmonious with the background context. Through analysis of the attention maps from the inpainting images, we identify the impact of background tokens on text tokens during the MAR generation, and leverage this to designToken Painter, a training-free text-guided image inpainting method based on MAR. Our approach introduces two key components: (1) Dual-Stream Encoder Information Fusion (DEIF), which fuses the semantic and context information from text and background in frequency domain to produce novel guidance tokens, allowing MAR to generate text-faithful inpainting content while keeping harmonious with background context. (2) Adaptive Decoder Attention Score Enhancing (ADAE), which adaptively enhances attention scores on guidance tokens and inpainting tokens to further enhance the alignment of prompt details and the content visual quality. Extensive experiments demonstrate that our training-free method outperforms prior state-of-the-art methods across almost all metrics.

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AAAI Conference 2025 Conference Paper

Dense Audio-Visual Event Localization Under Cross-Modal Consistency and Multi-Temporal Granularity Collaboration

  • Ziheng Zhou
  • Jinxing Zhou
  • Wei Qian
  • Shengeng Tang
  • Xiaojun Chang
  • Dan Guo

In the field of audio-visual learning, most research tasks focus exclusively on short videos. This paper focuses on the more practical Dense Audio-Visual Event Localization (DAVEL) task, advancing audio-visual scene understanding for longer, untrimmed videos. This task seeks to identify and temporally pinpoint all events simultaneously occurring in both audio and visual streams. Typically, each video encompasses dense events of multiple classes, which may overlap on the timeline, each exhibiting varied durations. Given these challenges, effectively exploiting the audio-visual relations and the temporal features encoded at various granularities becomes crucial. To address these challenges, we introduce a novel CCNet, comprising two core modules: the Cross-Modal Consistency Collaboration (CMCC) and the Multi-Temporal Granularity Collaboration (MTGC). Specifically, the CMCC module contains two branches: a cross-modal interaction branch and a temporal consistency-gated branch. The former branch facilitates the aggregation of consistent event semantics across modalities through the encoding of audio-visual relations, while the latter branch guides one modality's focus to pivotal event-relevant temporal areas as discerned in the other modality. The MTGC module includes a coarse-to-fine collaboration block and a fine-to-coarse collaboration block, providing bidirectional support among coarse- and fine-grained temporal features. Extensive experiments on the UnAV-100 dataset validate our module design, resulting in a new state-of-the-art performance in dense audio-visual event localization.

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AAAI Conference 2025 Conference Paper

HC-LLM: Historical-Constrained Large Language Models for Radiology Report Generation

  • Tengfei Liu
  • Jiapu Wang
  • Yongli Hu
  • Mingjie Li
  • Junfei Yi
  • Xiaojun Chang
  • Junbin Gao
  • Baocai Yin

Radiology report generation (RRG) models typically focus on individual exams, often overlooking the integration of historical visual or textual data, which is crucial for patient follow-ups. Traditional methods usually struggle with long sequence dependencies when incorporating historical information, but large language models (LLMs) excel at in-context learning, making them well-suited for analyzing longitudinal medical data. In light of this, we propose a novel Historical-Constrained Large Language Models (HC-LLM) framework for RRG, empowering LLMs with longitudinal report generation capabilities by constraining the consistency and differences between longitudinal images and their corresponding reports. Specifically, our approach extracts both time-shared and time-specific features from longitudinal chest X-rays and diagnostic reports to capture disease progression. Then, we ensure consistent representation by applying intra-modality similarity constraints and aligning various features across modalities with multimodal contrastive and structural constraints. These combined constraints effectively guide the LLMs in generating diagnostic reports that accurately reflect the progression of the disease, achieving state-of-the-art results on the Longitudinal-MIMIC dataset. Notably, our approach performs well even without historical data during testing and can be easily adapted to other multimodal large models, enhancing its versatility.

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ICML Conference 2025 Conference Paper

Let LLM Tell What to Prune and How Much to Prune

  • Mingzhe Yang
  • Sihao Lin
  • Changlin Li
  • Xiaojun Chang

Large language models (LLMs) have revolutionized various AI applications. However, their billions of parameters pose significant challenges for practical deployment. Structured pruning is a hardware-friendly compression technique and receives widespread attention. Nonetheless, existing literature typically targets a single structure of LLMs. We observe that the structure units of LLMs differ in terms of inference cost and functionality. Therefore, pruning a single structure unit in isolation often results in an imbalance between performance and efficiency. In addition, previous works mainly employ a prescribed pruning ratio. Since the significance of LLM modules may vary, it is ideal to distribute the pruning load to a specific structure unit according to its role within LLMs. To address the two issues, we propose a pruning method that targets multiple LLM modules with dynamic pruning ratios. Specifically, we find the intrinsic properties of LLMs can guide us to determine the importance of each module and thus distribute the pruning load on demand, i. e. , what to prune and how much to prune. This is achieved by quantifying the complex interactions within LLMs. Extensive experiments on multiple benchmarks and LLM variants demonstrate that our method effectively balances the trade-off between efficiency and performance.

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NeurIPS Conference 2025 Conference Paper

Neural-Driven Image Editing

  • Pengfei Zhou
  • Jie Xia
  • Xiaopeng Peng
  • Wangbo Zhao
  • Zilong Ye
  • Zekai Li
  • Suorong Yang
  • Jiadong Pan

Traditional image editing typically relies on manual prompting, making it labor-intensive and inaccessible to individuals with limited motor control or language abilities. Leveraging recent advances in brain-computer interfaces (BCIs) and generative models, we propose LoongX, a hands-free image editing approach driven by multimodal neurophysiological signals. LoongX utilizes state-of-the-art diffusion models trained on a comprehensive dataset of 23, 928 image editing pairs, each paired with synchronized electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), photoplethysmography (PPG), and head motion signals that capture user intent. To effectively address the heterogeneity of these signals, LoongX integrates two key modules. The cross-scale state space (CS3) module encodes informative modality-specific features. The dynamic gated fusion (DGF) module further aggregates these features into a unified latent space, which is then aligned with edit semantics via fine-tuning on a diffusion transformer (DiT). Additionally, we pre-train the encoders using contrastive learning to align cognitive states with semantic intentions from embedded natural language. Extensive experiments demonstrate that LoongX achieves performance comparable to text-driven methods (CLIP-I: 0. 6605 vs. 0. 6558; DINO: 0. 4812 vs. 0. 4637) and outperforms them when neural signals are combined with speech (CLIP-T: 0. 2588 vs. 0. 2549). These results highlight the promise of neural-driven generative models in enabling accessible, intuitive image editing and open new directions for cognitive-driven creative technologies. The code and dataset are released on the project website: https: //loongx1. github. io.

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ICLR Conference 2025 Conference Paper

Shot2Story: A New Benchmark for Comprehensive Understanding of Multi-shot Videos

  • Mingfei Han 0002
  • Linjie Yang
  • Xiaojun Chang
  • Lina Yao 0001
  • Heng Wang

A short clip of video may contain progression of multiple events and an interesting story line. A human need to capture both the event in every shot and associate them together to understand the story behind it. In this work, we present a new multi-shot video understanding benchmark \dataset with detailed shot-level captions, comprehensive video summaries and question-answering pairs. To facilitate better semantic understanding of videos, we provide captions for both visual signals and human narrations. We design several distinct tasks including single-shot video captioning, multi-shot video summarization, and multi-shot video question answering. Preliminary experiments show some challenges to generate a long and comprehensive video summary for multi-shot videos. Nevertheless, the generated imperfect summaries can already achieve competitive performance on existing video understanding tasks such as video question-answering, promoting an under-explored setting of video understanding with detailed summaries.

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ICLR Conference 2025 Conference Paper

Sitcom-Crafter: A Plot-Driven Human Motion Generation System in 3D Scenes

  • Jianqi Chen
  • Panwen Hu
  • Xiaojun Chang
  • Zhenwei Shi
  • Michael Kampffmeyer
  • Xiaodan Liang

Recent advancements in human motion synthesis have focused on specific types of motions, such as human-scene interaction, locomotion or human-human interaction, however, there is a lack of a unified system capable of generating a diverse combination of motion types. In response, we introduce *Sitcom-Crafter*, a comprehensive and extendable system for human motion generation in 3D space, which can be guided by extensive plot contexts to enhance workflow efficiency for anime and game designers. The system is comprised of eight modules, three of which are dedicated to motion generation, while the remaining five are augmentation modules that ensure consistent fusion of motion sequences and system functionality. Central to the generation modules is our novel 3D scene-aware human-human interaction module, which addresses collision issues by synthesizing implicit 3D Signed Distance Function (SDF) points around motion spaces, thereby minimizing human-scene collisions without additional data collection costs. Complementing this, our locomotion and human-scene interaction modules leverage existing methods to enrich the system's motion generation capabilities. Augmentation modules encompass plot comprehension for command generation, motion synchronization for seamless integration of different motion types, hand pose retrieval to enhance motion realism, motion collision revision to prevent human collisions, and 3D retargeting to ensure visual fidelity. Experimental evaluations validate the system's ability to generate high-quality, diverse, and physically realistic motions, underscoring its potential for advancing creative workflows. Code and demonstration videos can be found in the supplementary files.

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ICLR Conference 2024 Conference Paper

Masked Distillation Advances Self-Supervised Transformer Architecture Search

  • Caixia Yan
  • Xiaojun Chang
  • Zhihui Li 0001
  • Lina Yao 0001
  • Minnan Luo
  • Qinghua Zheng

Transformer architecture search (TAS) has achieved remarkable progress in automating the neural architecture design process of vision transformers. Recent TAS advancements have discovered outstanding transformer architectures while saving tremendous labor from human experts. However, it is still cumbersome to deploy these methods in real-world applications due to the expensive costs of data labeling under the supervised learning paradigm. To this end, this paper proposes a masked image modelling (MIM) based self-supervised neural architecture search method specifically designed for vision transformers, termed as MaskTAS, which completely avoids the expensive costs of data labeling inherited from supervised learning. Based on the one-shot NAS framework, MaskTAS requires to train various weight-sharing subnets, which can easily diverged without strong supervision in MIM-based self-supervised learning. For this issue, we design the search space of MaskTAS as a siamesed teacher-student architecture to distill knowledge from pre-trained networks, allowing for efficient training of the transformer supernet. To achieve self-supervised transformer architecture search, we further design a novel unsupervised evaluation metric for the evolutionary search algorithm, where each candidate of the student branch is rated by measuring its consistency with the larger teacher network. Extensive experiments demonstrate that the searched architectures can achieve state-of-the-art accuracy on CIFAR-10, CIFAR-100, and ImageNet datasets even without using manual labels. Moreover, the proposed MaskTAS can generalize well to various data domains and tasks by searching specialized transformer architectures in self-supervised manner.

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ICLR Conference 2024 Conference Paper

Maximum Entropy Heterogeneous-Agent Reinforcement Learning

  • Jiarong Liu
  • Yifan Zhong
  • Siyi Hu
  • Haobo Fu
  • Qiang Fu 0016
  • Xiaojun Chang
  • Yaodong Yang 0001

*Multi-agent reinforcement learning* (MARL) has been shown effective for cooperative games in recent years. However, existing state-of-the-art methods face challenges related to sample complexity, training instability, and the risk of converging to a suboptimal Nash Equilibrium. In this paper, we propose a unified framework for learning \emph{stochastic} policies to resolve these issues. We embed cooperative MARL problems into probabilistic graphical models, from which we derive the maximum entropy (MaxEnt) objective for MARL. Based on the MaxEnt framework, we propose *Heterogeneous-Agent Soft Actor-Critic* (HASAC) algorithm. Theoretically, we prove the monotonic improvement and convergence to *quantal response equilibrium* (QRE) properties of HASAC. Furthermore, we generalize a unified template for MaxEnt algorithmic design named *Maximum Entropy Heterogeneous-Agent Mirror Learning* (MEHAML), which provides any induced method with the same guarantees as HASAC. We evaluate HASAC on six benchmarks: Bi-DexHands, Multi-Agent MuJoCo, StarCraft Multi-Agent Challenge, Google Research Football, Multi-Agent Particle Environment, and Light Aircraft Game. Results show that HASAC consistently outperforms strong baselines, exhibiting better sample efficiency, robustness, and sufficient exploration.

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AAAI Conference 2024 Conference Paper

Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation

  • Zhuohang Dang
  • Minnan Luo
  • Chengyou Jia
  • Guang Dai
  • Xiaojun Chang
  • Jingdong Wang

Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. Recently, to alleviate expensive data collection, co-occurring pairs from the Internet are automatically harvested for training. However, it inevitably includes mismatched pairs, i.e., noisy correspondences, undermining supervision reliability and degrading performance. Current methods leverage deep neural networks' memorization effect to address noisy correspondences, which overconfidently focus on similarity-guided training with hard negatives and suffer from self-reinforcing errors. In light of above, we introduce a novel noisy correspondence learning framework, namely Self-Reinforcing Errors Mitigation (SREM). Specifically, by viewing sample matching as classification tasks within the batch, we generate classification logits for the given sample. Instead of a single similarity score, we refine sample filtration through energy uncertainty and estimate model's sensitivity of selected clean samples using swapped classification entropy, in view of the overall prediction distribution. Additionally, we propose cross-modal biased complementary learning to leverage negative matches overlooked in hard-negative training, further improving model optimization stability and curbing self-reinforcing errors. Extensive experiments on challenging benchmarks affirm the efficacy and efficiency of SREM.

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AAAI Conference 2024 Conference Paper

ProAgent: Building Proactive Cooperative Agents with Large Language Models

  • Ceyao Zhang
  • Kaijie Yang
  • Siyi Hu
  • Zihao Wang
  • Guanghe Li
  • Yihang Sun
  • Cheng Zhang
  • Zhaowei Zhang

Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in the realm of multi-agent systems. Current approaches to developing cooperative agents rely primarily on learning-based methods, whose policy generalization depends heavily on the diversity of teammates they interact with during the training phase. Such reliance, however, constrains the agents' capacity for strategic adaptation when cooperating with unfamiliar teammates, which becomes a significant challenge in zero-shot coordination scenarios. To address this challenge, we propose ProAgent, a novel framework that harnesses large language models (LLMs) to create proactive agents capable of dynamically adapting their behavior to enhance cooperation with teammates. ProAgent can analyze the present state, and infer the intentions of teammates from observations. It then updates its beliefs in alignment with the teammates' subsequent actual behaviors. Moreover, ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various of coordination scenarios. Experimental evaluations conducted within the Overcooked-AI environment unveil the remarkable performance superiority of ProAgent, outperforming five methods based on self-play and population-based training when cooperating with AI agents. Furthermore, in partnered with human proxy models, its performance exhibits an average improvement exceeding 10% compared to the current state-of-the-art method. For more information about our project, please visit https://pku-proagent.github.io.

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AAAI Conference 2024 Conference Paper

SSMG: Spatial-Semantic Map Guided Diffusion Model for Free-Form Layout-to-Image Generation

  • Chengyou Jia
  • Minnan Luo
  • Zhuohang Dang
  • Guang Dai
  • Xiaojun Chang
  • Mengmeng Wang
  • Jingdong Wang

Despite significant progress in Text-to-Image (T2I) generative models, even lengthy and complex text descriptions still struggle to convey detailed controls. In contrast, Layout-to-Image (L2I) generation, aiming to generate realistic and complex scene images from user-specified layouts, has risen to prominence. However, existing methods transform layout information into tokens or RGB images for conditional control in the generative process, leading to insufficient spatial and semantic controllability of individual instances. To address these limitations, we propose a novel Spatial-Semantic Map Guided (SSMG) diffusion model that adopts the feature map, derived from the layout, as guidance. Owing to rich spatial and semantic information encapsulated in well-designed feature maps, SSMG achieves superior generation quality with sufficient spatial and semantic controllability compared to previous works. Additionally, we propose the Relation-Sensitive Attention (RSA) and Location-Sensitive Attention (LSA) mechanisms. The former aims to model the relationships among multiple objects within scenes while the latter is designed to heighten the model's sensitivity to the spatial information embedded in the guidance. Extensive experiments demonstrate that SSMG achieves highly promising results, setting a new state-of-the-art across a range of metrics encompassing fidelity, diversity, and controllability.

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ICLR Conference 2024 Conference Paper

SWAP-NAS: Sample-Wise Activation Patterns for Ultra-fast NAS

  • Yameng Peng
  • Andy Song
  • Haytham M. Fayek
  • Vic Ciesielski
  • Xiaojun Chang

Training-free metrics (a.k.a. zero-cost proxies) are widely used to avoid resource-intensive neural network training, especially in Neural Architecture Search (NAS). Recent studies show that existing training-free metrics have several limitations, such as limited correlation and poor generalisation across different search spaces and tasks. Hence, we propose Sample-Wise Activation Patterns and its derivative, SWAP-Score, a novel high-performance training-free metric. It measures the expressivity of networks over a batch of input samples. The SWAP-Score is strongly correlated with ground-truth performance across various search spaces and tasks, outperforming 15 existing training-free metrics on NAS-Bench-101/201/301 and TransNAS-Bench-101. The SWAP-Score can be further enhanced by regularisation, which leads to even higher correlations in cell-based search space and enables model size control during the search. For example, Spearman’s rank correlation coefficient between regularised SWAP-Score and CIFAR-100 validation accuracies on NAS-Bench-201 networks is 0.90, significantly higher than 0.80 from the second-best metric, NWOT. When integrated with an evolutionary algorithm for NAS, our SWAP-NAS achieves competitive performance on CIFAR-10 and ImageNet in approximately 6 minutes and 9 minutes of GPU time respectively.

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AAAI Conference 2023 Conference Paper

3D-TOGO: Towards Text-Guided Cross-Category 3D Object Generation

  • Zutao Jiang
  • Guansong Lu
  • Xiaodan Liang
  • Jihua Zhu
  • Wei Zhang
  • Xiaojun Chang
  • Hang Xu

This article has been updated and an error has been fixed in published paper. An Erratum to this article was published on 6 September 2023. Text-guided 3D object generation aims to generate 3D objects described by user-defined captions, which paves a flexible way to visualize what we imagined. Although some works have been devoted to solving this challenging task, these works either utilize some explicit 3D representations (e.g., mesh), which lack texture and require post-processing for rendering photo-realistic views; or require individual time-consuming optimization for every single case. Here, we make the first attempt to achieve generic text-guided cross-category 3D object generation via a new 3D-TOGO model, which integrates a text-to-views generation module and a views-to-3D generation module. The text-to-views generation module is designed to generate different views of the target 3D object given an input caption. prior-guidance, caption-guidance and view contrastive learning are proposed for achieving better view-consistency and caption similarity. Meanwhile, a pixelNeRF model is adopted for the views-to-3D generation module to obtain the implicit 3D neural representation from the previously-generated views. Our 3D-TOGO model generates 3D objects in the form of the neural radiance field with good texture and requires no time-cost optimization for every single caption. Besides, 3D-TOGO can control the category, color and shape of generated 3D objects with the input caption. Extensive experiments on the largest 3D object dataset (i.e., ABO) are conducted to verify that 3D-TOGO can better generate high-quality 3D objects according to the input captions across 98 different categories, in terms of PSNR, SSIM, LPIPS and CLIP-score, compared with text-NeRF and Dreamfields.

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JMLR Journal 2023 Journal Article

MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library

  • Siyi Hu
  • Yifan Zhong
  • Minquan Gao
  • Weixun Wang
  • Hao Dong
  • Xiaodan Liang
  • Zhihui Li
  • Xiaojun Chang

A significant challenge facing researchers in the area of multi-agent reinforcement learning (MARL) pertains to the identification of a library that can offer fast and compatible development for multi-agent tasks and algorithm combinations, while obviating the need to consider compatibility issues. In this paper, we present MARLlib, a library designed to address the aforementioned challenge by leveraging three key mechanisms: 1) a standardized multi-agent environment wrapper, 2) an agent-level algorithm implementation, and 3) a flexible policy mapping strategy. By utilizing these mechanisms, MARLlib can effectively disentangle the intertwined nature of the multi-agent task and the learning process of the algorithm, with the ability to automatically alter the training strategy based on the current task's attributes. The MARLlib library's source code is publicly accessible on GitHub: https://github.com/Replicable-MARL/MARLlib. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2023. ( edit, beta )

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NeurIPS Conference 2023 Conference Paper

Mask Propagation for Efficient Video Semantic Segmentation

  • Yuetian Weng
  • Mingfei Han
  • Haoyu He
  • Mingjie Li
  • Lina Yao
  • Xiaojun Chang
  • Bohan Zhuang

Video Semantic Segmentation (VSS) involves assigning a semantic label to each pixel in a video sequence. Prior work in this field has demonstrated promising results by extending image semantic segmentation models to exploit temporal relationships across video frames; however, these approaches often incur significant computational costs. In this paper, we propose an efficient mask propagation framework for VSS, called MPVSS. Our approach first employs a strong query-based image segmentor on sparse key frames to generate accurate binary masks and class predictions. We then design a flow estimation module utilizing the learned queries to generate a set of segment-aware flow maps, each associated with a mask prediction from the key frame. Finally, the mask-flow pairs are warped to serve as the mask predictions for the non-key frames. By reusing predictions from key frames, we circumvent the need to process a large volume of video frames individually with resource-intensive segmentors, alleviating temporal redundancy and significantly reducing computational costs. Extensive experiments on VSPW and Cityscapes demonstrate that our mask propagation framework achieves SOTA accuracy and efficiency trade-offs. For instance, our best model with Swin-L backbone outperforms the SOTA MRCFA using MiT-B5 by 4. 0% mIoU, requiring only 26% FLOPs on the VSPW dataset. Moreover, our framework reduces up to 4× FLOPs compared to the per-frame Mask2Former baseline with only up to 2% mIoU degradation on the Cityscapes validation set. Code is available at https: //github. com/ziplab/MPVSS.

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ICLR Conference 2023 Conference Paper

ViewCo: Discovering Text-Supervised Segmentation Masks via Multi-View Semantic Consistency

  • Pengzhen Ren
  • Changlin Li
  • Hang Xu 0004
  • Yi Zhu 0004
  • Guangrun Wang
  • Jianzhuang Liu
  • Xiaojun Chang
  • Xiaodan Liang

Recently, great success has been made in learning visual representations from text supervision, facilitating the emergence of text-supervised semantic segmentation. However, existing works focus on pixel grouping and cross-modal semantic alignment, while ignoring the correspondence among multiple augmented views of the same image. To overcome such limitation, we propose multi-View Consistent learning (ViewCo) for text-supervised semantic segmentation. Specifically, we first propose text-to-views consistency modeling to learn correspondence for multiple views of the same input image. Additionally, we propose cross-view segmentation consistency modeling to address the ambiguity issue of text supervision by contrasting the segment features of Siamese visual encoders. The text-to-views consistency benefits dense assignment of the visual features by encouraging different crops to align with the same text, while the cross-view segmentation consistency modeling provides additional self-supervision, overcoming the limitation of ambiguous text supervision for segmentation masks. Trained with large-scale image-text data, our model can directly segment objects of arbitrary categories in a zero-shot manner. Extensive experiments show that ViewCo outperforms state-of-the-art methods on average by up to 2.9%, 1.6%, and 2.4% mIoU on PASCAL VOC2012, PASCAL Context, and COCO, respectively.

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IJCAI Conference 2023 Conference Paper

Vision Language Navigation with Knowledge-driven Environmental Dreamer

  • Fengda Zhu
  • Vincent CS Lee
  • Xiaojun Chang
  • Xiaodan Liang

Vision-language navigation (VLN) requires an agent to perceive visual observation in a house scene and navigate step-by-step following natural language instruction. Due to the high cost of data annotation and data collection, current VLN datasets provide limited instruction-trajectory data samples. Learning vision-language alignment for VLN from limited data is challenging since visual observation and language instruction are both complex and diverse. Previous works only generate augmented data based on original scenes while failing to generate data samples from unseen scenes, which limits the generalization ability of the navigation agent. In this paper, we introduce the Knowledge-driven Environmental Dreamer (KED), a method that leverages the knowledge of the embodied environment and generates unseen scenes for a navigation agent to learn. Generating an unseen environment with texture consistency and structure consistency is challenging. To address this problem, we incorporate three knowledge-driven regularization objectives into the KED and adopt a reweighting mechanism for self-adaptive optimization. Our KED method is able to generate unseen embodied environments without extra annotations. We use KED to successfully generate 270 houses and 500K instruction-trajectory pairs. The navigation agent with the KED method outperforms the state-of-the-art methods on various VLN benchmarks, such as R2R, R4R, and RxR. Both qualitative and quantitative experiments prove that our proposed KED method is able to high-quality augmentation data with texture consistency and structure consistency.

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ICML Conference 2022 Conference Paper

Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL

  • Siyi Hu
  • Chuanlong Xie
  • Xiaodan Liang
  • Xiaojun Chang

Cooperative multi-agent reinforcement learning (MARL) is making rapid progress for solving tasks in a grid world and real-world scenarios, in which agents are given different attributes and goals, resulting in different behavior through the whole multi-agent task. In this study, we quantify the agent’s behavior difference and build its relationship with the policy performance via {\bf Role Diversity}, a metric to measure the characteristics of MARL tasks. We define role diversity from three perspectives: action-based, trajectory-based, and contribution-based to fully measure a multi-agent task. Through theoretical analysis, we find that the error bound in MARL can be decomposed into three parts that have a strong relation to the role diversity. The decomposed factors can significantly impact policy optimization in three popular directions including parameter sharing, communication mechanism, and credit assignment. The main experimental platforms are based on {\bf Multiagent Particle Environment (MPE) }and {\bf The StarCraft Multi-Agent Challenge (SMAC)}. Extensive experiments clearly show that role diversity can serve as a robust measurement for the characteristics of a multi-agent cooperation task and help diagnose whether the policy fits the current multi-agent system for better policy performance.

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NeurIPS Conference 2021 Conference Paper

FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark

  • Mingjie Li
  • Wenjia Cai
  • Rui Liu
  • Yuetian Weng
  • Xiaoyun Zhao
  • Cong Wang
  • Xin Chen
  • Zhong Liu

The automatic generation of long and coherent medical reports given medical images (e. g. Chest X-ray and Fundus Fluorescein Angiography (FFA)) has great potential to support clinical practice. Researchers have explored advanced methods from computer vision and natural language processing to incorporate medical domain knowledge for the generation of readable medical reports. However, existing medical report generation (MRG) benchmarks lack both explainable annotations and reliable evaluation tools, hindering the current research advances from two aspects: firstly, existing methods can only predict reports without accurate explanation, undermining the trustworthiness of the diagnostic methods; secondly, the comparison among the predicted reports from different MRG methods is unreliable using the evaluation metrics of natural-language generation (NLG). To address these issues, in this paper, we propose an explainable and reliable MRG benchmark based on FFA Images and Reports (FFA-IR). Specifically, FFA-IR is large, with 10, 790 reports along with 1, 048, 584 FFA images from clinical practice; it includes explainable annotations, based on a schema of 46 categories of lesions; and it is bilingual, providing both English and Chinese reports for each case. Besides using the widely used NLG metrics, we propose a set of nine human evaluation criteria to evaluate the generated reports. We envision FFA-IR as a testbed for explainable and reliable medical report generation. We also hope that it can broadly accelerate medical imaging research and facilitate interaction between the fields of medical imaging, computer vision, and natural language processing.

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ICML Conference 2021 Conference Paper

iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients

  • Miao Zhang 0022
  • Steven W. Su
  • Shirui Pan
  • Xiaojun Chang
  • Ehsan Abbasnejad
  • Gholamreza Haffari

Differentiable ARchiTecture Search(DARTS) has recently become the mainstream in the neural architecture search (NAS) due to its efficiency and simplicity. With a gradient-based bi-level optimization, DARTS alternately optimizes the inner model weights and the outer architecture parameter in a weight-sharing supernet. A key challenge to the scalability and quality of the learned architectures is the need for differentiating through the inner-loop optimisation. While much has been discussed about several potentially fatal factors in DARTS, the architecture gradient, a. k. a. hypergradient, has received less attention. In this paper, we tackle the hypergradient computation in DARTS based on the implicit function theorem, making it only depends on the obtained solution to the inner-loop optimization and agnostic to the optimization path. To further reduce the computational requirements, we formulate a stochastic hypergradient approximation for differentiable NAS, and theoretically show that the architecture optimization with the proposed method is expected to converge to a stationary point. Comprehensive experiments on two NAS benchmark search spaces and the common NAS search space verify the effectiveness of our proposed method. It leads to architectures outperforming, with large margins, those learned by the baseline methods.

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IJCAI Conference 2021 Conference Paper

Person Search Challenges and Solutions: A Survey

  • Xiangtan Lin
  • Pengzhen Ren
  • Yun Xiao
  • Xiaojun Chang
  • Alex Hauptmann

Person search has drawn increasing attention due to its real-world applications and research significance. Person search aims to find a probe person in a gallery of scene images with a wide range of applications, such as criminals search, multicamera tracking, missing person search, etc. Early person search works focused on image-based person search, which uses person image as the search query. Text-based person search is another major person search category that uses free-form natural language as the search query. Person search is challenging, and corresponding solutions are diverse and complex. Therefore, systematic surveys on this topic are essential. This paper surveyed the recent works on image-based and text-based person search from the perspective of challenges and solutions. Specifically, we provide a brief analysis of highly influential person search methods considering the three significant challenges: the discriminative person features, the query-person gap, and the detection-identification inconsistency. We summarise and compare evaluation results. Finally, we discuss open issues and some promising future research directions.

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ICLR Conference 2021 Conference Paper

UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers

  • Siyi Hu
  • Fengda Zhu
  • Xiaojun Chang
  • Xiaodan Liang

Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task. The limitation is due to the restricted model architecture related to fixed input and output dimensions. This hinders the experience accumulation and transfer of the learned agent over tasks with diverse levels of difficulty (e.g. 3 vs 3 or 5 vs 6 multi-agent games). In this paper, we make the first attempt to explore a universal multi-agent reinforcement learning pipeline, designing one single architecture to fit tasks with the requirement of different observation and action configurations. Unlike previous RNN-based models, we utilize a transformer-based model to generate a flexible policy by decoupling the policy distribution from the intertwined input observation with an importance weight measured by the merits of the self-attention mechanism. Compared to a standard transformer block, the proposed model, named as Universal Policy Decoupling Transformer (UPDeT), further relaxes the action restriction and makes the multi-agent task's decision process more explainable. UPDeT is general enough to be plugged into any multi-agent reinforcement learning pipeline and equip them with strong generalization abilities that enables the handling of multiple tasks at a time. Extensive experiments on large-scale SMAC multi-agent competitive games demonstrate that the proposed UPDeT-based multi-agent reinforcement learning achieves significant results relative to state-of-the-art approaches, demonstrating advantageous transfer capability in terms of both performance and training speed (10 times faster).

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NeurIPS Conference 2020 Conference Paper

Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement

  • Miao Zhang
  • Huiqi Li
  • Shirui Pan
  • Xiaojun Chang
  • Zongyuan Ge
  • Steven Su

Recent works on One-Shot Neural Architecture Search (NAS) mostly adopt a bilevel optimization scheme to alternatively optimize the supernet weights and architecture parameters after relaxing the discrete search space into a differentiable space. However, the non-negligible incongruence in their relaxation methods is hard to guarantee the differentiable optimization in the continuous space is equivalent to the optimization in the discrete space. Differently, this paper utilizes a variational graph autoencoder to injectively transform the discrete architecture space into an equivalently continuous latent space, to resolve the incongruence. A probabilistic exploration enhancement method is accordingly devised to encourage intelligent exploration during the architecture search in the latent space, to avoid local optimal in architecture search. As the catastrophic forgetting in differentiable One-Shot NAS deteriorates supernet predictive ability and makes the bilevel optimization inefficient, this paper further proposes an architecture complementation method to relieve this deficiency. We analyze the effectiveness of the proposed method, and a series of experiments have been conducted to compare the proposed method with state-of-the-art One-Shot NAS methods.

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NeurIPS Conference 2020 Conference Paper

Hierarchical Neural Architecture Search for Deep Stereo Matching

  • Xuelian Cheng
  • Yiran Zhong
  • Mehrtash Harandi
  • Yuchao Dai
  • Xiaojun Chang
  • Hongdong Li
  • Tom Drummond
  • Zongyuan Ge

To reduce the human efforts in neural network design, Neural Architecture Search (NAS) has been applied with remarkable success to various high-level vision tasks such as classification and semantic segmentation. The underlying idea for the NAS algorithm is straightforward, namely, to allow the network the ability to choose among a set of operations (\eg convolution with different filter sizes), one is able to find an optimal architecture that is better adapted to the problem at hand. However, so far the success of NAS has not been enjoyed by low-level geometric vision tasks such as stereo matching. This is partly due to the fact that state-of-the-art deep stereo matching networks, designed by humans, are already sheer in size. Directly applying the NAS to such massive structures is computationally prohibitive based on the currently available mainstream computing resources. In this paper, we propose the first \emph{end-to-end} hierarchical NAS framework for deep stereo matching by incorporating task-specific human knowledge into the neural architecture search framework. Specifically, following the gold standard pipeline for deep stereo matching (\ie, feature extraction -- feature volume construction and dense matching), we optimize the architectures of the entire pipeline jointly. Extensive experiments show that our searched network outperforms all state-of-the-art deep stereo matching architectures and is ranked at the top 1 accuracy on KITTI stereo 2012, 2015, and Middlebury benchmarks, as well as the top 1 on SceneFlow dataset with a substantial improvement on the size of the network and the speed of inference. Code available at https: //github. com/XuelianCheng/LEAStereo.

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TIST Journal 2020 Journal Article

Pair-based Uncertainty and Diversity Promoting Early Active Learning for Person Re-identification

  • Wenhe Liu
  • Xiaojun Chang
  • Ling Chen
  • Dinh Phung
  • Xiaoqin Zhang
  • Yi Yang
  • Alexander G. Hauptmann

The effective training of supervised Person Re-identification (Re-ID) models requires sufficient pairwise labeled data. However, when there is limited annotation resource, it is difficult to collect pairwise labeled data. We consider a challenging and practical problem called Early Active Learning, which is applied to the early stage of experiments when there is no pre-labeled sample available as references for human annotating. Previous early active learning methods suffer from two limitations for Re-ID. First, these instance-based algorithms select instances rather than pairs, which can result in missing optimal pairs for Re-ID. Second, most of these methods only consider the representativeness of instances, which can result in selecting less diverse and less informative pairs. To overcome these limitations, we propose a novel pair-based active learning for Re-ID. Our algorithm selects pairs instead of instances from the entire dataset for annotation. Besides representativeness, we further take into account the uncertainty and the diversity in terms of pairwise relations. Therefore, our algorithm can produce the most representative, informative, and diverse pairs for Re-ID data annotation. Extensive experimental results on five benchmark Re-ID datasets have demonstrated the superiority of the proposed pair-based early active learning algorithm.

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IJCAI Conference 2020 Conference Paper

Quadratic Sparse Gaussian Graphical Model Estimation Method for Massive Variables

  • Jiaqi Zhang
  • Meng Wang
  • Qinchi Li
  • Sen Wang
  • Xiaojun Chang
  • Beilun Wang

We consider the problem of estimating a sparse Gaussian Graphical Model with a special graph topological structure and more than a million variables. Most previous scalable estimators still contain expensive calculation steps (e. g. , matrix inversion or Hessian matrix calculation) and become infeasible in high-dimensional scenarios, where p (number of variables) is larger than n (number of samples). To overcome this challenge, we propose a novel method, called Fast and Scalable Inverse Covariance Estimator by Thresholding (FST). FST first obtains a graph structure by applying a generalized threshold to the sample covariance matrix. Then, it solves multiple block-wise subproblems via element-wise thresholding. By using matrix thresholding instead of matrix inversion as the computational bottleneck, FST reduces its computational complexity to a much lower order of magnitude (O(p2)). We show that FST obtains the same sharp convergence rate O(√(log max{p, n}/n) as other state-of-the-art methods. We validate the method empirically, on multiple simulated datasets and one real-world dataset, and show that FST is two times faster than the four baselines while achieving a lower error rate under both Frobenius-norm and max-norm.

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TIST Journal 2020 Journal Article

Self-weighted Robust LDA for Multiclass Classification with Edge Classes

  • Caixia Yan
  • Xiaojun Chang
  • Minnan Luo
  • Qinghua Zheng
  • Xiaoqin Zhang
  • Zhihui Li
  • Feiping Nie

Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative features for multi-class classification. A vast majority of existing LDA algorithms are prone to be dominated by the class with very large deviation from the others, i.e., edge class, which occurs frequently in multi-class classification. First, the existence of edge classes often makes the total mean biased in the calculation of between-class scatter matrix. Second, the exploitation of ℓ 2 -norm based between-class distance criterion magnifies the extremely large distance corresponding to edge class. In this regard, a novel self-weighted robust LDA with ℓ 2,1 -norm based pairwise between-class distance criterion, called SWRLDA, is proposed for multi-class classification especially with edge classes. SWRLDA can automatically avoid the optimal mean calculation and simultaneously learn adaptive weights for each class pair without setting any additional parameter. An efficient re-weighted algorithm is exploited to derive the global optimum of the challenging ℓ 2,1 -norm maximization problem. The proposed SWRLDA is easy to implement and converges fast in practice. Extensive experiments demonstrate that SWRLDA performs favorably against other compared methods on both synthetic and real-world datasets while presenting superior computational efficiency in comparison with other techniques.

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AAAI Conference 2019 Conference Paper

Distributionally Robust Semi-Supervised Learning for People-Centric Sensing

  • Kaixuan Chen
  • Lina Yao
  • Dalin Zhang
  • Xiaojun Chang
  • Guodong Long
  • Sen Wang

Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing. However, humangenerated data inherently suffer from distribution shift in semi-supervised learning due to the diverse biological conditions and behavior patterns of humans. To address this problem, we propose a generic distributionally robust model for semi-supervised learning on distributionally shifted data. Considering both the discrepancy and the consistency between the labeled data and the unlabeled data, we learn the latent features that reduce person-specific discrepancy and preserve task-specific consistency. We evaluate our model in a variety of people-centric recognition tasks on real-world datasets, including intention recognition, activity recognition, muscular movement recognition and gesture recognition. The experiment results demonstrate that the proposed model outperforms the state-of-the-art methods.

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AAAI Conference 2018 Conference Paper

Balanced Clustering via Exclusive Lasso: A Pragmatic Approach

  • Zhihui Li
  • Feiping Nie
  • Xiaojun Chang
  • Zhigang Ma
  • Yi Yang

Clustering is an effective technique in data mining to generate groups that are the matter of interest. Among various clustering approaches, the family of k-means algorithms and min-cut algorithms gain most popularity due to their simplicity and efficacy. The classical k-means algorithm partitions a number of data points into several subsets by iteratively updating the clustering centers and the associated data points. By contrast, a weighted undirected graph is constructed in min-cut algorithms which partition the vertices of the graph into two sets. However, existing clustering algorithms tend to cluster minority of data points into a subset, which shall be avoided when the target dataset is balanced. To achieve more accurate clustering for balanced dataset, we propose to leverage exclusive lasso on k-means and min-cut to regulate the balance degree of the clustering results. By optimizing our objective functions that build atop the exclusive lasso, we can make the clustering result as much balanced as possible. Extensive experiments on several large-scale datasets validate the advantage of the proposed algorithms compared to the state-of-the-art clustering algorithms.

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TIST Journal 2018 Journal Article

Few-Shot Text and Image Classification via Analogical Transfer Learning

  • Wenhe Liu
  • Xiaojun Chang
  • Yan Yan
  • Yi Yang
  • Alexander G. Hauptmann

Learning from very few samples is a challenge for machine learning tasks, such as text and image classification. Performance of such task can be enhanced via transfer of helpful knowledge from related domains, which is referred to as transfer learning. In previous transfer learning works, instance transfer learning algorithms mostly focus on selecting the source domain instances similar to the target domain instances for transfer. However, the selected instances usually do not directly contribute to the learning performance in the target domain. Hypothesis transfer learning algorithms focus on the model/parameter level transfer. They treat the source hypotheses as well-trained and transfer their knowledge in terms of parameters to learn the target hypothesis. Such algorithms directly optimize the target hypothesis by the observable performance improvements. However, they fail to consider the problem that instances that contribute to the source hypotheses may be harmful for the target hypothesis, as instance transfer learning analyzed. To relieve the aforementioned problems, we propose a novel transfer learning algorithm, which follows an analogical strategy. Particularly, the proposed algorithm first learns a revised source hypothesis with only instances contributing to the target hypothesis. Then, the proposed algorithm transfers both the revised source hypothesis and the target hypothesis (only trained with a few samples) to learn an analogical hypothesis. We denote our algorithm as Analogical Transfer Learning. Extensive experiments on one synthetic dataset and three real-world benchmark datasets demonstrate the superior performance of the proposed algorithm.

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AAAI Conference 2018 Conference Paper

Semi-Supervised Bayesian Attribute Learning for Person Re-Identification

  • Wenhe Liu
  • Xiaojun Chang
  • Ling Chen
  • Yi Yang

Person re-identification (re-ID) tasks aim to identify the same person in multiple images captured from non-overlapping camera views. Most previous re-ID studies have attempted to solve this problem through either representation learning or metric learning, or by combining both techniques. Representation learning relies on the latent factors or attributes of the data. In most of these works, the dimensionality of the factors/attributes has to be manually determined for each new dataset. Thus, this approach is not robust. Metric learning optimizes a metric across the dataset to measure similarity according to distance. However, choosing the optimal method for computing these distances is data dependent, and learning the appropriate metric relies on a sufficient number of pair-wise labels. To overcome these limitations, we propose a novel algorithm for person re-ID, called semi-supervised Bayesian attribute learning. We introduce an Indian Buffet Process to identify the priors of the latent attributes. The dimensionality of attributes factors is then automatically determined by nonparametric Bayesian learning. Meanwhile, unlike traditional distance metric learning, we propose a reidentification probability distribution to describe how likely it is that a pair of images contains the same person. This technique relies solely on the latent attributes of both images. Moreover, pair-wise labels that are not known can be estimated from pair-wise labels that are known, making this a robust approach for semi-supervised learning. Extensive experiments demonstrate the superior performance of our algorithm over several state-of-the-art algorithms on small-scale datasets and comparable performance on large-scale re-ID datasets.

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IJCAI Conference 2018 Conference Paper

Teaching Semi-Supervised Classifier via Generalized Distillation

  • Chen Gong
  • Xiaojun Chang
  • Meng Fang
  • Jian Yang

Semi-Supervised Learning (SSL) is able to build reliable classifier with very scarce labeled examples by properly utilizing the abundant unlabeled examples. However, existing SSL algorithms often yield unsatisfactory performance due to the lack of supervision information. To address this issue, this paper formulates SSL as a Generalized Distillation (GD) problem, which treats existing SSL algorithm as a learner and introduces a teacher to guide the learner? s training process. Specifically, the intelligent teacher holds the privileged knowledge that? explains? the training data but remains unknown to the learner, and the teacher should convey its rich knowledge to the imperfect learner through a specific teaching function. After that, the learner gains knowledge by? imitating? the output of the teaching function under an optimization framework. Therefore, the learner in our algorithm learns from both the teacher and the training data, so its output can be substantially distilled and enhanced. By deriving the Rademacher complexity and error bounds of the proposed algorithm, the usefulness of the introduced teacher is theoretically demonstrated. The superiority of our algorithm to the related state-of-the-art methods has also been empirically demonstrated by the experiments on different datasets with various sources of privileged knowledge.

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IJCAI Conference 2018 Conference Paper

Uncertainty Sampling for Action Recognition via Maximizing Expected Average Precision

  • Hanmo Wang
  • Xiaojun Chang
  • Lei Shi
  • Yi Yang
  • Yi-Dong Shen

Recognizing human actions in video clips has been an important topic in computer vision. Sufficient labeled data is one of the prerequisites for the good performance of action recognition algorithms. However, while abundant videos can be collected from the Internet, categorizing each video clip is tedious and even time-consuming. Active learning is one way to alleviate the labeling labor by allowing the classifier to choose the most informative unlabeled instances for manual annotation. Among various active learning algorithms, uncertainty sampling is arguably the most widely-used strategy. Conventional uncertainty sampling strategies such as entropy-based methods are usually tested under accuracy. However, in action recognition Average Precision (AP) is an acknowledged evaluation metric, which is somehow ignored in the active learning community. It is defined as the area under the precision-recall curve. In this paper, we propose a novel uncertainty sampling algorithm for action recognition using expected AP. We conduct experiments on three real-world action recognition datasets and show that our algorithm outperforms other uncertainty-based active learning algorithms.

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IJCAI Conference 2017 Conference Paper

Adaptive Semi-Supervised Learning with Discriminative Least Squares Regression

  • Minnan Luo
  • Lingling Zhang
  • Feiping Nie
  • Xiaojun Chang
  • Buyue Qian
  • Qinghua Zheng

Semi-supervised learning plays a significant role in multi-class classification, where a small number of labeled data are more deterministic while substantial unlabeled data might cause large uncertainties and potential threats. In this paper, we distinguish the label fitting of labeled and unlabeled training data through a probabilistic vector with an adaptive parameter, which always ensures the significant importance of labeled data and characterizes the contribution of unlabeled instance according to its uncertainty. Instead of using traditional least squares regression (LSR) for classification, we develop a new discriminative LSR by equipping each label with an adjustment vector. This strategy avoids incorrect penalization on samples that are far away from the boundary and simultaneously facilitates multi-class classification by enlarging the geometrical distance of instances belonging to different classes. An efficient alternative algorithm is exploited to solve the proposed model with closed form solution for each updating rule. We also analyze the convergence and complexity of the proposed algorithm theoretically. Experimental results on several benchmark datasets demonstrate the effectiveness and superiority of the proposed model for multi-class classification tasks.

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IJCAI Conference 2017 Conference Paper

Discriminative Dictionary Learning With Ranking Metric Embedded for Person Re-Identification

  • De Cheng
  • Xiaojun Chang
  • Li Liu
  • Alexander G. Hauptmann
  • Yihong Gong
  • Nanning Zheng

The goal of person re-identification (Re-Id) is to match pedestrians captured from multiple non-overlapping cameras. In this paper, we propose a novel dictionary learning based method with the ranking metric embedded, for person Re-Id. A new and essential ranking graph Laplacian term is introduced, which minimizes the intra-personal compactness and maximizes the inter-personal dispersion in the objective. Different from the traditional dictionary learning based approaches and their extensions, which just use the same or not information, our proposed method can explore the ranking relationship among the person images, which is essential for such retrieval related tasks. Simultaneously, one distance measurement has been explicitly learned in the model to further improve the performance. Since we have reformulated these ranking constraints into the graph Laplacian form, the proposed method is easy-to-implement but effective. We conduct extensive experiments on three widely used person Re-Id benchmark datasets, and achieve state-of-the-art performances.

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IJCAI Conference 2017 Conference Paper

How Unlabeled Web Videos Help Complex Event Detection?

  • Huan Liu
  • Qinghua Zheng
  • Minnan Luo
  • Dingwen Zhang
  • Xiaojun Chang
  • Cheng Deng

The lack of labeled exemplars is an important factor that makes the task of multimedia event detection (MED) complicated and challenging. Utilizing artificially picked and labeled external sources is an effective way to enhance the performance of MED. However, building these data usually requires professional human annotators, and the procedure is too time-consuming and costly to scale. In this paper, we propose a new robust dictionary learning framework for complex event detection, which is able to handle both labeled and easy-to-get unlabeled web videos by sharing the same dictionary. By employing the lq-norm based loss jointly with the structured sparsity based regularization, our model shows strong robustness against the substantial noisy and outlier videos from open source. We exploit an effective optimization algorithm to solve the proposed highly non-smooth and non-convex problem. Extensive experiment results over standard datasets of TRECVID MEDTest 2013 and TRECVID MEDTest 2014 demonstrate the effectiveness and superiority of the proposed framework on complex event detection.

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AAAI Conference 2017 Conference Paper

Multi-View Correlated Feature Learning by Uncovering Shared Component

  • Xiaowei Xue
  • Feiping Nie
  • Sen Wang
  • Xiaojun Chang
  • Bela Stantic
  • Min Yao

Learning multiple heterogeneous features from different data sources is challenging. One research topic is how to exploit and utilize the correlations among various features across multiple views with the aim of improving the performance of learning tasks, such as classification. In this paper, we propose a new multi-view feature learning algorithm that simultaneously analyzes features from different views. Compared to most of the existing subspace learning methods that only focus on exploiting a shared latent subspace, our algorithm not only learns individual information in each view but also captures feature correlations among multiple views by learning a shared component. By assuming that such a component is shared by all views, we simultaneously exploit the shared component and individual information of each view in a batch mode. Since the objective function is non-smooth and difficult to solve, we propose an efficient iterative algorithm for optimization with guaranteed convergence. Extensive experiments are conducted on several benchmark datasets. The results demonstrate that our proposed algorithm performs better than all the compared multi-view learning algorithms.

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AAAI Conference 2017 Conference Paper

Probabilistic Non-Negative Matrix Factorization and Its Robust Extensions for Topic Modeling

  • Minnan Luo
  • Feiping Nie
  • Xiaojun Chang
  • Yi Yang
  • Alexander Hauptmann
  • Qinghua Zheng

Traditional topic model with maximum likelihood estimate inevitably suffers from the conditional independence of words given the documents topic distribution. In this paper, we follow the generative procedure of topic model and learn the topic-word distribution and topics distribution via directly approximating the word-document co-occurrence matrix with matrix decomposition technique. These methods include: (1) Approximating the normalized document-word conditional distribution with the documents probability matrix and words probability matrix based on probabilistic nonnegative matrix factorization (NMF); (2) Since the standard NMF is well known to be non-robust to noises and outliers, we extended the probabilistic NMF of the topic model to its robust versions using 2, 1-norm and capped 2, 1-norm based loss functions, respectively. The proposed framework inherits the explicit probabilistic meaning of factors in topic models and simultaneously makes the conditional independence assumption on words unnecessary. Straightforward and efficient algorithms are exploited to solve the corresponding nonsmooth and non-convex problems. Experimental results over several benchmark datasets illustrate the effectiveness and superiority of the proposed methods.

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IJCAI Conference 2017 Conference Paper

Self-paced Mixture of Regressions

  • Longfei Han
  • Dingwen Zhang
  • Dong Huang
  • Xiaojun Chang
  • Jun Ren
  • Senlin Luo
  • Junwei Han

Mixture of regressions (MoR) is the well-established and effective approach to model discontinuous and heterogeneous data in regression problems. Existing MoR approaches assume smooth joint distribution for its good anlaytic properties. However, such assumption makes existing MoR very sensitive to intra-component outliers (the noisy training data residing in certain components) and the inter-component imbalance (the different amounts of training data in different components). In this paper, we make the earliest effort on Self-paced Learning (SPL) in MoR, i. e. , Self-paced mixture of regressions (SPMoR) model. We propose a novel self-paced regularizer based on the Exclusive LASSO, which improves inter-component balance of training data. As a robust learning regime, SPL pursues confidence sample reasoning. To demonstrate the effectiveness of SPMoR, we conducted experiments on both the sythetic examples and real-world applications to age estimation and glucose estimation. The results show that SPMoR outperforms the state-of-the-arts methods.

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AAAI Conference 2017 Conference Paper

Structure Regularized Unsupervised Discriminant Feature Analysis

  • Mingyu Fan
  • Xiaojun Chang
  • Dacheng Tao

Feature selection is an important technique in machine learning research. An effective and robust feature selection method is desired to simultaneously identify the informative features and eliminate the noisy ones of data. In this paper, we consider the unsupervised feature selection problem which is particularly difficult as there is not any class labels that would guide the search for relevant features. To solve this, we propose a novel algorithmic framework which performs unsupervised feature selection. Firstly, the proposed framework implements structure learning, where the data structures (including intrinsic distribution structure and the data segment) are found via a combination of the alternative optimization and clustering. Then, both the intrinsic data structure and data segmentation are formulated as regularization terms for discriminant feature selection. The results of the feature selection also affect the structure learning step in the following iterations. By leveraging the interactions between structure learning and feature selection, we are able to capture more accurate structure of data and select more informative features. Clustering and classification experiments on real world image data sets demonstrate the effectiveness of our method.

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IJCAI Conference 2017 Conference Paper

Top-k Supervise Feature Selection via ADMM for Integer Programming

  • Mingyu Fan
  • Xiaojun Chang
  • Xiaoqin Zhang
  • Di Wang
  • Liang Du

Recently, structured sparsity inducing based feature selection has become a hot topic in machine learning and pattern recognition. Most of the sparsity inducing feature selection methods are designed to rank all features by certain criterion and then select the k top ranked features, where k is an integer. However, the k top features are usually not the top k features and therefore maybe a suboptimal result. In this paper, we propose a novel supervised feature selection method to directly identify the top k features. The new method is formulated as a classic regularized least squares regression model with two groups of variables. The problem with respect to one group of the variables turn out to be a 0-1 integer programming, which had been considered very hard to solve. To address this, we utilize an efficient optimization method to solve the integer programming, which first replaces the discrete 0-1 constraints with two continuous constraints and then utilizes the alternating direction method of multipliers to optimize the equivalent problem. The obtained result is the top subset with k features under the proposed criterion rather than the subset of k top features. Experiments have been conducted on benchmark data sets to show the effectiveness of proposed method.

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AAAI Conference 2016 Conference Paper

Dynamic Concept Composition for Zero-Example Event Detection

  • Xiaojun Chang
  • Yi Yang
  • Guodong Long
  • Chengqi Zhang
  • Alexander Hauptmann

In this paper, we focus on automatically detecting events in unconstrained videos without the use of any visual training exemplars. In principle, zero-shot learning makes it possible to train an event detection model based on the assumption that events (e. g. birthday party) can be described by multiple mid-level semantic concepts (e. g. “blowing candle”, “birthday cake”). Towards this goal, we first pre-train a bundle of concept classifiers using data from other sources. Then we evaluate the semantic correlation of each concept w. r. t. the event of interest and pick up the relevant concept classifiers, which are applied on all test videos to get multiple prediction score vectors. While most existing systems combine the predictions of the concept classi- fiers with fixed weights, we propose to learn the optimal weights of the concept classifiers for each testing video by exploring a set of online available videos with freeform text descriptions of their content. To validate the effectiveness of the proposed approach, we have conducted extensive experiments on the latest TRECVID MEDTest 2014, MEDTest 2013 and CCV dataset. The experimental results confirm the superiority of the proposed approach.

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AAAI Conference 2015 Conference Paper

A Convex Formulation for Spectral Shrunk Clustering

  • Xiaojun Chang
  • Feiping Nie
  • Zhigang Ma
  • Yi Yang
  • Xiaofang Zhou

Spectral clustering is a fundamental technique in the field of data mining and information processing. Most existing spectral clustering algorithms integrate dimensionality reduction into the clustering process assisted by manifold learning in the original space. However, the manifold in reduced-dimensional subspace is likely to exhibit altered properties in contrast with the original space. Thus, applying manifold information obtained from the original space to the clustering process in a low-dimensional subspace is prone to inferior performance. Aiming to address this issue, we propose a novel convex algorithm that mines the manifold structure in the low-dimensional subspace. In addition, our unified learning process makes the manifold learning particularly tailored for the clustering. Compared with other related methods, the proposed algorithm results in more structured clustering result. To validate the efficacy of the proposed algorithm, we perform extensive experiments on several benchmark datasets in comparison with some state-of-the-art clustering approaches. The experimental results demonstrate that the proposed algorithm has quite promising clustering performance.

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ICML Conference 2015 Conference Paper

Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM

  • Xiaojun Chang
  • Yi Yang 0001
  • Eric P. Xing
  • Yaoliang Yu

We aim to detect complex events in long Internet videos that may last for hours. A major challenge in this setting is that only a few shots in a long video are relevant to the event of interest while others are irrelevant or even misleading. Instead of indifferently pooling the shots, we first define a novel notion of semantic saliency that assesses the relevance of each shot with the event of interest. We then prioritize the shots according to their saliency scores since shots that are semantically more salient are expected to contribute more to the final event detector. Next, we propose a new isotonic regularizer that is able to exploit the semantic ordering information. The resulting nearly-isotonic SVM classifier exhibits higher discriminative power. Computationally, we develop an efficient implementation using the proximal gradient algorithm, and we prove new, closed-form proximal steps. We conduct extensive experiments on three real-world video datasets and confirm the effectiveness of the proposed approach.

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IJCAI Conference 2015 Conference Paper

Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection

  • Xiaojun Chang
  • Yi Yang
  • Alexander Hauptmann
  • Eric P Xing
  • Yao-Liang Yu

We focus on detecting complex events in unconstrained Internet videos. While most existing works rely on the abundance of labeled training data, we consider a more difficult zero-shot setting where no training data is supplied. We first pre-train a number of concept classifiers using data from other sources. Then we evaluate the semantic correlation of each concept w. r. t. the event of interest. After further refinement to take prediction inaccuracy and discriminative power into account, we apply the discovered concept classifiers on all test videos and obtain multiple score vectors. These distinct score vectors are converted into pairwise comparison matrices and the nuclear norm rank aggregation framework is adopted to seek consensus. To address the challenging optimization formulation, we propose an efficient, highly scalable algorithm that is an order of magnitude faster than existing alternatives. Experiments on recent TRECVID datasets verify the superiority of the proposed approach.

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AAAI Conference 2014 Conference Paper

A Convex Formulation for Semi-Supervised Multi-Label Feature Selection

  • Xiaojun Chang
  • Feiping Nie
  • Yi Yang
  • Heng Huang

Explosive growth of multimedia data has brought challenge of how to efficiently browse, retrieve and organize these data. Under this circumstance, different approaches have been proposed to facilitate multimedia analysis. Several semi-supervised feature selection algorithms have been proposed to exploit both labeled and unlabeled data. However, they are implemented based on graphs, such that they cannot handle large-scale datasets. How to conduct semi-supervised feature selection on large-scale datasets has become a challenging research problem. Moreover, existing multi-label feature selection algorithms rely on eigen-decomposition with heavy computational burden, which further prevent current feature selection algorithms from being applied for big data. In this paper, we propose a novel convex semi-supervised multi-label feature selection algorithm, which can be applied to large-scale datasets. We evaluate performance of the proposed algorithm over five benchmark datasets and compare the results with stateof-the-art supervised and semi-supervised feature selection algorithms as well as baseline using all features. The experimental results demonstrate that our proposed algorithm consistently achieve superiors performances.

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