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Jungong Han

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53 papers
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53

AAAI Conference 2026 Conference Paper

Tracking and Segmenting Anything in Any Modality

  • Tianlu Zhang
  • Qiang Zhang
  • Guiguang Ding
  • Jungong Han

Tracking and segmentation play essential roles in video understanding, providing basic positional information and temporal association of objects within video sequences. Despite their shared objective, existing approaches often tackle these tasks using specialized architectures or modality-specific parameters, limiting their generalization and scalability. Recent efforts have attempted to unify multiple tracking and segmentation sub-tasks from the perspectives of any modality input or multi-task inference. However, these approaches tend to overlook two critical challenges: the distributional gap across different modalities and the feature representation gap across tasks. These issues hinder effective cross-task and cross-modal knowledge sharing, ultimately constraining the development of a true generalist model. To address these limitations, we propose a universal tracking and segmentation framework named SATA, which unifies a broad spectrum of tracking and segmentation subtasks with any modality input. Specifically, a Decoupled Mixture-of-Expert (DeMoE) mechanism is presented to decouple the unified representation learning task into the modeling process of cross-modal shared knowledge and specific information, thus enabling the model to maintain flexibility while enhancing generalization. Additionally, we introduce a Task-aware Multi-object Tracking (TaMOT) pipeline to unify all the task outputs as a unified set of instances with calibrated ID information, thereby alleviating the degradation of task-specific knowledge during multi-task training. SATA demonstrates superior performance on 18 challenging tracking and segmentation benchmarks, offering a novel perspective for more generalizable video understanding.

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NeurIPS Conference 2025 Conference Paper

PrefixKV: Adaptive Prefix KV Cache is What Vision Instruction-Following Models Need for Efficient Generation

  • Ao Wang
  • Hui Chen
  • Jianchao Tan
  • Kefeng Zhang
  • Xunliang Cai
  • Zijia Lin
  • Jungong Han
  • Guiguang Ding

Recently, large vision-language models (LVLMs) have rapidly gained popularity for their strong generation and reasoning capabilities given diverse multimodal inputs. However, these models incur significant computational and memory overhead during inference, which greatly hinders the efficient deployment in practical scenarios. The extensive key-value (KV) cache, necessitated by the lengthy input and output sequences, notably contributes to the high inference cost. Based on this, recent works have investigated ways to reduce the KV cache size for higher efficiency. Although effective, they generally overlook the distinct importance distributions of KV vectors across layers and maintain the same cache size for each layer during the next token prediction. This results in the significant contextual information loss for certain layers, leading to notable performance decline. To address this, we present PrefixKV. It reframes the challenge of determining KV cache sizes for all layers into the task of searching for the optimal global prefix configuration. With an adaptive layer-wise KV retention recipe based on binary search, the maximum contextual information can thus be preserved in each layer, facilitating the generation. Extensive experiments demonstrate that our method achieves the state-of-the-art performance compared with others. It exhibits superior inference efficiency and generation quality trade-offs, showing promising potential for practical applications. Code is available at https: //github. com/THU-MIG/PrefixKV.

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AAAI Conference 2025 Conference Paper

Promptable Anomaly Segmentation with SAM Through Self-Perception Tuning

  • Hui-Yue Yang
  • Hui Chen
  • Ao Wang
  • Kai Chen
  • Zijia Lin
  • Yongliang Tang
  • Pengcheng Gao
  • Yuming Quan

Segment Anything Model (SAM) has made great progress in anomaly segmentation tasks due to its impressive generalization ability. However, existing methods that directly apply SAM through prompting often overlook the domain shift issue, where SAM performs well on natural images but struggles in industrial scenarios. Parameter-Efficient Fine-Tuning (PEFT) offers a promising solution, but it may yield suboptimal performance by not adequately addressing the perception challenges during adaptation to anomaly images. In this paper, we propose a novel Self-Perception Tuning (SPT) method, aiming to enhance SAM's perception capability for anomaly segmentation. The SPT method incorporates a self-drafting tuning strategy, which generates an initial coarse draft of the anomaly mask, followed by a refinement process. Additionally, a visual-relation-aware adapter is introduced to improve the perception of discriminative relational information for mask generation. Extensive experimental results on several benchmark datasets demonstrate that our SPT method can significantly outperform baseline methods, validating its effectiveness.

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ICML Conference 2025 Conference Paper

Rethinking Score Distilling Sampling for 3D Editing and Generation

  • Xingyu Miao
  • Haoran Duan 0001
  • Yang Long 0001
  • Jungong Han

Score Distillation Sampling (SDS) has emerged as a prominent method for text-to-3D generation by leveraging the strengths of 2D diffusion models. However, SDS is limited to generation tasks and lacks the capability to edit existing 3D assets. Conversely, variants of SDS that introduce editing capabilities often can not generate new 3D assets effectively. In this work, we observe that the processes of generation and editing within SDS and its variants have unified underlying gradient terms. Building on this insight, we propose Unified Distillation Sampling (UDS), a method that seamlessly integrates both the generation and editing of 3D assets. Essentially, UDS refines the gradient terms used in vanilla SDS methods, unifying them to support both tasks. Extensive experiments demonstrate that UDS not only outperforms baseline methods in generating 3D assets with richer details but also excels in editing tasks, thereby bridging the gap between 3D generation and editing.

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AAAI Conference 2025 Conference Paper

Scaffold-BPE: Enhancing Byte Pair Encoding for Large Language Models with Simple and Effective Scaffold Token Removal

  • Haoran Lian
  • Yizhe Xiong
  • Jianwei Niu
  • Shasha Mo
  • Zhenpeng Su
  • Zijia Lin
  • Hui Chen
  • Jungong Han

Byte Pair Encoding (BPE) serves as a foundation method for text tokenization in the Natural Language Processing (NLP) field. Despite its wide adoption, the original BPE algorithm harbors an inherent flaw: it inadvertently introduces a frequency imbalance for tokens in the text corpus. Since BPE iteratively merges the most frequent token pair in the text corpus to generate a new token and keeps all generated tokens in the vocabulary, it unavoidably holds tokens that primarily act as components of a longer token and appear infrequently on their own. We term such tokens as Scaffold Tokens. Due to their infrequent occurrences in the text corpus, Scaffold Tokens pose a learning imbalance issue. To address that issue, we propose Scaffold-BPE, which incorporates a dynamic scaffold token removal mechanism by parameter-free, computation-light, and easy-to-implement modifications to the original BPE method. This novel approach ensures the exclusion of low-frequency Scaffold Tokens from the token representations for given texts, thereby mitigating the issue of frequency imbalance and facilitating model training. On extensive experiments across language modeling and even machine translation, Scaffold-BPE consistently outperforms the original BPE, well demonstrating its effectiveness.

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AAAI Conference 2025 Conference Paper

Sequential Joint Dependency Aware Human Pose Estimation with State Space Model

  • Hanxi Yin
  • Shaodi You
  • Jungong Han
  • Zhixiang Chen

In this paper, we present a sequential joint dependency aware model for monocular 2D-to-3D human pose estimation. While existing estimators leverage the (bi)directional joint dependency with graph convolutions and attention, we further propose to exploit the sequential dependency between joints with state space model (SSM). Our sequential dependency takes into consideration the information of kinematic chain, joint hierarchy and the body part. We design a sequential dependency aware representation to transform the pose data into sequential data for our pose SSM module. We tailor the SSM layer in the pose SSM module for pose estimation by learning joint-dependent parameters and introducing pose aware hidden state initialization. Extensive experiments are conducted on two datasets to validate the effectiveness of our proposed SSM module, and the results demonstrate that our pose estimator can deliver impressive performance.

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JBHI Journal 2025 Journal Article

Towards High-Quality MRI Reconstruction With Anisotropic Diffusion-Assisted Generative Adversarial Networks and Its Multi-Modal Images Extension

  • Yuyang Luo
  • Gengshen Wu
  • Yi Liu
  • Wenjian Liu
  • Jungong Han

Recently, fast Magnetic Resonance Imaging reconstruction technology has emerged as a promising way to improve the clinical diagnostic experience by significantly reducing scan times. While existing studies have used Generative Adversarial Networks to achieve impressive results in reconstructing MR images, they still suffer from challenges such as blurred zones/boundaries and abnormal spots caused by inevitable noise in the reconstruction process. To this end, we propose a novel deep framework termed Anisotropic Diffusion-Assisted Generative Adversarial Networks, which aims to maximally preserve valid high-frequency information and structural details while minimizing noises in reconstructed images by optimizing a joint loss function in a unified framework. In doing so, it enables more authentic and accurate MR image generation. To specifically handle unforeseeable noises, an Anisotropic Diffused Reconstruction Module is developed and added aside the backbone network as a denoise assistant, which improves the final image quality by minimizing reconstruction losses between targets and iteratively denoised generative outputs with no extra computational complexity during the testing phase. To make the most of valuable MRI data, we extend its application to support multi-modal learning to boost reconstructed image quality by aggregating more valid information from images of diverse modalities. Extensive experiments on public datasets show that the proposed framework can achieve superior performance in polishing up the quality of reconstructed MR images. For example, the proposed method obtains average PSNR and mSSIM values of 35. 785 dB and 0. 9765 on the MRNet dataset, which are at least about 2. 9 dB and 0. 07 higher than those from the baselines.

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ECAI Conference 2024 Conference Paper

Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly Detection

  • Hui-Yue Yang
  • Hui Chen 0013
  • Lihao Liu
  • Zijia Lin
  • Kai Chen
  • Liejun Wang
  • Jungong Han
  • Guiguang Ding

Unsupervised anomaly detection (AD) aims to train robust detection models using only normal samples, while can generalize well to unseen anomalies. Recent research focuses on a unified unsupervised AD setting in which only one model is trained for all classes, i. e. , n-class-one-model paradigm. Feature-reconstruction-based methods achieve state-of-the-art performance in this scenario. However, existing methods often suffer from a lack of sufficient contextual awareness, thereby compromising the quality of the reconstruction. To address this issue, we introduce a novel Reconstruction as Sequence (RAS) method, which enhances the contextual correspondence during feature reconstruction from a sequence modeling perspective. In particular, based on the transformer technique, we integrate a specialized RASFormer block into RAS. This block enables the capture of spatial relationships among different image regions and enhances sequential dependencies throughout the reconstruction process. By incorporating the RASFormer block, our RAS method achieves superior contextual awareness capabilities, leading to remarkable performance. Experimental results show that our RAS significantly outperforms competing methods, well demonstrating the effectiveness and superiority of our method. Our code is available at https: //github. com/Nothingtolose9979/RAS

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NeurIPS Conference 2024 Conference Paper

FedGMKD: An Efficient Prototype Federated Learning Framework through Knowledge Distillation and Discrepancy-Aware Aggregation

  • Jianqiao Zhang
  • Caifeng Shan
  • Jungong Han

Federated Learning (FL) faces significant challenges due to data heterogeneity across distributed clients. To address this, we propose FedGMKD, a novel framework that combines knowledge distillation and differential aggregation for efficient prototype-based personalized FL without the need for public datasets or server-side generative models. FedGMKD introduces Cluster Knowledge Fusion, utilizing Gaussian Mixture Models to generate prototype features and soft predictions on the client side, enabling effective knowledge distillation while preserving data privacy. Additionally, we implement a Discrepancy-Aware Aggregation Technique that weights client contributions based on data quality and quantity, enhancing the global model's generalization across diverse client distributions. Theoretical analysis confirms the convergence of FedGMKD. Extensive experiments on benchmark datasets, including SVHN, CIFAR-10, and CIFAR-100, demonstrate that FedGMKD outperforms state-of-the-art methods, significantly improving both local and global accuracy in non-IID data settings.

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NeurIPS Conference 2024 Conference Paper

Revisiting motion information for RGB-Event tracking with MOT philosophy

  • Tianlu Zhang
  • Kurt Debattista
  • Qiang Zhang
  • Guiguang Ding
  • Jungong Han

RGB-Event single object tracking (SOT) aims to leverage the merits of RGB and event data to achieve higher performance. However, existing frameworks focus on exploring complementary appearance information within multi-modal data, and struggle to address the association problem of targets and distractors in the temporal domain using motion information from the event stream. In this paper, we introduce the Multi-Object Tracking (MOT) philosophy into RGB-E SOT to keep track of targets as well as distractors by using both RGB and event data, thereby improving the robustness of the tracker. Specifically, an appearance model is employed to predict the initial candidates. Subsequently, the initially predicted tracking results, in combination with the RGB-E features, are encoded into appearance and motion embeddings, respectively. Furthermore, a Spatial-Temporal Transformer Encoder is proposed to model the spatial-temporal relationships and learn discriminative features for each candidate through guidance of the appearance-motion embeddings. Simultaneously, a Dual-Branch Transformer Decoder is designed to adopt such motion and appearance information for candidate matching, thus distinguishing between targets and distractors. The proposed method is evaluated on multiple benchmark datasets and achieves state-of-the-art performance on all the datasets tested.

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IJCAI Conference 2024 Conference Paper

TaD: A Plug-and-Play Task-Aware Decoding Method to Better Adapt LLMs on Downstream Tasks

  • Xinhao Xu
  • Hui Chen
  • Zijia Lin
  • Jungong Han
  • Lixing Gong
  • Guoxin Wang
  • Yongjun Bao
  • Guiguang Ding

Fine-tuning pre-trained models on downstream tasks is a common practice in leveraging large language models (LLMs) today. A critical issue is how to adapt pre-trained models to downstream tasks better, thereby enhancing their performance. This paper introduces Task-aware Decoding (TaD), a plug-and-play method that exploits the difference in probability distributions before and after fine-tuning to boost the performance of LLMs on downstream tasks. The proposed TaD argues that the difference between the pre-finetuning probability distribution and the post-finetuning one represents the direction from common knowledge towards specific downstream-task knowledge. Aligning the final output probability distribution to that direction can probably result in superior downstream task performance, compared to the original fine-tuned model. Experiments on various datasets across four different task categories well demonstrate TaD's effectiveness on different LLMs, i. e. , GPT, BLOOM, and LLaMA, with different fine-tuning methods. Moreover, further experiments reveal that TaD better enhances model performance in data-scarce scenarios.

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NeurIPS Conference 2024 Conference Paper

YOLOv10: Real-Time End-to-End Object Detection

  • Ao Wang
  • Hui Chen
  • Lihao Liu
  • Kai Chen
  • Zijia Lin
  • Jungong Han
  • Guiguang Ding

Over the past years, YOLOs have emerged as the predominant paradigm in the field of real-time object detection owing to their effective balance between computational cost and detection performance. Researchers have explored the architectural designs, optimization objectives, data augmentation strategies, and others for YOLOs, achieving notable progress. However, the reliance on the non-maximum suppression (NMS) for post-processing hampers the end-to-end deployment of YOLOs and adversely impacts the inference latency. Besides, the design of various components in YOLOs lacks the comprehensive and thorough inspection, resulting in noticeable computational redundancy and limiting the model's capability. It renders the suboptimal efficiency, along with considerable potential for performance improvements. In this work, we aim to further advance the performance-efficiency boundary of YOLOs from both the post-processing and the model architecture. To this end, we first present the consistent dual assignments for NMS-free training of YOLOs, which brings the competitive performance and low inference latency simultaneously. Moreover, we introduce the holistic efficiency-accuracy driven model design strategy for YOLOs. We comprehensively optimize various components of YOLOs from both the efficiency and accuracy perspectives, which greatly reduces the computational overhead and enhances the capability. The outcome of our effort is a new generation of YOLO series for real-time end-to-end object detection, dubbed YOLOv10. Extensive experiments show that YOLOv10 achieves the state-of-the-art performance and efficiency across various model scales. For example, our YOLOv10-S is 1. 8$\times$ faster than RT-DETR-R18 under the similar AP on COCO, meanwhile enjoying 2. 8$\times$ smaller number of parameters and FLOPs. Compared with YOLOv9-C, YOLOv10-B has 46\% less latency and 25\% fewer parameters for the same performance. Code and models are available at https: //github. com/THU-MIG/yolov10.

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ICLR Conference 2023 Conference Paper

Re-parameterizing Your Optimizers rather than Architectures

  • Xiaohan Ding
  • Honghao Chen
  • Xiangyu Zhang 0005
  • Kaiqi Huang
  • Jungong Han
  • Guiguang Ding

The well-designed structures in neural networks reflect the prior knowledge incorporated into the models. However, though different models have various priors, we are used to training them with model-agnostic optimizers such as SGD. In this paper, we propose to incorporate model-specific prior knowledge into optimizers by modifying the gradients according to a set of model-specific hyper-parameters. Such a methodology is referred to as Gradient Re-parameterization, and the optimizers are named RepOptimizers. For the extreme simplicity of model structure, we focus on a VGG-style plain model and showcase that such a simple model trained with a RepOptimizer, which is referred to as RepOpt-VGG, performs on par with or better than the recent well-designed models. From a practical perspective, RepOpt-VGG is a favorable base model because of its simple structure, high inference speed and training efficiency. Compared to Structural Re-parameterization, which adds priors into models via constructing extra training-time structures, RepOptimizers require no extra forward/backward computations and solve the problem of quantization. We hope to spark further research beyond the realms of model structure design. Code and models https://github.com/DingXiaoH/RepOptimizers.

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JBHI Journal 2022 Journal Article

Guest Editorial Artificial Intelligence in Pre-DICOM

  • Tao Tan
  • Ravi Soni
  • Jungong Han
  • Shuo Li

The papers in this special section focus on artificial intelligence pre-DICOM medical imaging. AI for medical imaging is applied in three domains: pre-DICOM, pre-processing and clinical applications. Clinical applications mainly cover topics such as disease detection, classification, segmentation, registration. Pre-processing components are mainly designed for facilitating applications using image transformation such as image normalization, noise reduction, bias correction in MR. AI in the pre-DICOM domain is expected to improve imaging workflow, image protocol selection, imaging quality, imaging scanning time before images are converted into DICOM format for radiologists to review. The trends of AI publications in medical imaging have been gradually extended from clinical applications to pre-processing and, to pre-DICOM. The papers in this special section seek to present and highlight the latest development on applying advanced deep learning techniques in pre-DICOM space. The papers highlight the latest development on applying advanced deep learning techniques in pre-DICOM space.

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NeurIPS Conference 2022 Conference Paper

Physically-Based Face Rendering for NIR-VIS Face Recognition

  • Yunqi Miao
  • Alexandros Lattas
  • Jiankang Deng
  • Jungong Han
  • Stefanos Zafeiriou

Near infrared (NIR) to Visible (VIS) face matching is challenging due to the significant domain gaps as well as a lack of sufficient data for cross-modality model training. To overcome this problem, we propose a novel method for paired NIR-VIS facial image generation. Specifically, we reconstruct 3D face shape and reflectance from a large 2D facial dataset and introduce a novel method of transforming the VIS reflectance to NIR reflectance. We then use a physically-based renderer to generate a vast, high-resolution and photorealistic dataset consisting of various poses and identities in the NIR and VIS spectra. Moreover, to facilitate the identity feature learning, we propose an IDentity-based Maximum Mean Discrepancy (ID-MMD) loss, which not only reduces the modality gap between NIR and VIS images at the domain level but encourages the network to focus on the identity features instead of facial details, such as poses and accessories. Extensive experiments conducted on four challenging NIR-VIS face recognition benchmarks demonstrate that the proposed method can achieve comparable performance with the state-of-the-art (SOTA) methods without requiring any existing NIR-VIS face recognition datasets. With slightly fine-tuning on the target NIR-VIS face recognition datasets, our method can significantly surpass the SOTA performance. Code and pretrained models are released under the insightface GitHub.

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AAAI Conference 2022 Conference Paper

ReMoNet: Recurrent Multi-Output Network for Efficient Video Denoising

  • Liuyu Xiang
  • Jundong Zhou
  • Jirui Liu
  • Zerun Wang
  • Haidong Huang
  • Jie Hu
  • Jungong Han
  • Yuchen Guo

While deep neural network-based video denoising methods have achieved promising results, it is still hard to deploy them on mobile devices due to their high computational cost and memory demands. This paper aims to develop a lightweight deep video denoising method that is friendly to resource-constrained mobile devices. Inspired by the facts that 1) consecutive video frames usually contain redundant temporal coherency, and 2) neural networks are usually over-parameterized, we propose a multi-input multi-output (MIMO) paradigm to process consecutive video frames within one-forward-pass. The basic idea is concretized to a novel architecture termed Recurrent Multi-output Network (ReMoNet), which consists of recurrent temporal fusion and temporal aggregation blocks and is further reinforced by similarity-based mutual distillation. We conduct extensive experiments on NVIDIA GPU and Qualcomm Snapdragon 888 mobile platform with Gaussian noise and simulated Image- Signal-Processor (ISP) noise. The experimental results show that ReMoNet is both effective and efficient on video denoising. Moreover, we show that ReMoNet is more robust under higher noise level scenarios.

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NeurIPS Conference 2020 Conference Paper

Few-Cost Salient Object Detection with Adversarial-Paced Learning

  • Dingwen Zhang
  • HaiBin Tian
  • Jungong Han

Detecting and segmenting salient objects from given image scenes has received great attention in recent years. A fundamental challenge in training the existing deep saliency detection models is the requirement of large amounts of annotated data. While gathering large quantities of training data becomes cheap and easy, annotating the data is an expensive process in terms of time, labor and human expertise. To address this problem, this paper proposes to learn the effective salient object detection model based on the manual annotation on a few training images only, thus dramatically alleviating human labor in training models. To this end, we name this new task as the few-cost salient object detection and propose an adversarial-paced learning (APL)-based framework to facilitate the few-cost learning scenario. Essentially, APL is derived from the self-paced learning (SPL) regime but it infers the robust learning pace through the data-driven adversarial learning mechanism rather than the heuristic design of the learning regularizer. Comprehensive experiments on four widely-used benchmark datasets have demonstrated that the proposed approach can effectively approach to the existing supervised deep salient object detection models with only 1k human-annotated training images.

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AAAI Conference 2020 Conference Paper

Heterogeneous Transfer Learning with Weighted Instance-Correspondence Data

  • Yuwei He
  • Xiaoming Jin
  • Guiguang Ding
  • Yuchen Guo
  • Jungong Han
  • Jiyong Zhang
  • Sicheng Zhao

Instance-correspondence (IC) data are potent resources for heterogeneous transfer learning (HeTL) due to the capability of bridging the source and the target domains at the instancelevel. To this end, people tend to use machine-generated IC data, because manually establishing IC data is expensive and primitive. However, existing IC data machine generators are not perfect and always produce the data that are not of high quality, thus hampering the performance of domain adaption. In this paper, instead of improving the IC data generator, which might not be an optimal way, we accept the fact that data quality variation does exist but find a better way to use the data. Specifically, we propose a novel heterogeneous transfer learning method named Transfer Learning with Weighted Correspondence (TLWC), which utilizes IC data to adapt the source domain to the target domain. Rather than treating IC data equally, TLWC can assign solid weights to each IC data pair depending on the quality of the data. We conduct extensive experiments on HeTL datasets and the state-of-the-art results verify the effectiveness of TLWC.

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AAAI Conference 2020 Conference Paper

Shallow Feature Based Dense Attention Network for Crowd Counting

  • Yunqi Miao
  • Zijia Lin
  • Guiguang Ding
  • Jungong Han

While the performance of crowd counting via deep learning has been improved dramatically in the recent years, it remains an ingrained problem due to cluttered backgrounds and varying scales of people within an image. In this paper, we propose a Shallow feature based Dense Attention Network (SDANet) for crowd counting from still images, which diminishes the impact of backgrounds via involving a shallow feature based attention model, and meanwhile, captures multiscale information via densely connecting hierarchical image features. Specifically, inspired by the observation that backgrounds and human crowds generally have noticeably different responses in shallow features, we decide to build our attention model upon shallow-feature maps, which results in accurate background-pixel detection. Moreover, considering that the most representative features of people across different scales can appear in different layers of a feature extraction network, to better keep them all, we propose to densely connect hierarchical image features of different layers and subsequently encode them for estimating crowd density. Experimental results on three benchmark datasets clearly demonstrate the superiority of SDANet when dealing with different scenarios. Particularly, on the challenging UCF CC 50 dataset, our method outperforms other existing methods by a large margin, as is evident from a remarkable 11. 9% Mean Absolute Error (MAE) drop of our SDANet.

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ICML Conference 2019 Conference Paper

Approximated Oracle Filter Pruning for Destructive CNN Width Optimization

  • Xiaohan Ding
  • Guiguang Ding
  • Yuchen Guo
  • Jungong Han
  • Chenggang Yan 0001

It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the optimal number of filters (i. e. , the width) at each layer is tricky, given an architecture; and 2) the computational intensity of CNNs impedes the deployment on computationally limited devices. Oracle Pruning is designed to remove the unimportant filters from a well-trained CNN, which estimates the filters’ importance by ablating them in turn and evaluating the model, thus delivers high accuracy but suffers from intolerable time complexity, and requires a given resulting width but cannot automatically find it. To address these problems, we propose Approximated Oracle Filter Pruning (AOFP), which keeps searching for the least important filters in a binary search manner, makes pruning attempts by masking out filters randomly, accumulates the resulting errors, and finetunes the model via a multi-path framework. As AOFP enables simultaneous pruning on multiple layers, we can prune an existing very deep CNN with acceptable time cost, negligible accuracy drop, and no heuristic knowledge, or re-design a model which exerts higher accuracy and faster inference.

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AAAI Conference 2019 Conference Paper

Attentive Temporal Pyramid Network for Dynamic Scene Classification

  • Yuanjun Huang
  • Xianbin Cao
  • Xiantong Zhen
  • Jungong Han

Dynamic scene classification is an important yet challenging problem especially with the presence of defected or irrelevant frames due to unconstrained imaging conditions such as illumination, camera motion and irrelevant background. In this paper, we propose the attentive temporal pyramid network (ATP-Net) to establish effective representations of dynamic scenes by extracting and aggregating the most informative and discriminative features. The proposed ATP-Net detects informative features of frames that contain the most relevant information to scenes by a temporal pyramid structure with the incorporated attention mechanism. These frame features are effectively fused by a newly designed kernel aggregation layer based on kernel approximation into a discriminative holistic representations of dynamic scenes. The proposed ATP-Net leverages the strength of attention mechanism to select the most relevant frame features and the ability of kernels to achieve optimal feature fusion for discriminative representations of dynamic scenes. Extensive experiments and comparisons are conducted on three benchmark datasets and the results show our superiority over the state-of-the-art methods on all these three benchmark datasets.

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AAAI Conference 2019 Conference Paper

Dual-View Ranking with Hardness Assessment for Zero-Shot Learning

  • Yuchen Guo
  • Guiguang Ding
  • Jungong Han
  • Xiaohan Ding
  • Sicheng Zhao
  • Zheng Wang
  • Chenggang Yan
  • Qionghai Dai

Zero-shot learning (ZSL) is to build recognition models for previously unseen target classes which have no labeled data for training by transferring knowledge from some other related auxiliary source classes with abundant labeled samples to the target ones with class attributes as the bridge. The key is to learn a similarity based ranking function between samples and class labels using the labeled source classes so that the proper (unseen) class label for a test sample can be identified by the function. In order to learn the function, single-view ranking based loss is widely used which aims to rank the true label prior to the other labels for a training sample. However, we argue that the ranking can be performed from the other view, which aims to place the images belonging to a label before the images from the other classes. Motivated by it, we propose a novel DuAl-view RanKing (DARK) loss for zeroshot learning simultaneously ranking labels for an image by point-to-point metric and ranking images for a label by pointto-set metric, which is capable of better modeling the relationship between images and classes. In addition, we also notice that previous ZSL approaches mostly fail to well exploit the hardness of training samples, either using only very hard ones or using all samples indiscriminately. In this work, we also introduce a sample hardness assessment method to ZSL which assigns different weights to training samples based on their hardness, which leads to a more accurate and robust ZSL model. Experiments on benchmarks demonstrate that DARK outperforms the state-of-the-arts for (generalized) ZSL.

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NeurIPS Conference 2019 Conference Paper

Global Sparse Momentum SGD for Pruning Very Deep Neural Networks

  • Xiaohan Ding
  • Guiguang Ding
  • Xiangxin Zhou
  • Yuchen Guo
  • Jungong Han
  • Ji Liu

Deep Neural Network (DNN) is powerful but computationally expensive and memory intensive, thus impeding its practical usage on resource-constrained front-end devices. DNN pruning is an approach for deep model compression, which aims at eliminating some parameters with tolerable performance degradation. In this paper, we propose a novel momentum-SGD-based optimization method to reduce the network complexity by on-the-fly pruning. Concretely, given a global compression ratio, we categorize all the parameters into two parts at each training iteration which are updated using different rules. In this way, we gradually zero out the redundant parameters, as we update them using only the ordinary weight decay but no gradients derived from the objective function. As a departure from prior methods that require heavy human works to tune the layer-wise sparsity ratios, prune by solving complicated non-differentiable problems or finetune the model after pruning, our method is characterized by 1) global compression that automatically finds the appropriate per-layer sparsity ratios; 2) end-to-end training; 3) no need for a time-consuming re-training process after pruning; and 4) superior capability to find better winning tickets which have won the initialization lottery.

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IJCAI Conference 2019 Conference Paper

Graph and Autoencoder Based Feature Extraction for Zero-shot Learning

  • Yang Liu
  • Deyan Xie
  • Quanxue Gao
  • Jungong Han
  • Shujian Wang
  • Xinbo Gao

Zero-shot learning (ZSL) aims to build models to recognize novel visual categories that have no associated labelled training samples. The basic framework is to transfer knowledge from seen classes to unseen classes by learning the visual-semantic embedding. However, most of approaches do not preserve the underlying sub-manifold of samples in the embedding space. In addition, whether the mapping can precisely reconstruct the original visual feature is not investigated in-depth. In order to solve these problems, we formulate a novel framework named Graph and Autoencoder Based Feature Extraction (GAFE) to seek a low-rank mapping to preserve the sub-manifold of samples. Taking the encoder-decoder paradigm, the encoder part learns a mapping from the visual feature to the semantic space, while decoder part reconstructs the original features with the learned mapping. In addition, a graph is constructed to guarantee the learned mapping can preserve the local intrinsic structure of the data. To this end, an L21 norm sparsity constraint is imposed on the mapping to identify features relevant to the target domain. Extensive experiments on five attribute datasets demonstrate the effectiveness of the proposed model.

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IJCAI Conference 2019 Conference Paper

Incremental Few-Shot Learning for Pedestrian Attribute Recognition

  • Liuyu Xiang
  • Xiaoming Jin
  • Guiguang Ding
  • Jungong Han
  • Leida Li

Pedestrian attribute recognition has received increasing attention due to its important role in video surveillance applications. However, most existing methods are designed for a fixed set of attributes. They are unable to handle the incremental few-shot learning scenario, i. e. adapting a well-trained model to newly added attributes with scarce data, which commonly exists in the real world. In this work, we present a meta learning based method to address this issue. The core of our framework is a meta architecture capable of disentangling multiple attribute information and generalizing rapidly to new coming attributes. By conducting extensive experiments on the benchmark dataset PETA and RAP under the incremental few-shot setting, we show that our method is able to perform the task with competitive performances and low resource requirements.

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IJCAI Conference 2019 Conference Paper

Landmark Selection for Zero-shot Learning

  • Yuchen Guo
  • Guiguang Ding
  • Jungong Han
  • Chenggang Yan
  • Jiyong Zhang
  • Qionghai Dai

Zero-shot learning (ZSL) is an emerging research topic whose goal is to build recognition models for previously unseen classes. The basic idea of ZSL is based on heterogeneous feature matching which learns a compatibility function between image and class features using seen classes. The function is constructed based on one-vs-all training in which each class has only one class feature and many image features. Existing ZSL works mostly treat all image features equivalently. However, in this paper we argue that it is more reasonable to use some representative cross-domain data instead of all. Motivated by this idea, we propose a novel approach, termed as Landmark Selection(LAST) for ZSL. LAST is able to identify representative cross-domain features which further lead to better image-class compatibility function. Experiments on several ZSL datasets including ImageNet demonstrate the superiority of LAST to the state-of-the-arts.

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AAAI Conference 2019 Conference Paper

Learning Object Context for Dense Captioning

  • Xiangyang Li
  • Shuqiang Jiang
  • Jungong Han

Dense captioning is a challenging task which not only detects visual elements in images but also generates natural language sentences to describe them. Previous approaches do not leverage object information in images for this task. However, objects provide valuable cues to help predict the locations of caption regions as caption regions often highly overlap with objects (i. e. caption regions are usually parts of objects or combinations of them). Meanwhile, objects also provide important information for describing a target caption region as the corresponding description not only depicts its properties, but also involves its interactions with objects in the image. In this work, we propose a novel scheme with an object context encoding Long Short-Term Memory (LSTM) network to automatically learn complementary object context for each caption region, transferring knowledge from objects to caption regions. All contextual objects are arranged as a sequence and progressively fed into the context encoding module to obtain context features. Then both the learned object context features and region features are used to predict the bounding box offsets and generate the descriptions. The context learning procedure is in conjunction with the optimization of both location prediction and caption generation, thus enabling the object context encoding LSTM to capture and aggregate useful object context. Experiments on benchmark datasets demonstrate the superiority of our proposed approach over the state-of-the-art methods.

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IJCAI Conference 2019 Conference Paper

Low Shot Box Correction for Weakly Supervised Object Detection

  • Tianxiang Pan
  • Bin Wang
  • Guiguang Ding
  • Jungong Han
  • Junhai Yong

Weakly supervised object detection (WSOD) has been widely studied but the accuracy of state-of-art methods remains far lower than strongly supervised methods. One major reason for this huge gap is the incomplete box detection problem which arises because most previous WSOD models are structured on classification networks and therefore tend to recognize the most discriminative parts instead of complete bounding boxes. To solve this problem, we define a low-shot weakly supervised object detection task and propose a novel low-shot box correction network to address it. The proposed task enables to train object detectors on a large data set all of which have image-level annotations, but only a small portion or few shots have box annotations. Given the low-shot box annotations, we use a novel box correction network to transfer the incomplete boxes into complete ones. Extensive empirical evidence shows that our proposed method yields state-of-art detection accuracy under various settings on the PASCAL VOC benchmark.

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AAAI Conference 2019 Conference Paper

Projection Convolutional Neural Networks for 1-bit CNNs via Discrete Back Propagation

  • Jiaxin Gu
  • Ce Li
  • Baochang Zhang
  • Jungong Han
  • Xianbin Cao
  • Jianzhuang Liu
  • David Doermann

The advancement of deep convolutional neural networks (DCNNs) has driven significant improvement in the accuracy of recognition systems for many computer vision tasks. However, their practical applications are often restricted in resource-constrained environments. In this paper, we introduce projection convolutional neural networks (PCNNs) with a discrete back propagation via projection (DBPP) to improve the performance of binarized neural networks (BNNs). The contributions of our paper include: 1) for the first time, the projection function is exploited to efficiently solve the discrete back propagation problem, which leads to a new highly compressed CNNs (termed PCNNs); 2) by exploiting multiple projections, we learn a set of diverse quantized kernels that compress the full-precision kernels in a more efficient way than those proposed previously; 3) PCNNs achieve the best classification performance compared to other state-ofthe-art BNNs on the ImageNet and CIFAR datasets.

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AAAI Conference 2019 Conference Paper

Recurrent Attention Model for Pedestrian Attribute Recognition

  • Xin Zhao
  • Liufang Sang
  • Guiguang Ding
  • Jungong Han
  • Na Di
  • Chenggang Yan

Pedestrian attribute recognition is to predict attribute labels of pedestrian from surveillance images, which is a very challenging task for computer vision due to poor imaging quality and small training dataset. It is observed that many semantic pedestrian attributes to be recognised tend to show spatial locality and semantic correlations by which they can be grouped while previous works mostly ignore this phenomenon. Inspired by Recurrent Neural Network (RNN)’s super capability of learning context correlations and Attention Model’s capability of highlighting the region of interest on feature map, this paper proposes end-to-end Recurrent Convolutional (RC) and Recurrent Attention (RA) models, which are complementary to each other. RC model mines the correlations among different attribute groups with convolutional LSTM unit, while RA model takes advantage of the intra-group spatial locality and inter-group attention correlation to improve the performance of pedestrian attribute recognition. Our RA method combines the Recurrent Learning and Attention Model to highlight the spatial position on feature map and mine the attention correlations among different attribute groups to obtain more precise attention. Extensive empirical evidence shows that our recurrent model frameworks achieve state-of-the-art results, based on pedestrian attribute datasets, i. e. standard PETA and RAP datasets.

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IJCAI Conference 2019 Conference Paper

Zero-shot Learning with Many Classes by High-rank Deep Embedding Networks

  • Yuchen Guo
  • Guiguang Ding
  • Jungong Han
  • Hang Shao
  • Xin Lou
  • Qionghai Dai

Zero-shot learning (ZSL) is a recently emerging research topic which aims to build classification models for unseen classes with knowledge from auxiliary seen classes. Though many ZSL works have shown promising results on small-scale datasets by utilizing a bilinear compatibility function, the ZSL performance on large-scale datasets with many classes (say, ImageNet) is still unsatisfactory. We argue that the bilinear compatibility function is a low-rank approximation of the true compatibility function such that it is not expressive enough especially when there are a large number of classes because of the rank limitation. To address this issue, we propose a novel approach, termed as High-rank Deep Embedding Networks (GREEN), for ZSL with many classes. In particular, we propose a feature-dependent mixture of softmaxes as the image-class compatibility function, which is a simple extension of the bilinear compatibility function, but yields much better results. It utilizes a mixture of non-linear transformations with feature-dependent latent variables to approximate the true function in a high-rank way, which makes GREEN more expressive. Experiments on several datasets including ImageNet demonstrate GREEN significantly outperforms the state-of-the-art approaches.

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AAAI Conference 2018 Conference Paper

Auto-Balanced Filter Pruning for Efficient Convolutional Neural Networks

  • Xiaohan Ding
  • Guiguang Ding
  • Jungong Han
  • Sheng Tang

In recent years considerable research efforts have been devoted to compression techniques of convolutional neural networks (CNNs). Many works so far have focused on CNN connection pruning methods which produce sparse parameter tensors in convolutional or fully-connected layers. It has been demonstrated in several studies that even simple methods can effectively eliminate connections of a CNN. However, since these methods make parameter tensors just sparser but no smaller, the compression may not transfer directly to acceleration without support from specially designed hardware. In this paper, we propose an iterative approach named Auto-balanced Filter Pruning, where we pre-train the network in an innovative auto-balanced way to transfer the representational capacity of its convolutional layers to a fraction of the filters, prune the redundant ones, then re-train it to restore the accuracy. In this way, a smaller version of the original network is learned and the floating-point operations (FLOPs) are reduced. By applying this method on several common CNNs, we show that a large portion of the filters can be discarded without obvious accuracy drop, leading to significant reduction of computational burdens. Concretely, we reduce the inference cost of LeNet-5 on MNIST, VGG-16 and ResNet-56 on CIFAR-10 by 95. 1%, 79. 7% and 60. 9%, respectively.

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AAAI Conference 2018 Conference Paper

Euler Sparse Representation for Image Classification

  • Yang Liu
  • Quanxue Gao
  • Jungong Han
  • Shujian Wang

Sparse representation based classification (SRC) has gained great success in image recognition. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which may help improve the separability and margin between nearby data points, we propose Euler SRC for image classification, which is essentially the SRC with Euler sparse representation. To be specific, it first maps the images into the complex space by Euler representation, which has a negligible effect for outliers and illumination, and then performs complex SRC with Euler representation. The major advantage of our method is that Euler representation is explicit with no increase of the image space dimensionality, thereby enabling this technique to be easily deployed in real applications. To solve Euler SRC, we present an efficient algorithm, which is fast and has good convergence. Extensive experimental results illustrate that Euler SRC outperforms traditional SRC and achieves better performance for image classification.

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IJCAI Conference 2018 Conference Paper

Implicit Non-linear Similarity Scoring for Recognizing Unseen Classes

  • Yuchen Guo
  • Guiguang Ding
  • Jungong Han
  • Sicheng Zhao
  • Bin Wang

Recognizing unseen classes is an important task for real-world applications, due to: 1) it is common that some classes in reality have no labeled image exemplar for training; and 2) novel classes emerge rapidly. Recently, to address this task many zero-shot learning (ZSL) approaches have been proposed where explicit linear scores, like inner product score, are employed to measure the similarity between a class and an image. We argue that explicit linear scoring (ELS) seems too weak to capture complicated image-class correspondence. We propose a simple yet effective framework, called Implicit Non-linear Similarity Scoring (ICINESS). In particular, we train a scoring network which uses image and class features as input, fuses them by hidden layers, and outputs the similarity. Based on the universal approximation theorem, it can approximate the true similarity function between images and classes if a proper structure is used in an implicit non-linear way, which is more flexible and powerful. With ICINESS framework, we implement ZSL algorithms by shallow and deep networks, which yield consistently superior results.

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IJCAI Conference 2018 Conference Paper

Memory Attention Networks for Skeleton-based Action Recognition

  • Chunyu Xie
  • Ce Li
  • Baochang Zhang
  • Chen Chen
  • Jungong Han
  • Jianzhuang Liu

Skeleton-based action recognition task is entangled with complex spatio-temporal variations of skeleton joints, and remains challenging for Recurrent Neural Networks (RNNs). In this work, we propose a temporal-then-spatial recalibration scheme to alleviate such complex variations, resulting in an end-to-end Memory Attention Networks (MANs) which consist of a Temporal Attention Recalibration Module (TARM) and a Spatio-Temporal Convolution Module (STCM). Specifically, the TARM is deployed in a residual learning module that employs a novel attention learning network to recalibrate the temporal attention of frames in a skeleton sequence. The STCM treats the attention calibrated skeleton joint sequences as images and leverages the Convolution Neural Networks (CNNs) to further model the spatial and temporal information of skeleton data. These two modules (TARM and STCM) seamlessly form a single network architecture that can be trained in an end-to-end fashion. MANs significantly boost the performance of skeleton-based action recognition and achieve the best results on four challenging benchmark datasets: NTU RGB+D, HDM05, SYSU-3D and UT-Kinect.

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AAAI Conference 2018 Conference Paper

On Trivial Solution and High Correlation Problems in Deep Supervised Hashing

  • Yuchen Guo
  • Xin Zhao
  • Guiguang Ding
  • Jungong Han

Deep supervised hashing (DSH), which combines binary learning and convolutional neural network, has attracted considerable research interests and achieved promising performance for highly efficient image retrieval. In this paper, we show that the widely used loss functions, pair-wise loss and triplet loss, suffer from the trivial solution problem and usually lead to highly correlated bits in practice, limiting the performance of DSH. One important reason is that it is difficult to incorporate proper constraints into the loss functions under the mini-batch based optimization algorithm. To tackle these problems, we propose to adopt ensemble learning strategy for deep model training. We found out that this simple strategy is capable of effectively decorrelating different bits, making the hashcodes more informative. Moreover, it is very easy to parallelize the training and support incremental model learning, which are very useful for real-world applications but usually ignored by existing DSH approaches. Experiments on benchmarks demonstrate the proposed ensemble based DSH can improve the performance of DSH approaches significant.

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IJCAI Conference 2018 Conference Paper

Personality-Aware Personalized Emotion Recognition from Physiological Signals

  • Sicheng Zhao
  • Guiguang Ding
  • Jungong Han
  • Yue Gao

Emotion recognition methodologies from physiological signals are increasingly becoming personalized, due to the subjective responses of different subjects to physical stimuli. Existing works mainly focused on modelling the involved physiological corpus of each subject, without considering the psychological factors. The latent correlation among different subjects has also been rarely examined. We propose to investigate the influence of personality on emotional behavior in a hypergraph learning framework. Assuming that each vertex is a compound tuple (subject, stimuli), multi-modal hypergraphs can be constructed based on the personality correlation among different subjects and on the physiological correlation among corresponding stimuli. To reveal the different importance of vertices, hyperedges, and modalities, we assign each of them with weights. The emotion relevance learned on the vertex-weighted multi-modal multi-task hypergraphs is employed for emotion recognition. We carry out extensive experiments on the ASCERTAIN dataset and the results demonstrate the superiority of the proposed method.

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IJCAI Conference 2018 Conference Paper

Show, Observe and Tell: Attribute-driven Attention Model for Image Captioning

  • Hui Chen
  • Guiguang Ding
  • Zijia Lin
  • Sicheng Zhao
  • Jungong Han

Despite the fact that attribute-based approaches and attention-based approaches have been proven to be effective in image captioning, most attribute-based approaches simply predict attributes independently without taking the co-occurrence dependencies among attributes into account. Besides, most attention-based captioning models directly leverage the feature map extracted from CNN, in which many features may be redundant in relation to the image content. In this paper, we focus on training a good attribute-inference model via the recurrent neural network (RNN) for image captioning, where the co-occurrence dependencies among attributes can be maintained. The uniqueness of our inference model lies in the usage of a RNN with the visual attention mechanism to \textit{observe} the image before generating captions. Additionally, it is noticed that compact and attribute-driven features will be more useful for the attention-based captioning model. To this end, we extract the context feature for each attribute, and guide the captioning model adaptively attend to these context features. We verify the effectiveness and superiority of the proposed approach over the other captioning approaches by conducting massive experiments and comparisons on MS COCO image captioning dataset.

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AAAI Conference 2018 Conference Paper

Temporal-Difference Learning With Sampling Baseline for Image Captioning

  • Hui Chen
  • Guiguang Ding
  • Sicheng Zhao
  • Jungong Han

The existing methods for image captioning usually train the language model under the cross entropy loss, which results in the exposure bias and inconsistency of evaluation metric. Recent research has shown these two issues can be well addressed by policy gradient method in reinforcement learning domain attributable to its unique capability of directly optimizing the discrete and non-differentiable evaluation metric. In this paper, we utilize reinforcement learning method to train the image captioning model. Specifically, we train our image captioning model to maximize the overall reward of the sentences by adopting the temporal-difference (TD) learning method, which takes the correlation between temporally successive actions into account. In this way, we assign different values to different words in one sampled sentence by a discounted coefficient when back-propagating the gradient with the REINFORCE algorithm, enabling the correlation between actions to be learned. Besides, instead of estimating a “baseline” to normalize the rewards with another network, we utilize the reward of another Monte-Carlo sample as the “baseline” to avoid high variance. We show that our proposed method can improve the quality of generated captions and outperforms the state-of-the-art methods on the benchmark dataset MS COCO in terms of seven evaluation metrics.

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IJCAI Conference 2018 Conference Paper

Unsupervised Deep Hashing via Binary Latent Factor Models for Large-scale Cross-modal Retrieval

  • Gengshen Wu
  • Zijia Lin
  • Jungong Han
  • Li Liu
  • Guiguang Ding
  • Baochang Zhang
  • Jialie Shen

Despite its great success, matrix factorization based cross-modality hashing suffers from two problems: 1) there is no engagement between feature learning and binarization; and 2) most existing methods impose the relaxation strategy by discarding the discrete constraints when learning the hash function, which usually yields suboptimal solutions. In this paper, we propose a novel multimodal hashing framework, referred as Unsupervised Deep Cross-Modal Hashing (UDCMH), for multimodal data search in a self-taught manner via integrating deep learning and matrix factorization with binary latent factor models. On one hand, our unsupervised deep learning framework enables the feature learning to be jointly optimized with the binarization. On the other hand, the hashing system based on the binary latent factor models can generate unified binary codes by solving a discrete-constrained objective function directly with no need for a relaxation step. Moreover, novel Laplacian constraints are incorporated into the objective function, which allow to preserve not only the nearest neighbors that are commonly considered in the literature but also the farthest neighbors of data, even if the semantic labels are not available. Extensive experiments on multiple datasets highlight the superiority of the proposed framework over several state-of-the-art baselines.

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IJCAI Conference 2018 Conference Paper

Where to Prune: Using LSTM to Guide End-to-end Pruning

  • Jing Zhong
  • Guiguang Ding
  • Yuchen Guo
  • Jungong Han
  • Bin Wang

Recent years have witnessed the great success of convolutional neural networks (CNNs) in many related fields. However, its huge model size and computation complexity bring in difficulty when deploying CNNs in some scenarios, like embedded system with low computation power. To address this issue, many works have been proposed to prune filters in CNNs to reduce computation. However, they mainly focus on seeking which filters are unimportant in a layer and then prune filters layer by layer or globally. In this paper, we argue that the pruning order is also very significant for model pruning. We propose a novel approach to figure out which layers should be pruned in each step. First, we utilize a long short-term memory (LSTM) to learn the hierarchical characteristics of a network and generate a pruning decision for each layer, which is the main difference from previous works. Next, a channel-based method is adopted to evaluate the importance of filters in a to-be-pruned layer, followed by an accelerated recovery step. Experimental results demonstrate that our approach is capable of reducing 70. 1% FLOPs for VGG and 47. 5% for Resnet-56 with comparable accuracy. Also, the learning results seem to reveal the sensitivity of each network layer.

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IJCAI Conference 2018 Conference Paper

Zero Shot Learning via Low-rank Embedded Semantic AutoEncoder

  • Yang Liu
  • Quanxue Gao
  • Jin Li
  • Jungong Han
  • Ling Shao

Zero-shot learning (ZSL) has been widely researched and get successful in machine learning. Most existing ZSL methods aim to accurately recognize objects of unseen classes by learning a shared mapping from the feature space to a semantic space. However, such methods did not investigate in-depth whether the mapping can precisely reconstruct the original visual feature. Motivated by the fact that the data have low intrinsic dimensionality e. g. low-dimensional subspace. In this paper, we formulate a novel framework named Low-rank Embedded Semantic AutoEncoder (LESAE) to jointly seek a low-rank mapping to link visual features with their semantic representations. Taking the encoder-decoder paradigm, the encoder part aims to learn a low-rank mapping from the visual feature to the semantic space, while decoder part manages to reconstruct the original data with the learned mapping. In addition, a non-greedy iterative algorithm is adopted to solve our model. Extensive experiments on six benchmark datasets demonstrate its superiority over several state-of-the-art algorithms.

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AAAI Conference 2018 Conference Paper

Zero-Shot Learning With Attribute Selection

  • Yuchen Guo
  • Guiguang Ding
  • Jungong Han
  • Sheng Tang

Zero-shot learning (ZSL) is regarded as an effective way to construct classification models for target classes which have no labeled samples available. The basic framework is to transfer knowledge from (different) auxiliary source classes having sufficient labeled samples with some attributes shared by target and source classes as bridge. Attributes play an important role in ZSL but they have not gained sufficient attention in recent years. Previous works mostly assume attributes are perfect and treat each attribute equally. However, as shown in this paper, different attributes have different properties, such as their class distribution, variance, and entropy, which may have considerable impact on ZSL accuracy if treated equally. Based on this observation, in this paper we propose to use a subset of attributes, instead of the whole set, for building ZSL models. The attribute selection is conducted by considering the information amount and predictability under a novel joint optimization framework. To our knowledge, this is the first work that notices the influence of attributes themselves and proposes to use a refined attribute set for ZSL. Since our approach focuses on selecting good attributes for ZSL, it can be combined to any attribute based ZSL approaches so as to augment their performance. Experiments on four ZSL benchmarks demonstrate that our approach can improve zeroshot classification accuracy and yield state-of-the-art results.

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AAAI Conference 2017 Conference Paper

Active Learning with Cross-Class Similarity Transfer

  • Yuchen Guo
  • Guiguang Ding
  • Yue Gao
  • Jungong Han

How to save labeling efforts for training supervised classi- fiers is an important research topic in machine learning community. Active learning (AL) and transfer learning (TL) are two useful tools to achieve this goal, and their combination, i. e. , transfer active learning (T-AL) has also attracted considerable research interest. However, existing T-AL approaches consider to transfer knowledge from a source/auxiliary domain which has the same class labels as the target domain, but ignore the relationship among classes. In this paper, we investigate a more practical setting where the classes in source domain are related/similar to but different from the target domain classes. Specifically, we propose a novel cross-class T-AL approach to simultaneously transfer knowledge from source domain and actively annotate the most informative samples in target domain so that we can train satisfactory classifiers with as few labeled samples as possible. In particular, based on the class-class similarity and sample-sample similarity, we adopt a similarity propagation to find the source domain samples that can well capture the characteristics of a target class and then transfer the similar samples as the (pseudo) labeled data for the target class. In turn, the labeled and transferred samples are used to train classifiers and actively select new samples for annotation. Extensive experiments on three datasets demonstrate that the proposed approach outperforms significantly the state-of-the-art related approaches.

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IJCAI Conference 2017 Conference Paper

Approximating Discrete Probability Distribution of Image Emotions by Multi-Modal Features Fusion

  • Sicheng Zhao
  • Guiguang Ding
  • Yue Gao
  • Jungong Han

Existing works on image emotion recognition mainly assigned the dominant emotion category or average dimension values to an image based on the assumption that viewers can reach a consensus on the emotion of images. However, the image emotions perceived by viewers are subjective by nature and highly related to the personal and situational factors. On the other hand, image emotions can be conveyed by different features, such as semantics and aesthetics. In this paper, we propose a novel machine learning approach that formulates the categorical image emotions as a discrete probability distribution (DPD). To associate emotions with the extracted visual features, we present a weighted multi-modal shared sparse leaning to learn the combination coefficients, with which the DPD of an unseen image can be predicted by linearly integrating the DPDs of the training images. The representation abilities of different modalities are jointly explored and the optimal weight of each modality is automatically learned. Extensive experiments on three datasets verify the superiority of the proposed method, as compared to the state-of-the-art.

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IJCAI Conference 2017 Conference Paper

Dynamic Multi-View Hashing for Online Image Retrieval

  • Liang Xie
  • Jialie Shen
  • Jungong Han
  • Lei Zhu
  • Ling Shao

Advanced hashing technique is essential to facilitate effective large scale online image organization and retrieval, where image contents could be frequently changed. Traditional multi-view hashing methods are developed based on batch-based learning, which leads to very expensive updating cost. Meanwhile, existing online hashing methods mainly focus on single-view data and thus can not achieve promising performance when searching real online images, which are multiple view based data. Further, both types of hashing methods can only produce hash code with fixed length. Consequently they suffer from limited capability to comprehensive characterization of streaming image data in the real world. In this paper, we propose dynamic multi-view hashing (DMVH), which can adaptively augment hash codes according to dynamic changes of image. Meanwhile, DMVH leverages online learning to generate hash codes. It can increase the code length when current code is not able to represent new images effectively. Moreover, to gain further improvement on overall performance, each view is assigned with a weight, which can be efficiently updated during the online learning process. In order to avoid the frequent updating of code length and view weights, an intelligent buffering scheme is also specifically designed to preserve significant data to maintain good effectiveness of DMVH. Experimental results on two real-world image datasets demonstrate superior performance of DWVH over several state-of-the-art hashing methods.

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AAAI Conference 2017 Conference Paper

Reference Based LSTM for Image Captioning

  • Minghai Chen
  • Guiguang Ding
  • Sicheng Zhao
  • Hui Chen
  • Qiang Liu
  • Jungong Han

Image captioning is an important problem in artificial intelligence, related to both computer vision and natural language processing. There are two main problems in existing methods: in the training phase, it is difficult to find which parts of the captions are more essential to the image; in the caption generation phase, the objects or the scenes are sometimes misrecognized. In this paper, we consider the training images as the references and propose a Reference based Long Short Term Memory (R-LSTM) model, aiming to solve these two problems in one goal. When training the model, we assign different weights to different words, which enables the network to better learn the key information of the captions. When generating a caption, the consensus score is utilized to exploit the reference information of neighbor images, which might fix the misrecognition and make the descriptions more natural-sounding. The proposed R-LSTM model outperforms the state-of-the-art approaches on the benchmark dataset MS COCO and obtains top 2 position on 11 of the 14 metrics on the online test server.

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IJCAI Conference 2017 Conference Paper

SitNet: Discrete Similarity Transfer Network for Zero-shot Hashing

  • Yuchen Guo
  • Guiguang Ding
  • Jungong Han
  • Yue Gao

Hashing has been widely utilized for fast image retrieval recently. With semantic information as supervision, hashing approaches perform much better, especially when combined with deep convolution neural network(CNN). However, in practice, new concepts emerge every day, making collecting supervised information for re-training hashing model infeasible. In this paper, we propose a novel zero-shot hashing approach, called Discrete Similarity Transfer Network (SitNet), to preserve the semantic similarity between images from both ``seen'' concepts and new ``unseen'' concepts. Motivated by zero-shot learning, the semantic vectors of concepts are adopted to capture the similarity structures among classes, making the model trained with seen concepts generalize well for unseen ones benefiting from the transferability of the semantic vector space. We adopt a multi-task architecture to exploit the supervised information for seen concepts and the semantic vectors simultaneously. Moreover, a discrete hashing layer is integrated into the network for hashcode generating to avoid the information loss caused by real-value relaxation in training phase, which is a critical problem in existing works. Experiments on three benchmarks validate the superiority of SitNet to the state-of-the-arts.

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IJCAI Conference 2017 Conference Paper

Synthesizing Samples for Zero-shot Learning

  • Yuchen Guo
  • Guiguang Ding
  • Jungong Han
  • Yue Gao

Zero-shot learning (ZSL) is to construct recognition models for unseen target classes that have no labeled samples for training. It utilizes the class attributes or semantic vectors as side information and transfers supervision information from related source classes with abundant labeled samples. Existing ZSL approaches adopt an intermediary embedding space to measure the similarity between a sample and the attributes of a target class to perform zero-shot classification. However, this way may suffer from the information loss caused by the embedding process and the similarity measure cannot fully make use of the data distribution. In this paper, we propose a novel approach which turns the ZSL problem into a conventional supervised learning problem by synthesizing samples for the unseen classes. Firstly, the probability distribution of an unseen class is estimated by using the knowledge from seen classes and the class attributes. Secondly, the samples are synthesized based on the distribution for the unseen class. Finally, we can train any supervised classifiers based on the synthesized samples. Extensive experiments on benchmarks demonstrate the superiority of the proposed approach to the state-of-the-art ZSL approaches.

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IJCAI Conference 2017 Conference Paper

TUCH: Turning Cross-view Hashing into Single-view Hashing via Generative Adversarial Nets

  • Xin Zhao
  • Guiguang Ding
  • Yuchen Guo
  • Jungong Han
  • Yue Gao

Cross-view retrieval, which focuses on searching images as response to text queries or vice versa, has received increasing attention recently. Cross-view hashing is to efficiently solve the cross-view retrieval problem with binary hash codes. Most existing works on cross-view hashing exploit multi-view embedding method to tackle this problem, which inevitably causes the information loss in both image and text domains. Inspired by the Generative Adversarial Nets (GANs), this paper presents a new model that is able to Turn Cross-view Hashing into single-view hashing (TUCH), thus enabling the information of image to be preserved as much as possible. TUCH is a novel deep architecture that integrates a language model network T for text feature extraction, a generator network G to generate fake images from text feature and a hashing network H for learning hashing functions to generate compact binary codes. Our architecture effectively unifies joint generative adversarial learning and cross-view hashing. Extensive empirical evidence shows that our TUCH approach achieves state-of-the-art results, especially on text to image retrieval, based on image-sentences datasets, i. e. standard IAPRTC-12 and large-scale Microsoft COCO.

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IJCAI Conference 2017 Conference Paper

Unsupervised Deep Video Hashing with Balanced Rotation

  • Gengshen Wu
  • Li Liu
  • Yuchen Guo
  • Guiguang Ding
  • Jungong Han
  • Jialie Shen
  • Ling Shao

Recently, hashing video contents for fast retrieval has received increasing attention due to the enormous growth of online videos. As the extension of image hashing techniques, traditional video hashing methods mainly focus on seeking the appropriate video features but pay little attention to how the video-specific features can be leveraged to achieve optimal binarization. In this paper, an end-to-end hashing framework, namely Unsupervised Deep Video Hashing (UDVH), is proposed, where feature extraction, balanced code learning and hash function learning are integrated and optimized in a self-taught manner. Particularly, distinguished from previous work, our framework enjoys two novelties: 1) an unsupervised hashing method that integrates the feature clustering and feature binarization, enabling the neighborhood structure to be preserved in the binary space; 2) a smart rotation applied to the video-specific features that are widely spread in the low-dimensional space such that the variance of dimensions can be balanced, thus generating more effective hash codes. Extensive experiments have been performed on two real-world datasets and the results demonstrate its superiority, compared to the state-of-the-art video hashing methods. To bootstrap further developments, the source code will be made publically available.

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AAAI Conference 2017 Conference Paper

Zero-Shot Recognition via Direct Classifier Learning with Transferred Samples and Pseudo Labels

  • Yuchen Guo
  • Guiguang Ding
  • Jungong Han
  • Yue Gao

As an interesting and emerging topic, zero-shot recognition (ZSR) makes it possible to train a recognition model by specifying the category’s attributes when there are no labeled exemplars available. The fundamental idea for ZSR is to transfer knowledge from the abundant labeled data in different but related source classes via the class attributes. Conventional ZSR approaches adopt a two-step strategy in test stage, where the samples are projected into the attribute space in the first step, and then the recognition is carried out based on considering the relationship between samples and classes in the attribute space. Due to this intermediate transformation, information loss is unavoidable, thus degrading the performance of the overall system. Rather than following this two-step strategy, in this paper, we propose a novel one-step approach that is able to perform ZSR in the original feature space by using directly trained classifiers. To tackle the problem that no labeled samples of target classes are available, we propose to assign pseudo labels to samples based on the reliability and diversity, which in turn will be used to train the classi- fiers. Moreover, we adopt a robust SVM that accounts for the unreliability of pseudo labels. Extensive experiments on four datasets demonstrate consistent performance gains of our approach over the state-of-the-art two-step ZSR approaches.

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IJCAI Conference 2016 Conference Paper

3D Action Recognition Using Multi-Temporal Depth Motion Maps and Fisher Vector

  • Chen Chen
  • Mengyuan Liu
  • Baochang Zhang
  • Jungong Han
  • Junjun Jiang
  • Hong Liu

This paper presents an effective local spatio-temporal descriptor for action recognition from depth video sequences. The unique property of our descriptor is that it takes the shape discrimination and action speed variations into account, intending to solve the problems of distinguishing different pose shapes and identifying the actions with different speeds in one goal. The entire algorithm is carried out in three stages. In the first stage, a depth sequence is divided into temporally overlapping depth segments which are used to generate three depth motion maps (DMMs), capturing the shape and motion cues. To cope with speed variations in actions, multiple frame lengths of depth segments are utilized, leading to a multi-temporal DMMs representation. In the second stage, all the DMMs are first partitioned into dense patches. Then, the local binary patterns (LBP) descriptor is exploited to characterize local rotation invariant texture information in those patches. In the third stage, the Fisher kernel is employed to encode the patch descriptors for a compact feature representation, which is fed into a kernel-based extreme learning machine classifier. Extensive experiments on the public MSRAction3D, MSRGesture3D and DHA datasets show that our proposed method outperforms state-of-the-art approaches for depth-based action recognition.

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