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Tieniu Tan

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49

AAAI Conference 2026 Conference Paper

Artificial Immune System of Secure Face Recognition Against Adversarial Attacks (Abstract Reprint)

  • Min Ren
  • Yunlong Wang
  • Yuhao Zhu
  • Yongzhen Huang
  • Zhenan Sun
  • Qi Li
  • Tieniu Tan

Deep learning-based face recognition models are vulnerable to adversarial attacks. In contrast to general noises, the presence of imperceptible adversarial noises can lead to catastrophic errors in deep face recognition models. The primary difference between adversarial noise and general noise lies in its specificity. Adversarial attack methods give rise to noises tailored to the characteristics of the individual image and recognition model at hand. Diverse samples and recognition models can engender specific adversarial noise patterns, which pose significant challenges for adversarial defense. Addressing this challenge in the realm of face recognition presents a more formidable endeavor due to the inherent nature of face recognition as an open set task. In order to tackle this challenge, it is imperative to employ customized processing for each individual input sample. Drawing inspiration from the biological immune system, which can identify and respond to various threats, this paper aims to create an artificial immune system to provide adversarial defense for face recognition. The proposed defense model incorporates the principles of antibody cloning, mutation, selection, and memory mechanisms to generate a distinct antibody for each input sample, wherein the term antibody refers to a specialized noise removal manner. Furthermore, we introduce a self-supervised adversarial training mechanism that serves as a simulated rehearsal of immune system invasions. Extensive experimental results demonstrate the efficacy of the proposed method, surpassing state-of-the-art adversarial defense methods.

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AAAI Conference 2026 Conference Paper

Delphi: A Neuro-Symbolic Framework for Individualized, Safe and Interpretable Treatment Recommendation

  • Muchan Tao
  • Haonan Qin
  • Yuqi Fang
  • Caifeng Shan
  • Tieniu Tan

Clinical reinforcement learning (RL) holds promise for treatment recommendation but remains hindered by black-box decision processes, limited safety guarantees, and lack of individualized reasoning. We introduce Delphi Engine, the first fully trainable neuro-symbolic causal RL framework for dynamic treatment planning, designed to answer three core clinical questions in real time: Why this action? Why is it safe? Why for this patient? Specifically, Delphi integrates: (1) causality-aware state modeling using discretized physiological variables and subtype-specific causal graphs; (2) adaptive symbolic rule constraints, combining clinical guidelines and behavior-derived rules into soft differentiable logic; and (3) interpretable decision fusion, where actions are selected based on joint neural-symbolic Q-values and explained via structured LLM-based justifications. We evaluate Delphi on the MIMIC-III sepsis cohort using both standard off-policy evaluations (WIS↑1.47, DR↑1.29, RMSE↓0.207) and the first blinded physician evaluation of an explainable RL system in healthcare. Delphi consistently outperforms historical physicians' treatments in safety (+10.4%), understandability (+8.9%), and adoption rate (+5.75%) across six clinical axes. These results highlight Delphi’s potential as a safe, interpretable, and patient-specific AI assistant for critical care medicine.

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ICLR Conference 2025 Conference Paper

Breaking Mental Set to Improve Reasoning through Diverse Multi-Agent Debate

  • Yexiang Liu
  • Jie Cao 0002
  • Zekun Li 0001
  • Ran He 0001
  • Tieniu Tan

Large Language Models (LLMs) have seen significant progress but continue to struggle with persistent reasoning mistakes. Previous methods of *self-reflection* have been proven limited due to the models’ inherent fixed thinking patterns. While Multi-Agent Debate (MAD) attempts to mitigate this by incorporating multiple agents, it often employs the same reasoning methods, even though assigning different personas to models. This leads to a "fixed mental set", where models rely on homogeneous thought processes without exploring alternative perspectives. In this paper, we introduce Diverse Multi-Agent Debate (DMAD), a method that encourages agents to think with distinct reasoning approaches. By leveraging diverse problem-solving strategies, each agent can gain insights from different perspectives, refining its responses through discussion and collectively arriving at the optimal solution. DMAD effectively breaks the limitations of fixed mental sets. We evaluate DMAD against various prompting techniques, including *self-reflection* and traditional MAD, across multiple benchmarks using both LLMs and Multimodal LLMs. Our experiments show that DMAD consistently outperforms other methods, delivering better results than MAD in fewer rounds. Code is available at https://github.com/MraDonkey/DMAD.

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NeurIPS Conference 2025 Conference Paper

BridgeVLA: Input-Output Alignment for Efficient 3D Manipulation Learning with Vision-Language Models

  • Peiyan Li
  • Yixiang Chen
  • Hongtao Wu
  • Xiao Ma
  • Xiangnan Wu
  • Yan Huang
  • Liang Wang
  • Tao Kong

Recently, leveraging pre-trained vision-language models (VLMs) for building vision-language-action (VLA) models has emerged as a promising approach to effective robot manipulation learning. However, only few methods incorporate 3D signals into VLMs for action prediction, and they do not fully leverage the spatial structure inherent in 3D data, leading to low data efficiency. In this paper, we introduce a new paradigm for constructing 3D VLAs. Specifically, we first pre-train the VLM backbone to take 2D images as input and produce 2D heatmaps as output. Using this pre-trained VLM as the backbone, we then fine-tune the entire VLA model while maintaining alignment between inputs and outputs by: (1) projecting raw point cloud inputs into multi-view images, and (2) predicting heatmaps before generating the final action. Extensive experiments show that the resulting model, BridgeVLA, can learn 3D manipulation both efficiently and effectively. BridgeVLA outperforms state-of-the-art baselines across three simulation benchmarks. In RLBench, it improves the average success rate from 81. 4\% to 88. 2\%. In COLOSSEUM, it demonstrates significantly better performance in challenging generalization settings, boosting the average success rate from 56. 7\% to 64. 0\%. In GemBench, it surpasses all the comparing baseline methods in terms of average success rate. In real-robot experiments, BridgeVLA outperforms a state-of-the-art baseline method by 32\% on average. It generalizes robustly in multiple out-of-distribution settings, including visual disturbances and unseen instructions. Remarkably, it is able to achieve a success rate of 95. 4\% on 10+ tasks with only 3 trajectories per task, while other VLA methods such as $\pi_{0}$ fail completely. Project Website: https: //bridgevla. github. io/.

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ICLR Conference 2025 Conference Paper

Enhancing End-to-End Autonomous Driving with Latent World Model

  • Yingyan Li
  • Lue Fan
  • Jiawei He 0002
  • Yuqi Wang 0001
  • Yuntao Chen
  • Zhaoxiang Zhang 0001
  • Tieniu Tan

In autonomous driving, end-to-end planners directly utilize raw sensor data, enabling them to extract richer scene features and reduce information loss compared to traditional planners. This raises a crucial research question: how can we develop better scene feature representations to fully leverage sensor data in end-to-end driving? Self-supervised learning methods show great success in learning rich feature representations in NLP and computer vision. Inspired by this, we propose a novel self-supervised learning approach using the LAtent World model (LAW) for end-to-end driving. LAW predicts future latent scene features based on current features and ego trajectories. This self-supervised task can be seamlessly integrated into perception-free and perception-based frameworks, improving scene feature learning while optimizing trajectory prediction. LAW achieves state-of-the-art performance across multiple benchmarks, including real-world open-loop benchmark nuScenes, NAVSIM, and simulator-based closed-loop benchmark CARLA. The code will be released.

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AAAI Conference 2025 Conference Paper

Exploring Vacant Classes in Label-Skewed Federated Learning

  • Kuangpu Guo
  • Yuhe Ding
  • Jian Liang
  • Zilei Wang
  • Ran He
  • Tieniu Tan

Label skews, characterized by disparities in local label distribution across clients, pose a significant challenge in federated learning. As minority classes suffer from worse accuracy due to overfitting on local imbalanced data, prior methods often incorporate class-balanced learning techniques during local training. Although these methods improve the mean accuracy across all classes, we observe that vacant classes—referring to categories absent from a client's data distribution—remain poorly recognized. Besides, there is still a gap in the accuracy of local models on minority classes compared to the global model. This paper introduces FedVLS, a novel approach to label-skewed federated learning that integrates both vacant-class distillation and logit suppression simultaneously. Specifically, vacant-class distillation leverages knowledge distillation during local training on each client to retain essential information related to vacant classes from the global model. Moreover, logit suppression directly penalizes network logits for non-label classes, effectively addressing misclassifications in minority classes that may be biased toward majority classes. Extensive experiments validate the efficacy of FedVLS, demonstrating superior performance compared to previous state-of-the-art (SOTA) methods across diverse datasets with varying degrees of label skews.

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ICLR Conference 2025 Conference Paper

LoRA-Pro: Are Low-Rank Adapters Properly Optimized?

  • Zhengbo Wang
  • Jian Liang 0001
  • Ran He 0001
  • Zilei Wang
  • Tieniu Tan

Low-rank adaptation, also known as LoRA, has emerged as a prominent method for parameter-efficient fine-tuning of foundation models. Despite its computational efficiency, LoRA still yields inferior performance compared to full fine-tuning. In this paper, we first uncover a fundamental connection between the optimization processes of LoRA and full fine-tuning: using LoRA for optimization is mathematically equivalent to full fine-tuning using a low-rank gradient for parameter updates. And this low-rank gradient can be expressed in terms of the gradients of the two low-rank matrices in LoRA. Leveraging this insight, we introduce LoRA-Pro, a method that enhances LoRA's performance by strategically adjusting the gradients of these low-rank matrices. This adjustment allows the low-rank gradient to more accurately approximate the full fine-tuning gradient, thereby narrowing the performance gap between LoRA and full fine-tuning. Furthermore, we theoretically derive the optimal solutions for adjusting the gradients of the low-rank matrices, applying them during fine-tuning in LoRA-Pro. We conduct extensive experiments across natural language understanding, dialogue generation, mathematical reasoning, code generation, and image classification tasks, demonstrating that LoRA-Pro substantially improves LoRA's performance, effectively narrowing the gap with full fine-tuning. Our code is publicly available at https://github.com/mrflogs/LoRA-Pro.

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AAAI Conference 2025 Conference Paper

Protecting Model Adaptation from Trojans in the Unlabeled Data

  • Lijun Sheng
  • Jian Liang
  • Ran He
  • Zilei Wang
  • Tieniu Tan

Model adaptation tackles the distribution shift problem with a pre-trained model instead of raw data, which has become a popular paradigm due to its great privacy protection. Existing methods always assume adapting to a clean target domain, overlooking the security risks of unlabeled samples. This paper for the first time explores the potential trojan attacks on model adaptation launched by well-designed poisoning target data. Concretely, we provide two trigger patterns with two poisoning strategies for different prior knowledge owned by attackers. These attacks achieve a high success rate while maintaining the normal performance on clean samples in the test stage. To defend against such backdoor injection, we propose a plug-and-play method named DiffAdapt, which can be seamlessly integrated with existing adaptation algorithms. Experiments across commonly used benchmarks and adaptation methods demonstrate the effectiveness of DiffAdapt. We hope this work will shed light on the safety of transfer learning with unlabeled data.

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NeurIPS Conference 2025 Conference Paper

Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing

  • Junfei Wu
  • Jian Guan
  • Kaituo Feng
  • Qiang Liu
  • Shu Wu
  • Liang Wang
  • Wei Wu
  • Tieniu Tan

As textual reasoning with large language models (LLMs) has advanced significant, there has been growing interest in enhancing the multimodal reasoning capabilities of large vision-language models (LVLMs). However, existing methods primarily approach multimodal reasoning in a straightforward, text-centric manner, where both reasoning and answer derivation are conducted purely through text, with the only difference being the presence of multimodal input. As a result, these methods often encounter fundamental limitations in spatial reasoning tasks that demand precise geometric understanding and continuous spatial tracking\textemdash capabilities that humans achieve through mental visualization and manipulation. To address the limitations, we propose drawing to reason in space, a novel paradigm that enables LVLMs to reason through elementary drawing operations in the visual space. By equipping models with basic drawing operations including annotating bounding boxes and drawing auxiliary lines, we empower them to express and analyze spatial relationships through direct visual manipulation, meanwhile avoiding the performance ceiling imposed by specialized perception tools in previous tool-integrated reasoning approaches. To cultivate this capability, we develop a three-stage training framework: cold-start training with synthetic data to establish basic drawing abilities, reflective rejection sampling to enhance self-reflection behaviors, and reinforcement learning to directly optimize for target rewards. Extensive experiments demonstrate that our model, named \textsc{Spark}, consistently outperforms existing methods across diverse spatial reasoning benchmarks involving maze navigation, static spatial reasoning, video-based reasoning and multi-view-based reasoning tasks, with an average improvement of 11. 5\%. Ablation studies reveal the critical role of each training stage, with reflective rejection sampling particularly enhancing the model's self-correction capabilities and reasoning potential.

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IJCAI Conference 2025 Conference Paper

TEST-V: TEst-time Support-set Tuning for Zero-shot Video Classification

  • Rui Yan
  • Jin Wang
  • Hongyu Qu
  • Xiaoyu Du
  • Dong Zhang
  • Jinhui Tang
  • Tieniu Tan

Recently, adapting Vision Language Models (VLMs) to zero-shot visual classification by tuning class embedding with a few prompts (Test-time Prompt Tuning, TPT) or replacing class names with generated visual samples (support-set) has shown promising results. However, TPT cannot avoid the semantic gap between modalities while the support-set cannot be tuned. To this end, we draw on each other's strengths and propose a novel framework, namely TEst-time Support-set Tuning for zero-shot Video Classification (TEST-V). It first dilates the support-set with multiple prompts (Multi-prompting Support-set Dilation, MSD) and then erodes the support-set via learnable weights to mine key cues dynamically (Temporal-aware Support-set Erosion, TSE). Specifically, i) MSD expands the support samples for each class based on multiple prompts inquired from LLMs to enrich the diversity of the support-set. ii) TSE tunes the support-set with factorized learnable weights according to the temporal prediction consistency in a self-supervised manner to dig pivotal supporting cues for each class. TEST-V achieves state-of-the-art results across four benchmarks and shows good interpretability.

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NeurIPS Conference 2025 Conference Paper

The Illusion of Progress? A Critical Look at Test-Time Adaptation for Vision-Language Models

  • Lijun Sheng
  • Jian Liang
  • Ran He
  • Zilei Wang
  • Tieniu Tan

Test-time adaptation (TTA) methods have gained significant attention for enhancing the performance of vision-language models (VLMs) such as CLIP during inference, without requiring additional labeled data. However, current TTA researches generally suffer from major limitations such as duplication of baseline results, limited evaluation metrics, inconsistent experimental settings, and insufficient analysis. These problems hinder fair comparisons between TTA methods and make it difficult to assess their practical strengths and weaknesses. To address these challenges, we introduce TTA-VLM, a comprehensive benchmark for evaluating TTA methods on VLMs. Our benchmark implements 8 episodic TTA and 7 online TTA methods within a unified and reproducible framework, and evaluates them across 15 widely used datasets. Unlike prior studies focused solely on CLIP, we extend the evaluation to SigLIP—a model trained with a Sigmoid loss—and include training-time tuning methods such as CoOp, MaPLe, and TeCoA to assess generality. Beyond classification accuracy, TTA-VLM incorporates various evaluation metrics, including robustness, calibration, out-of-distribution detection, and stability, enabling a more holistic assessment of TTA methods. Through extensive experiments, we find that 1) existing TTA methods produce limited gains compared to the previous pioneering work; 2) current TTA methods exhibit poor collaboration with training-time fine-tuning methods; 3) accuracy gains frequently come at the cost of reduced model trustworthiness. We release TTA-VLM to provide fair comparison and comprehensive evaluation of TTA methods for VLMs, and we hope it encourages the community to develop more reliable and generalizable TTA strategies. The code is available in https: //github. com/TomSheng21/tta-vlm.

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ICLR Conference 2024 Conference Paper

A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation

  • Zhengbo Wang
  • Jian Liang 0001
  • Lijun Sheng
  • Ran He 0001
  • Zilei Wang
  • Tieniu Tan

Contrastive Language-Image Pretraining (CLIP) has gained popularity for its remarkable zero-shot capacity. Recent research has focused on developing efficient fine-tuning methods, such as prompt learning and adapter, to enhance CLIP's performance in downstream tasks. However, these methods still require additional training time and computational resources, which is undesirable for devices with limited resources. In this paper, we revisit a classical algorithm, Gaussian Discriminant Analysis (GDA), and apply it to the downstream classification of CLIP. Typically, GDA assumes that features of each class follow Gaussian distributions with identical covariance. By leveraging Bayes' formula, the classifier can be expressed in terms of the class means and covariance, which can be estimated from the data without the need for training. To integrate knowledge from both visual and textual modalities, we ensemble it with the original zero-shot classifier within CLIP. Extensive results on 17 datasets validate that our method surpasses or achieves comparable results with state-of-the-art methods on few-shot classification, imbalanced learning, and out-of-distribution generalization. In addition, we extend our method to base-to-new generalization and unsupervised learning, once again demonstrating its superiority over competing approaches. Our code is publicly available at https://github.com/mrflogs/ICLR24.

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AAAI Conference 2024 Conference Paper

AE-NeRF: Audio Enhanced Neural Radiance Field for Few Shot Talking Head Synthesis

  • Dongze Li
  • Kang Zhao
  • Wei Wang
  • Bo Peng
  • Yingya Zhang
  • Jing Dong
  • Tieniu Tan

Audio-driven talking head synthesis is a promising topic with wide applications in digital human, film making and virtual reality. Recent NeRF-based approaches have shown superiority in quality and fidelity compared to previous studies. However, when it comes to few-shot talking head generation, a practical scenario where only few seconds of talking video is available for one identity, two limitations emerge: 1) they either have no base model, which serves as a facial prior for fast convergence, or ignore the importance of audio when building the prior; 2) most of them overlook the degree of correlation between different face regions and audio, e.g., mouth is audio related, while ear is audio independent. In this paper, we present Audio Enhanced Neural Radiance Field (AE-NeRF) to tackle the above issues, which can generate realistic portraits of a new speaker with few-shot dataset. Specifically, we introduce an Audio Aware Aggregation module into the feature fusion stage of the reference scheme, where the weight is determined by the similarity of audio between reference and target image. Then, an Audio-Aligned Face Generation strategy is proposed to model the audio related and audio independent regions respectively, with a dual-NeRF framework. Extensive experiments have shown AE-NeRF surpasses the state-of-the-art on image fidelity, audio-lip synchronization, and generalization ability, even in limited training set or training iterations.

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ICML Conference 2024 Conference Paper

Connecting the Dots: Collaborative Fine-tuning for Black-Box Vision-Language Models

  • Zhengbo Wang
  • Jian Liang 0001
  • Ran He 0001
  • Zilei Wang
  • Tieniu Tan

With the emergence of pretrained vision-language models (VLMs), considerable efforts have been devoted to fine-tuning them for downstream tasks. Despite the progress made in designing efficient fine-tuning methods, such methods require access to the model’s parameters, which can be challenging as model owners often opt to provide their models as a black box to safeguard model ownership. This paper proposes a C ollabo ra tive F ine- T uning ( CraFT ) approach for fine-tuning black-box VLMs to downstream tasks, where one only has access to the input prompts and the output predictions of the model. CraFT comprises two modules, a prompt generation module for learning text prompts and a prediction refinement module for enhancing output predictions in residual style. Additionally, we introduce an auxiliary prediction-consistent loss to promote consistent optimization across these modules. These modules are optimized by a novel collaborative training algorithm. Extensive experiments on few-shot classification over 15 datasets demonstrate the superiority of CraFT. The results show that CraFT achieves a decent gain of about 12% with 16-shot datasets and only 8, 000 queries. Moreover, CraFT trains faster and uses only about 1/80 of the memory footprint for deployment, while sacrificing only 1. 62% compared to the white-box method. Our code is publicly available at https: //github. com/mrflogs/CraFT.

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IJCAI Conference 2024 Conference Paper

DTS-TPT: Dual Temporal-Sync Test-time Prompt Tuning for Zero-shot Activity Recognition

  • Rui Yan
  • Hongyu Qu
  • Xiangbo Shu
  • Wenbin Li
  • Jinhui Tang
  • Tieniu Tan

Finetuning the large vision-language models on video data with a set of learnable prompts has shown promising performance on zero-shot activity recognition but still requires extra video data and expensive training costs. Inspired by recent Test-time Prompt Tuning (TPT) on the image domain, this work attempts to extend TPT to video data for zero-shot activity recognition. However, monotonous spatial augmentation and short class names cannot meet the need to capture diverse and complicated semantics of human behavior during prompt tuning. To this end, this work proposes a Dual Temporal-Sync Test-time Prompt Tuning (DTS-TPT) framework for zero-shot activity recognition. DTS-TPT tunes the learnable prompts appended to text inputs on video feature sequences of different temporal scales in multiple steps during test time. In each tuning step, we minimize the semantic consistency among the predictions from video feature sequences randomly augmented via AugMix with both original class names and the corresponding description generated through LLM. Compared with the state-of-the-art methods, the proposed method improves the zero-shot top-1 accuracy by approximately 2% ~ 5% on popular benchmarks. The code is available at https: //github. com/quhongyu/DTS-TPT.

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ICML Conference 2024 Conference Paper

Realistic Unsupervised CLIP Fine-tuning with Universal Entropy Optimization

  • Jian Liang 0001
  • Lijun Sheng
  • Zhengbo Wang
  • Ran He 0001
  • Tieniu Tan

The emergence of vision-language models, such as CLIP, has spurred a significant research effort towards their application for downstream supervised learning tasks. Although some previous studies have explored the unsupervised fine-tuning of CLIP, they often rely on prior knowledge in the form of class names associated with ground truth labels. This paper explores a realistic unsupervised fine-tuning scenario, considering the presence of out-of-distribution samples from unknown classes within the unlabeled data. In particular, we focus on simultaneously enhancing out-of-distribution detection and the recognition of instances associated with known classes. To tackle this problem, we present a simple, efficient, and effective approach called Universal Entropy Optimization (UEO). UEO leverages sample-level confidence to approximately minimize the conditional entropy of confident instances and maximize the marginal entropy of less confident instances. Apart from optimizing the textual prompt, UEO incorporates optimization of channel-wise affine transformations within the visual branch of CLIP. Extensive experiments across 15 domains and 4 different types of prior knowledge validate the effectiveness of UEO compared to baseline methods. The code is at https: //github. com/tim-learn/UEO.

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NeurIPS Conference 2024 Conference Paper

VLKEB: A Large Vision-Language Model Knowledge Editing Benchmark

  • Han Huang
  • Haitian Zhong
  • Tao Yu
  • Qiang Liu
  • Shu Wu
  • Liang Wang
  • Tieniu Tan

Recently, knowledge editing on large language models (LLMs) has received considerable attention. Compared to this, editing Large Vision-Language Models (LVLMs) faces extra challenges from diverse data modalities and complicated model components, and data for LVLMs editing are limited. The existing LVLM editing benchmark, which comprises three metrics (Reliability, Locality, and Generality), falls short in the quality of synthesized evaluation images and cannot assess whether models apply edited knowledge in relevant content. Therefore, we employ more reliable data collection methods to construct a new Large $\textbf{V}$ision-$\textbf{L}$anguage Model $\textbf{K}$nowledge $\textbf{E}$diting $\textbf{B}$enchmark, $\textbf{VLKEB}$, and extend the Portability metric for more comprehensive evaluation. Leveraging a multi-modal knowledge graph, our image data are bound with knowledge entities. This can be further used to extract entity-related knowledge, which constitutes the base of editing data. We conduct experiments of different editing methods on five LVLMs, and thoroughly analyze how do they impact the models. The results reveal strengths and deficiencies of these methods and hopefully provide insights for future research. The codes and dataset are available at: https: //github. com/VLKEB/VLKEB.

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ICML Conference 2023 Conference Paper

AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation

  • Yifan Zhang 0004
  • Xue Wang 0010
  • Kexin Jin
  • Kun Yuan 0001
  • Zhang Zhang 0001
  • Liang Wang 0001
  • Rong Jin 0001
  • Tieniu Tan

Many recent machine learning tasks focus to develop models that can generalize to unseen distributions. Domain generalization (DG) has become one of the key topics in various fields. Several literatures show that DG can be arbitrarily hard without exploiting target domain information. To address this issue, test-time adaptive (TTA) methods are proposed. Existing TTA methods require offline target data or extra sophisticated optimization procedures during the inference stage. In this work, we adopt N on- P arametric C lassifier to perform the test-time Ada ptation ( AdaNPC ). In particular, we construct a memory that contains the feature and label pairs from training domains. During inference, given a test instance, AdaNPC first recalls $k$ closed samples from the memory to vote for the prediction, and then the test feature and predicted label are added to the memory. In this way, the sample distribution in the memory can be gradually changed from the training distribution towards the test distribution with very little extra computation cost. We theoretically justify the rationality behind the proposed method. Besides, we test our model on extensive numerical experiments. AdaNPC significantly outperforms competitive baselines on various DG benchmarks. In particular, when the adaptation target is a series of domains, the adaptation accuracy of AdaNPC is $50$% higher than advanced TTA methods.

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AAAI Conference 2023 Conference Paper

CFFT-GAN: Cross-Domain Feature Fusion Transformer for Exemplar-Based Image Translation

  • Tianxiang Ma
  • Bingchuan Li
  • Wei Liu
  • Miao Hua
  • Jing Dong
  • Tieniu Tan

Exemplar-based image translation refers to the task of generating images with the desired style, while conditioning on certain input image. Most of the current methods learn the correspondence between two input domains and lack the mining of information within the domain. In this paper, we propose a more general learning approach by considering two domain features as a whole and learning both inter-domain correspondence and intra-domain potential information interactions. Specifically, we propose a Cross-domain Feature Fusion Transformer (CFFT) to learn inter- and intra-domain feature fusion. Based on CFFT, the proposed CFFT-GAN works well on exemplar-based image translation. Moreover, CFFT-GAN is able to decouple and fuse features from multiple domains by cascading CFFT modules. We conduct rich quantitative and qualitative experiments on several image translation tasks, and the results demonstrate the superiority of our approach compared to state-of-the-art methods. Ablation studies show the importance of our proposed CFFT. Application experimental results reflect the potential of our method.

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ICLR Conference 2023 Conference Paper

Free Lunch for Domain Adversarial Training: Environment Label Smoothing

  • Yifan Zhang 0004
  • Xue Wang 0010
  • Jian Liang 0001
  • Zhang Zhang 0001
  • Liang Wang 0001
  • Rong Jin 0001
  • Tieniu Tan

A fundamental challenge for machine learning models is how to generalize learned models for out-of-distribution (OOD) data. Among various approaches, exploiting invariant features by Domain Adversarial Training (DAT) received widespread attention. Despite its success, we observe training instability from DAT, mostly due to over-confident domain discriminator and environment label noise. To address this issue, we proposed Environment Label Smoothing (ELS), which encourages the discriminator to output soft probability, which thus reduces the confidence of the discriminator and alleviates the impact of noisy environment labels. We demonstrate, both experimentally and theoretically, that ELS can improve training stability, local convergence, and robustness to noisy environment labels. By incorporating ELS with DAT methods, we are able to yield state-of-art results on a wide range of domain generalization/adaptation tasks, particularly when the environment labels are highly noisy.

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NeurIPS Conference 2023 Conference Paper

OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling

  • Yifan Zhang
  • Qingsong Wen
  • Xue Wang
  • Weiqi Chen
  • Liang Sun
  • Zhang Zhang
  • Liang Wang
  • Rong Jin

Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data. Many algorithms are designed for online time series forecasting, with some exploiting cross-variable dependency while others assume independence among variables. Given every data assumption has its own pros and cons in online time series modeling, we propose **On**line **e**nsembling **Net**work (**OneNet**). It dynamically updates and combines two models, with one focusing on modeling the dependency across the time dimension and the other on cross-variate dependency. Our method incorporates a reinforcement learning-based approach into the traditional online convex programming framework, allowing for the linear combination of the two models with dynamically adjusted weights. OneNet addresses the main shortcoming of classical online learning methods that tend to be slow in adapting to the concept drift. Empirical results show that OneNet reduces online forecasting error by more than $\mathbf{50}\\%$ compared to the State-Of-The-Art (SOTA) method.

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AAAI Conference 2023 Conference Paper

Semantic 3D-Aware Portrait Synthesis and Manipulation Based on Compositional Neural Radiance Field

  • Tianxiang Ma
  • Bingchuan Li
  • Qian He
  • Jing Dong
  • Tieniu Tan

Recently 3D-aware GAN methods with neural radiance field have developed rapidly. However, current methods model the whole image as an overall neural radiance field, which limits the partial semantic editability of synthetic results. Since NeRF renders an image pixel by pixel, it is possible to split NeRF in the spatial dimension. We propose a Compositional Neural Radiance Field (CNeRF) for semantic 3D-aware portrait synthesis and manipulation. CNeRF divides the image by semantic regions and learns an independent neural radiance field for each region, and finally fuses them and renders the complete image. Thus we can manipulate the synthesized semantic regions independently, while fixing the other parts unchanged. Furthermore, CNeRF is also designed to decouple shape and texture within each semantic region. Compared to state-of-the-art 3D-aware GAN methods, our approach enables fine-grained semantic region manipulation, while maintaining high-quality 3D-consistent synthesis. The ablation studies show the effectiveness of the structure and loss function used by our method. In addition real image inversion and cartoon portrait 3D editing experiments demonstrate the application potential of our method.

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ICML Conference 2022 Conference Paper

Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring

  • Zhengquan Luo
  • Yunlong Wang 0003
  • Zilei Wang
  • Zhenan Sun
  • Tieniu Tan

Attributes skew hinders the current federated learning (FL) frameworks from consistent optimization directions among the clients, which inevitably leads to performance reduction and unstable convergence. The core problems lie in that: 1) Domain-specific attributes, which are non-causal and only locally valid, are indeliberately mixed into global aggregation. 2) The one-stage optimizations of entangled attributes cannot simultaneously satisfy two conflicting objectives, i. e. , generalization and personalization. To cope with these, we proposed disentangled federated learning (DFL) to disentangle the domain-specific and cross-invariant attributes into two complementary branches, which are trained by the proposed alternating local-global optimization independently. Importantly, convergence analysis proves that the FL system can be stably converged even if incomplete client models participate in the global aggregation, which greatly expands the application scope of FL. Extensive experiments verify that DFL facilitates FL with higher performance, better interpretability, and faster convergence rate, compared with SOTA FL methods on both manually synthesized and realistic attributes skew datasets.

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AAAI Conference 2022 Conference Paper

Generalizable Person Re-identification via Self-Supervised Batch Norm Test-Time Adaption

  • Ke Han
  • Chenyang Si
  • Yan Huang
  • Liang Wang
  • Tieniu Tan

In this paper, we investigate the generalization problem of person re-identification (re-id), whose major challenge is the distribution shift on an unseen domain. As an important tool of regularizing the distribution, batch normalization (BN) has been widely used in existing methods. However, they neglect that BN is severely biased to the training domain and inevitably suffers the performance drop if directly generalized without being updated. To tackle this issue, we propose Batch Norm Test-time Adaption (BNTA), a novel re-id framework that applies the self-supervised strategy to update BN parameters adaptively. Specifically, BNTA quickly explores the domain-aware information within unlabeled target data before inference, and accordingly modulates the feature distribution normalized by BN to adapt to the target domain. This is accomplished by two designed self-supervised auxiliary tasks, namely part positioning and part nearest neighbor matching, which help the model mine the domain-aware information with respect to the structure and identity of body parts, respectively. To demonstrate the effectiveness of our method, we conduct extensive experiments on three re-id datasets and confirm the superior performance to the stateof-the-art methods.

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IJCAI Conference 2022 Conference Paper

GraphDIVE: Graph Classification by Mixture of Diverse Experts

  • Fenyu Hu
  • Liping Wang
  • Qiang Liu
  • Shu Wu
  • Liang Wang
  • Tieniu Tan

Graph classification is a challenging research task in many applications across a broad range of domains. Recently, Graph Neural Network (GNN) models have achieved superior performance on various real-world graph datasets. Despite their successes, most of current GNN models largely suffer from the ubiquitous class imbalance problem, which typically results in prediction bias towards majority classes. Although many imbalanced learning methods have been proposed, they mainly focus on regular Euclidean data and cannot well utilize topological structure of graph (non-Euclidean) data. To boost the performance of GNNs and investigate the relationship between topological structure and class imbalance, we propose GraphDIVE, which learns multi-view graph representations and combine multi-view experts (i. e. , classifiers). Specifically, multi-view graph representations correspond to the intrinsic diverse graph topological structure characteristics. Extensive experiments on molecular benchmark datasets demonstrate the effectiveness of the proposed approach.

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IJCAI Conference 2021 Conference Paper

Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images

  • Wentao Chen
  • Chenyang Si
  • Wei Wang
  • Liang Wang
  • Zilei Wang
  • Tieniu Tan

Few-shot learning is a challenging task since only few instances are given for recognizing an unseen class. One way to alleviate this problem is to acquire a strong inductive bias via meta-learning on similar tasks. In this paper, we show that such inductive bias can be learned from a flat collection of unlabeled images, and instantiated as transferable representations among seen and unseen classes. Specifically, we propose a novel part-based self-supervised representation learning scheme to learn transferable representations by maximizing the similarity of an image to its discriminative part. To mitigate the overfitting in few-shot classification caused by data scarcity, we further propose a part augmentation strategy by retrieving extra images from a base dataset. We conduct systematic studies on miniImageNet and tieredImageNet benchmarks. Remarkably, our method yields impressive results, outperforming the previous best unsupervised methods by 7. 74% and 9. 24% under 5-way 1-shot and 5-way 5-shot settings, which are comparable with state-of-the-art supervised methods.

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AAAI Conference 2020 Conference Paper

CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation

  • Junsong Fan
  • Zhaoxiang Zhang
  • Tieniu Tan
  • Chunfeng Song
  • Jun Xiao

Weakly supervised semantic segmentation with only imagelevel labels saves large human effort to annotate pixel-level labels. Cutting-edge approaches rely on various innovative constraints and heuristic rules to generate the masks for every single image. Although great progress has been achieved by these methods, they treat each image independently and do not take account of the relationships across different images. In this paper, however, we argue that the cross-image relationship is vital for weakly supervised segmentation. Because it connects related regions across images, where supplementary representations can be propagated to obtain more consistent and integral regions. To leverage this information, we propose an end-to-end cross-image affinity module, which exploits pixel-level cross-image relationships with only imagelevel labels. By means of this, our approach achieves 64. 3% and 65. 3% mIoU on Pascal VOC 2012 validation and test set respectively, which is a new state-of-the-art result by only using image-level labels for weakly supervised semantic segmentation, demonstrating the superiority of our approach.

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AAAI Conference 2020 Conference Paper

Dynamic Graph Representation for Occlusion Handling in Biometrics

  • Min Ren
  • Yunlong Wang
  • Zhenan Sun
  • Tieniu Tan

The generalization ability of Convolutional neural networks (CNNs) for biometrics drops greatly due to the adverse effects of various occlusions. To this end, we propose a novel unified framework integrated the merits of both CNNs and graphical models to learn dynamic graph representations for occlusion problems in biometrics, called Dynamic Graph Representation (DGR). Convolutional features onto certain regions are re-crafted by a graph generator to establish the connections among the spatial parts of biometrics and build Feature Graphs based on these node representations. Each node of Feature Graphs corresponds to a specific part of the input image and the edges express the spatial relationships between parts. By analyzing the similarities between the nodes, the framework is able to adaptively remove the nodes representing the occluded parts. During dynamic graph matching, we propose a novel strategy to measure the distances of both nodes and adjacent matrixes. In this way, the proposed method is more convincing than CNNs-based methods because the dynamic graph method implies a more illustrative and reasonable inference of the biometrics decision. Experiments conducted on iris and face demonstrate the superiority of the proposed framework, which boosts the accuracy of occluded biometrics recognition by a large margin comparing with baseline methods.

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AAAI Conference 2020 Conference Paper

Pose-Guided Multi-Granularity Attention Network for Text-Based Person Search

  • Ya Jing
  • Chenyang Si
  • Junbo Wang
  • Wei Wang
  • Liang Wang
  • Tieniu Tan

Text-based person search aims to retrieve the corresponding person images in an image database by virtue of a describing sentence about the person, which poses great potential for various applications such as video surveillance. Extracting visual contents corresponding to the human description is the key to this cross-modal matching problem. Moreover, correlated images and descriptions involve different granularities of semantic relevance, which is usually ignored in previous methods. To exploit the multilevel corresponding visual contents, we propose a pose-guided multi-granularity attention network (PMA). Firstly, we propose a coarse alignment network (CA) to select the related image regions to the global description by a similarity-based attention. To further capture the phrase-related visual body part, a fine-grained alignment network (FA) is proposed, which employs pose information to learn latent semantic alignment between visual body part and textual noun phrase. To verify the effectiveness of our model, we perform extensive experiments on the CUHK Person Description Dataset (CUHK-PEDES) which is currently the only available dataset for text-based person search. Experimental results show that our approach outperforms the state-of-the-art methods by 15 % in terms of the top-1 metric.

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NeurIPS Conference 2020 Conference Paper

Unfolding the Alternating Optimization for Blind Super Resolution

  • zhengxiong luo
  • Yan Huang
  • Shang Li
  • Liang Wang
  • Tieniu Tan

Previous methods decompose blind super resolution (SR) problem into two sequential steps: \textit{i}) estimating blur kernel from given low-resolution (LR) image and \textit{ii}) restoring SR image based on estimated kernel. This two-step solution involves two independently trained models, which may not well compatible with each other. Small estimation error of the first step could cause severe performance drop of the second one. While on the other hand, the first step can only utilize limited information from LR image, which makes it difficult to predict highly accurate blur kernel. Towards these issues, instead of considering these two steps separately, we adopt an alternating optimization algorithm, which can estimate blur kernel and restore SR image in a single model. Specifically, we design two convolutional neural modules, namely \textit{Restorer} and \textit{Estimator}. \textit{Restorer} restores SR image based on predicted kernel, and \textit{Estimator} estimates blur kernel with the help of restored SR image. We alternate these two modules repeatedly and unfold this process to form an end-to-end trainable network. In this way, \textit{Estimator} utilizes information from both LR and SR images, which makes the estimation of blur kernel easier. More importantly, \textit{Restorer} is trained with the kernel estimated by \textit{Estimator}, instead of ground-truth kernel, thus \textit{Restorer} could be more tolerant to the estimation error of \textit{Estimator}. Extensive experiments on synthetic datasets and real-world images show that our model can largely outperform state-of-the-art methods and produce more visually favorable results at much higher speed. The source code will be publicly available.

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AAAI Conference 2019 Conference Paper

Attention-Aware Sampling via Deep Reinforcement Learning for Action Recognition

  • Wenkai Dong
  • Zhaoxiang Zhang
  • Tieniu Tan

Deep learning based methods have achieved remarkable progress in action recognition. Existing works mainly focus on designing novel deep architectures to achieve video representations learning for action recognition. Most methods treat sampled frames equally and average all the frame-level predictions at the testing stage. However, within a video, discriminative actions may occur sparsely in a few frames and most other frames are irrelevant to the ground truth and may even lead to a wrong prediction. As a result, we think that the strategy of selecting relevant frames would be a further important key to enhance the existing deep learning based action recognition. In this paper, we propose an attentionaware sampling method for action recognition, which aims to discard the irrelevant and misleading frames and preserve the most discriminative frames. We formulate the process of mining key frames from videos as a Markov decision process and train the attention agent through deep reinforcement learning without extra labels. The agent takes features and predictions from the baseline model as input and generates importance scores for all frames. Moreover, our approach is extensible, which can be applied to different existing deep learning based action recognition models. We achieve very competitive action recognition performance on two widely used action recognition datasets.

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NeurIPS Conference 2019 Conference Paper

Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection

  • Junran Peng
  • Ming Sun
  • ZHAO-XIANG ZHANG
  • Tieniu Tan
  • Junjie Yan

Recently, Neural Architecture Search has achieved great success in large-scale image classification. In contrast, there have been limited works focusing on architecture search for object detection, mainly because the costly ImageNet pretraining is always required for detectors. Training from scratch, as a substitute, demands more epochs to converge and brings no computation saving. To overcome this obstacle, we introduce a practical neural architecture transformation search(NATS) algorithm for object detection in this paper. Instead of searching and constructing an entire network, NATS explores the architecture space on the base of existing network and reusing its weights. We propose a novel neural architecture search strategy in channel-level instead of path-level and devise a search space specially targeting at object detection. With the combination of these two designs, an architecture transformation scheme could be discovered to adapt a network designed for image classification to task of object detection. Since our method is gradient-based and only searches for a transformation scheme, the weights of models pretrained in ImageNet could be utilized in both searching and retraining stage, which makes the whole process very efficient. The transformed network requires no extra parameters and FLOPs, and is friendly to hardware optimization, which is practical to use in real-time application. In experiments, we demonstrate the effectiveness of NATS on networks like {\em ResNet} and {\em ResNeXt}. Our transformed networks, combined with various detection frameworks, achieve significant improvements on the COCO dataset while keeping fast.

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AAAI Conference 2019 Conference Paper

Session-Based Recommendation with Graph Neural Networks

  • Shu Wu
  • Yuyuan Tang
  • Yanqiao Zhu
  • Liang Wang
  • Xing Xie
  • Tieniu Tan

The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user vectors in sessions and neglect complex transitions of items. To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i. e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity. In the proposed method, session sequences are modeled as graphstructured data. Based on the session graph, GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods. Each session is then represented as the composition of the global preference and the current interest of that session using an attention network. Extensive experiments conducted on two real datasets show that SR-GNN evidently outperforms the state-of-the-art session-based recommendation methods consistently.

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AAAI Conference 2018 Conference Paper

Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification

  • Yi Li
  • Lingxiao Song
  • Xiang Wu
  • Ran He
  • Tieniu Tan

Makeup is widely used to improve facial attractiveness and is well accepted by the public. However, different makeup styles will result in significant facial appearance changes. It remains a challenging problem to match makeup and non-makeup face images. This paper proposes a learning from generation approach for makeup-invariant face verification by introducing a bi-level adversarial network (BLAN). To alleviate the negative effects from makeup, we first generate non-makeup images from makeup ones, and then use the synthesized nonmakeup images for further verification. Two adversarial networks in BLAN are integrated in an end-to-end deep network, with the one on pixel level for reconstructing appealing facial images and the other on feature level for preserving identity information. These two networks jointly reduce the sensing gap between makeup and non-makeup images. Moreover, we make the generator well constrained by incorporating multiple perceptual losses. Experimental results on three benchmark makeup face datasets demonstrate that our method achieves state-of-the-art verification accuracy across makeup status and can produce photo-realistic non-makeup face images.

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AAAI Conference 2018 Conference Paper

Coupled Deep Learning for Heterogeneous Face Recognition

  • Xiang Wu
  • Lingxiao Song
  • Ran He
  • Tieniu Tan

Heterogeneous face matching is a challenge issue in face recognition due to large domain difference as well as insufficient pairwise images in different modalities during training. This paper proposes a coupled deep learning (CDL) approach for the heterogeneous face matching. CDL seeks a shared feature space in which the heterogeneous face matching problem can be approximately treated as a homogeneous face matching problem. The objective function of CDL mainly includes two parts. The first part contains a trace norm and a block-diagonal prior as relevance constraints, which not only make unpaired images from multiple modalities be clustered and correlated, but also regularize the parameters to alleviate overfitting. An approximate variational formulation is introduced to deal with the difficulties of optimizing low-rank constraint directly. The second part contains a cross modal ranking among triplet domain specific images to maximize the margin for different identities and increase data for a small amount of training samples. Besides, an alternating minimization method is employed to iteratively update the parameters of CDL. Experimental results show that CDL achieves better performance on the challenging CASIA NIR-VIS 2. 0 face recognition database, the IIIT-D Sketch database, the CUHK Face Sketch (CUFS), and the CUHK Face Sketch FERET (CUFSF), which significantly outperforms state-ofthe-art heterogeneous face recognition methods.

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AAAI Conference 2018 Conference Paper

Deep Semantic Structural Constraints for Zero-Shot Learning

  • Yan Li
  • Zhen Jia
  • Junge Zhang
  • Kaiqi Huang
  • Tieniu Tan

Zero-shot learning aims to classify unseen image categories by learning a visual-semantic embedding space. In most cases, the traditional methods adopt a separated two-step pipeline that extracts image features from pre-trained CNN models. Then the fixed image features are utilized to learn the embedding space. It leads to the lack of specific structural semantic information of image features for zero-shot learning task. In this paper, we propose an end-to-end trainable Deep Semantic Structural Constraints model to address this issue. The proposed model contains the Image Feature Structure constraint and the Semantic Embedding Structure constraint, which aim to learn structure-preserving image features and endue the learned embedding space with stronger generalization ability respectively. With the assistance of semantic structural information, the model gains more auxiliary clues for zero-shot learning. The state-of-the-art performance certifies the effectiveness of our proposed method.

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AAAI Conference 2018 Conference Paper

DF 2 Net: Discriminative Feature Learning and Fusion Network for RGB-D Indoor Scene Classification

  • Yabei Li
  • Junge Zhang
  • Yanhua Cheng
  • Kaiqi Huang
  • Tieniu Tan

This paper focuses on the task of RGB-D indoor scene classification. It is a very challenging task due to two folds. 1) Learning robust representation for indoor scene is difficult because of various objects and layouts. 2) Fusing the complementary cues in RGB and Depth is nontrivial since there are large semantic gaps between the two modalities. Most existing works learn representation for classification by training a deep network with softmax loss and fuse the two modalities by simply concatenating the features of them. However, these pipelines do not explicitly consider intra-class and interclass similarity as well as inter-modal intrinsic relationships. To address these problems, this paper proposes a Discriminative Feature Learning and Fusion Network (DF2 Net) with two-stage training. In the first stage, to better represent scene in each modality, a deep multi-task network is constructed to simultaneously minimize the structured loss and the softmax loss. In the second stage, we design a novel discriminative fusion network which is able to learn correlative features of multiple modalities and distinctive features of each modality. Extensive analysis and experiments on SUN RGB-D Dataset and NYU Depth Dataset V2 show the superiority of DF2 Net over other state-of-the-art methods in RGB-D indoor scene classification task.

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NeurIPS Conference 2018 Conference Paper

IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis

  • Huaibo Huang
  • Zhihang Li
  • Ran He
  • Zhenan Sun
  • Tieniu Tan

We present a novel introspective variational autoencoder (IntroVAE) model for synthesizing high-resolution photographic images. IntroVAE is capable of self-evaluating the quality of its generated samples and improving itself accordingly. Its inference and generator models are jointly trained in an introspective way. On one hand, the generator is required to reconstruct the input images from the noisy outputs of the inference model as normal VAEs. On the other hand, the inference model is encouraged to classify between the generated and real samples while the generator tries to fool it as GANs. These two famous generative frameworks are integrated in a simple yet efficient single-stream architecture that can be trained in a single stage. IntroVAE preserves the advantages of VAEs, such as stable training and nice latent manifold. Unlike most other hybrid models of VAEs and GANs, IntroVAE requires no extra discriminators, because the inference model itself serves as a discriminator to distinguish between the generated and real samples. Experiments demonstrate that our method produces high-resolution photo-realistic images (e. g. , CELEBA images at (1024^{2})), which are comparable to or better than the state-of-the-art GANs.

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AAAI Conference 2018 Conference Paper

Lateral Inhibition-Inspired Convolutional Neural Network for Visual Attention and Saliency Detection

  • Chunshui Cao
  • Yongzhen Huang
  • Zilei Wang
  • Liang Wang
  • Ninglong Xu
  • Tieniu Tan

Lateral inhibition in top-down feedback is widely existing in visual neurobiology, but such an important mechanism has not be well explored yet in computer vision. In our recent research, we find that modeling lateral inhibition in convolutional neural network (LICNN) is very useful for visual attention and saliency detection. In this paper, we propose to formulate lateral inhibition inspired by the related studies from neurobiology, and embed it into the top-down gradient computation of a general CNN for classification, i. e. only category-level information is used. After this operation (only conducted once), the network has the ability to generate accurate category-specific attention maps. Further, we apply LICNN for weakly-supervised salient object detection. Extensive experimental studies on a set of databases, e. g. , EC- SSD, HKU-IS, PASCAL-S and DUT-OMRON, demonstrate the great advantage of LICNN which achieves the state-ofthe-art performance. It is especially impressive that LICNN with only category-level supervised information even outperforms some recent methods with segmentation-level supervised learning.

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IJCAI Conference 2017 Conference Paper

A Convolutional Approach for Misinformation Identification

  • Feng Yu
  • Qiang Liu
  • Shu Wu
  • Liang Wang
  • Tieniu Tan

The fast expanding of social media fuels the spreading of misinformation which disrupts people's normal lives. It is urgent to achieve goals of misinformation identification and early detection in social media. In dynamic and complicated social media scenarios, some conventional methods mainly concentrate on feature engineering which fail to cover potential features in new scenarios and have difficulty in shaping elaborate high-level interactions among significant features. Moreover, a recent Recurrent Neural Network (RNN) based method suffers from deficiencies that it is not qualified for practical early detection of misinformation and poses a bias to the latest input. In this paper, we propose a novel method, Convolutional Approach for Misinformation Identification (CAMI) based on Convolutional Neural Network (CNN). CAMI can flexibly extract key features scattered among an input sequence and shape high-level interactions among significant features, which help effectively identify misinformation and achieve practical early detection. Experiment results on two large-scale datasets validate the effectiveness of CAMI model on both misinformation identification and early detection tasks.

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NeurIPS Conference 2017 Conference Paper

Deep Supervised Discrete Hashing

  • Qi Li
  • Zhenan Sun
  • Ran He
  • Tieniu Tan

With the rapid growth of image and video data on the web, hashing has been extensively studied for image or video search in recent years. Benefiting from recent advances in deep learning, deep hashing methods have achieved promising results for image retrieval. However, there are some limitations of previous deep hashing methods (e. g. , the semantic information is not fully exploited). In this paper, we develop a deep supervised discrete hashing algorithm based on the assumption that the learned binary codes should be ideal for classification. Both the pairwise label information and the classification information are used to learn the hash codes within one stream framework. We constrain the outputs of the last layer to be binary codes directly, which is rarely investigated in deep hashing algorithm. Because of the discrete nature of hash codes, an alternating minimization method is used to optimize the objective function. Experimental results have shown that our method outperforms current state-of-the-art methods on benchmark datasets.

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AAAI Conference 2017 Conference Paper

Learning Invariant Deep Representation for NIR-VIS Face Recognition

  • Ran He
  • Xiang Wu
  • Zhenan Sun
  • Tieniu Tan

Visual versus near infrared (VIS-NIR) face recognition is still a challenging heterogeneous task due to large appearance difference between VIS and NIR modalities. This paper presents a deep convolutional network approach that uses only one network to map both NIR and VIS images to a compact Euclidean space. The low-level layers of this network are trained only on large-scale VIS data. Each convolutional layer is implemented by the simplest case of maxout operator. The highlevel layer is divided into two orthogonal subspaces that contain modality-invariant identity information and modalityvariant spectrum information respectively. Our joint formulation leads to an alternating minimization approach for deep representation at the training time and an efficient computation for heterogeneous data at the testing time. Experimental evaluations show that our method achieves 94% verification rate at FAR=0. 1% on the challenging CASIA NIR-VIS 2. 0 face recognition dataset. Compared with state-of-the-art methods, it reduces the error rate by 58% only with a compact 64-D representation.

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IJCAI Conference 2016 Conference Paper

Group-Invariant Cross-Modal Subspace Learning

  • Jian Liang
  • Ran He
  • Zhenan Sun
  • Tieniu Tan

Cross-modal learning tries to find various types of heterogeneous data (e. g. , image) from a given query (e. g. , text). Most cross-modal algorithms heavily rely on semantic labels and benefit from a semantic-preserving aggregation of pairs of heterogeneous data. However, the semantic labels are not readily obtained in many real-world applications. This paper studies the aggregation of these pairs unsupervisedly. Apart from lower pairwise correspondences that force the data from one pair to be close to each other, we propose a novel concept, referred as groupwise correspondences, supposing that each paired heterogeneous data are from an identical latent group. We incorporate this groupwise correspondences into canonical correlation analysis (CCA) model, and seek a latent common subspace where data are naturally clustered into several latent groups. To simplify this nonconvex and nonsmooth problem, we introduce a non-negative orthogonal variable to represent the soft group membership, then two coupled computationally efficient subproblems (a generalized ratio-trace problem and a non-negative problem) are alternatively minimized to guarantee the proposed algorithm converges locally. Experimental results on two benchmark datasets demonstrate that the proposed unsupervised algorithm even achieves comparable performance to some state-of-the-art supervised cross-modal algorithms. Cross-modal learning tries to find various types of heterogeneous data (e. g. , image) from a given query (e. g. , text). Most cross-modal algorithms heavily rely on semantic labels and benefit from a semantic-preserving aggregation of pairs of heterogeneous data. However, the semantic labels are not readily obtained in many real-world applications. This paper studies the aggregation of these pairs unsupervisedly. Apart from lower pairwise correspondences that force the data from one pair to be close to each other, we propose a novel concept, referred as groupwise correspondences, supposing that each paired heterogeneous data are from an identical latent group. We incorporate this groupwise correspondences into canonical correlation analysis (CCA) model, and seek a latent common subspace where data are naturally clustered into several latent groups. To simplify this nonconvex and nonsmooth problem, we introduce a non-negative orthogonal variable to represent the soft group membership, then two coupled computationally efficient subproblems (a generalized ratio-trace problem and a non-negative problem) are alternatively minimized to guarantee the proposed algorithm converges locally. Experimental results on two benchmark datasets demonstrate that the proposed unsupervised algorithm even achieves comparable performance to some state-of-the-art supervised cross-modal algorithms.

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AAAI Conference 2016 Conference Paper

Information Credibility Evaluation on Social Media

  • Shu Wu
  • Qiang Liu
  • Yong Liu
  • Liang Wang
  • Tieniu Tan

With the growth of social media, rumors are spread fast and viewed by more and more people on the Internet. Rumors bring significant harm to daily life and public security. It is crucial to evaluate the credibility of information and detect the rumors on social media automatically. In this work, we establish a Network Information Credibility Evaluation (NICE) platform, which collects a database of rumors that have been verified on Sina Weibo and automatically evaluates the information which is generated by users on social media but has not been verified. Users can use a query to search related information. If the according information appears in our database, users can identify it is a rumor immediately. Otherwise, NICE will show users with realtime results crawled automatically from social media and can calculate credibility of a specific result with our algorithm. Our algorithm learns dynamic representations for information on social media based on behavior information, dynamic information, user information and comment information. Then, we use an ordinary logistic regression to classify information into rumors and non-rumors. Based on our algorithm, NICE system achieves satisfactory performance on evaluating information credibility and detecting rumors on social media.

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AAAI Conference 2016 Conference Paper

Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts

  • Qiang Liu
  • Shu Wu
  • Liang Wang
  • Tieniu Tan

Spatial and temporal contextual information plays a key role for analyzing user behaviors, and is helpful for predicting where he or she will go next. With the growing ability of collecting information, more and more temporal and spatial contextual information is collected in systems, and the location prediction problem becomes crucial and feasible. Some works have been proposed to address this problem, but they all have their limitations. Factorizing Personalized Markov Chain (FPMC) is constructed based on a strong independence assumption among different factors, which limits its performance. Tensor Factorization (TF) faces the cold start problem in predicting future actions. Recurrent Neural Networks (RNN) model shows promising performance comparing with PFMC and TF, but all these methods have problem in modeling continuous time interval and geographical distance. In this paper, we extend RNN and propose a novel method called Spatial Temporal Recurrent Neural Networks (ST-RNN). ST-RNN can model local temporal and spatial contexts in each layer with time-specific transition matrices for different time intervals and distance-specific transition matrices for different geographical distances. Experimental results show that the proposed ST-RNN model yields significant improvements over the competitive compared methods on two typical datasets, i. e. , Global Terrorism Database (GTD) and Gowalla dataset.

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AAAI Conference 2016 Conference Paper

SAPE: A System for Situation-Aware Public Security Evaluation

  • Shu Wu
  • Qiang Liu
  • Ping Bai
  • Liang Wang
  • Tieniu Tan

Public security events are occurring all over the world, bringing threat to personal and property safety, and homeland security. It is vital to construct an effective model to evaluate and predict the public security. In this work, we establish a Situation-Aware Public Security Evaluation (SAPE) platform. Based on conventional Recurrent Neural Networks (RNN), we develop a new variant for temporal contexts in public security event datasets. This model can achieve better performance than the compared state-of-the-art methods. SAPE has two demonstrations, i. e. , global public security evaluation and China public security evaluation. In the global part, based on Global Terrorism Database from UMD, for each country, SAPE can predict risk level and top-n potential terrorist organizations which might attack the country. Users can also view the actual attacking organizations and predicted results. For each province in China, SAPE can predict the risk level and the probability scores of different types of events in the next month. Users can also view the actual numbers of events and predicted risk levels of the past one year.

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AAAI Conference 2016 Conference Paper

Simultaneous Feature and Sample Reduction for Image-Set Classification

  • Man Zhang
  • Ran He
  • Dong Cao
  • Zhenan Sun
  • Tieniu Tan

Image-set classification is the assignment of a label to a given image set. In real-life scenarios such as surveillance videos, each image set often contains much redundancy in terms of features and samples. This paper introduces a joint learning method for image-set classification that simultaneously learns compact binary codes and removes redundant samples. The joint objective function of our model mainly includes two parts. The first part seeks a hashing function to generate binary codes that have larger inter-class and smaller intra-class distances. The second one reduces redundant samples with discrete constraints in a low-rank way. A kernel method based on anchor points is further used to reduce sample variations. The proposed discrete objective function is simplified to a series of sub-problems that admit an analytical solution, resulting in a high-quality discrete solution with a low computational cost. Experiments on three commonly used image-set datasets show that the proposed method for the tasks of face recognition from image sets is efficient and effective.

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NeurIPS Conference 2013 Conference Paper

Relevance Topic Model for Unstructured Social Group Activity Recognition

  • Fang Zhao
  • Yongzhen Huang
  • Liang Wang
  • Tieniu Tan

Unstructured social group activity recognition in web videos is a challenging task due to 1) the semantic gap between class labels and low-level visual features and 2) the lack of labeled training data. To tackle this problem, we propose a relevance topic model" for jointly learning meaningful mid-level representations upon bag-of-words (BoW) video representations and a classifier with sparse weights. In our approach, sparse Bayesian learning is incorporated into an undirected topic model (i. e. , Replicated Softmax) to discover topics which are relevant to video classes and suitable for prediction. Rectified linear units are utilized to increase the expressive power of topics so as to explain better video data containing complex contents and make variational inference tractable for the proposed model. An efficient variational EM algorithm is presented for model parameter estimation and inference. Experimental results on the Unstructured Social Activity Attribute dataset show that our model achieves state of the art performance and outperforms other supervised topic model in terms of classification accuracy, particularly in the case of a very small number of labeled training videos. "

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ICRA Conference 2003 Conference Paper

Mobile robot self-localization based on global visual appearance features

  • Chao Zhou
  • Yucheng Wei
  • Tieniu Tan

The paper presents a novel method for mobile robot localization using visual appearance features. A multidimensional histogram is used to describe the global appearance features of an image such as colors, edge density, gradient magnitude, textures and so on. The matching of histograms determines the location of the robot. The method has been evaluated in an indoor environment, and the system correctly determines the location of 82. 9% of the input scene images.

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