Search Results for author: Richang Hong

Found 82 papers, 36 papers with code

Controllable Relation Disentanglement for Few-Shot Class-Incremental Learning

no code implementations17 Mar 2024 Yuan Zhou, Richang Hong, Yanrong Guo, Lin Liu, Shijie Hao, Hanwang Zhang

In this paper, we propose to tackle Few-Shot Class-Incremental Learning (FSCIL) from a new perspective, i. e., relation disentanglement, which means enhancing FSCIL via disentangling spurious relation between categories.

Disentanglement Few-Shot Class-Incremental Learning +2

Gradient-Aware Logit Adjustment Loss for Long-tailed Classifier

1 code implementation14 Mar 2024 Fan Zhang, Wei Qin, Weijieying Ren, Lei Wang, Zetong Chen, Richang Hong

Additionally, We find that most of the solutions to long-tailed problems are still biased towards head classes in the end, and we propose a simple and post hoc prediction re-balancing strategy to further mitigate the basis toward head class.

Few-shot Learner Parameterization by Diffusion Time-steps

1 code implementation5 Mar 2024 Zhongqi Yue, Pan Zhou, Richang Hong, Hanwang Zhang, Qianru Sun

To this end, we find an inductive bias that the time-steps of a Diffusion Model (DM) can isolate the nuanced class attributes, i. e., as the forward diffusion adds noise to an image at each time-step, nuanced attributes are usually lost at an earlier time-step than the spurious attributes that are visually prominent.

Few-Shot Learning Inductive Bias

Doubly Abductive Counterfactual Inference for Text-based Image Editing

1 code implementation5 Mar 2024 Xue Song, Jiequan Cui, Hanwang Zhang, Jingjing Chen, Richang Hong, Yu-Gang Jiang

Through the lens of the formulation, we find that the crux of TBIE is that existing techniques hardly achieve a good trade-off between editability and fidelity, mainly due to the overfitting of the single-image fine-tuning.

counterfactual Counterfactual Inference +2

Audio-Infused Automatic Image Colorization by Exploiting Audio Scene Semantics

no code implementations24 Jan 2024 Pengcheng Zhao, Yanxiang Chen, Yang Zhao, Wei Jia, Zhao Zhang, Ronggang Wang, Richang Hong

Second, the natural co-occurrence of audio and video is utilized to learn the color semantic correlations between audio and visual scenes.

Colorization Image Colorization

Group Multi-View Transformer for 3D Shape Analysis with Spatial Encoding

no code implementations27 Dec 2023 Lixiang Xu, Qingzhe Cui, Richang Hong, Wei Xu, Enhong Chen, Xin Yuan, Chenglong Li, Yuanyan Tang

The large model GMViT achieves excellent 3D classification and retrieval results on the benchmark datasets ModelNet, ShapeNetCore55, and MCB.

3D Classification 3D Shape Recognition +2

Deep Ensembles Meets Quantile Regression: Uncertainty-aware Imputation for Time Series

no code implementations3 Dec 2023 Ying Liu, Peng Cui, WenBo Hu, Richang Hong

Score-based diffusion method(i. e., CSDI) is effective for the time series imputation task but computationally expensive due to the nature of the generative diffusion model framework.

Imputation regression +1

One-bit Supervision for Image Classification: Problem, Solution, and Beyond

no code implementations26 Nov 2023 Hengtong Hu, Lingxi Xie, Xinyue Hue, Richang Hong, Qi Tian

An intriguing property of the setting is that the burden of annotation largely alleviates in comparison to offering the accurate label.

Active Learning Image Classification +2

Clarity ChatGPT: An Interactive and Adaptive Processing System for Image Restoration and Enhancement

no code implementations20 Nov 2023 Yanyan Wei, Zhao Zhang, Jiahuan Ren, Xiaogang Xu, Richang Hong, Yi Yang, Shuicheng Yan, Meng Wang

The generalization capability of existing image restoration and enhancement (IRE) methods is constrained by the limited pre-trained datasets, making it difficult to handle agnostic inputs such as different degradation levels and scenarios beyond their design scopes.

Image Restoration Language Modelling

Embedded Heterogeneous Attention Transformer for Cross-lingual Image Captioning

no code implementations19 Jul 2023 Zijie Song, Zhenzhen Hu, Yuanen Zhou, Ye Zhao, Richang Hong, Meng Wang

The crucial issue in this task is to model the global and the local matching between the image and different languages.

Image Captioning

Generative Contrastive Graph Learning for Recommendation

1 code implementation11 Jul 2023 Yonghui Yang, Zhengwei Wu, Le Wu, Kun Zhang, Richang Hong, Zhiqiang Zhang, Jun Zhou, Meng Wang

Second, feature augmentation imposes the same scale noise augmentation on each node, which neglects the unique characteristics of nodes on the graph.

Collaborative Filtering Contrastive Learning +3

Advancing Incremental Few-shot Semantic Segmentation via Semantic-guided Relation Alignment and Adaptation

no code implementations18 May 2023 Yuan Zhou, Xin Chen, Yanrong Guo, Shijie Hao, Richang Hong, Qi Tian

Incremental few-shot semantic segmentation (IFSS) aims to incrementally extend a semantic segmentation model to novel classes according to only a few pixel-level annotated data, while preserving its segmentation capability on previously learned base categories.

Few-Shot Semantic Segmentation Incremental Learning +3

Iterative Adversarial Attack on Image-guided Story Ending Generation

no code implementations16 May 2023 Youze Wang, WenBo Hu, Richang Hong

Multimodal learning involves developing models that can integrate information from various sources like images and texts.

Adversarial Robustness Adversarial Text +4

Unlearnable Examples Give a False Sense of Security: Piercing through Unexploitable Data with Learnable Examples

1 code implementation16 May 2023 Wan Jiang, Yunfeng Diao, He Wang, Jianxin Sun, Meng Wang, Richang Hong

Unfortunately, we find UEs provide a false sense of security, because they cannot stop unauthorized users from utilizing other unprotected data to remove the protection, by turning unlearnable data into learnable again.

Multimodal Feature Extraction and Fusion for Emotional Reaction Intensity Estimation and Expression Classification in Videos with Transformers

1 code implementation16 Mar 2023 Jia Li, Yin Chen, Xuesong Zhang, Jiantao Nie, Ziqiang Li, Yangchen Yu, Yan Zhang, Richang Hong, Meng Wang

In this paper, we present our advanced solutions to the two sub-challenges of Affective Behavior Analysis in the wild (ABAW) 2023: the Emotional Reaction Intensity (ERI) Estimation Challenge and Expression (Expr) Classification Challenge.


Adaptive Data-Free Quantization

1 code implementation CVPR 2023 Biao Qian, Yang Wang, Richang Hong, Meng Wang

Data-free quantization (DFQ) recovers the performance of quantized network (Q) without the original data, but generates the fake sample via a generator (G) by learning from full-precision network (P), which, however, is totally independent of Q, overlooking the adaptability of the knowledge from generated samples, i. e., informative or not to the learning process of Q, resulting into the overflow of generalization error.

Data Free Quantization

Contrastive Video Question Answering via Video Graph Transformer

1 code implementation27 Feb 2023 Junbin Xiao, Pan Zhou, Angela Yao, Yicong Li, Richang Hong, Shuicheng Yan, Tat-Seng Chua

CoVGT's uniqueness and superiority are three-fold: 1) It proposes a dynamic graph transformer module which encodes video by explicitly capturing the visual objects, their relations and dynamics, for complex spatio-temporal reasoning.

Ranked #12 on Video Question Answering on NExT-QA (using extra training data)

Contrastive Learning Question Answering +1

Rethinking Data-Free Quantization as a Zero-Sum Game

1 code implementation19 Feb 2023 Biao Qian, Yang Wang, Richang Hong, Meng Wang

how to generate the samples with desirable adaptability to benefit the quantized network?

Data Free Quantization

LipFormer: Learning to Lipread Unseen Speakers based on Visual-Landmark Transformers

no code implementations4 Feb 2023 Feng Xue, Yu Li, Deyin Liu, Yincen Xie, Lin Wu, Richang Hong

However, generalizing these methods to unseen speakers incurs catastrophic performance degradation due to the limited number of speakers in training bank and the evident visual variations caused by the shape/color of lips for different speakers.

Lipreading Sentence

3D Human Pose Estimation With Spatio-Temporal Criss-Cross Attention

1 code implementation CVPR 2023 Zhenhua Tang, Zhaofan Qiu, Yanbin Hao, Richang Hong, Ting Yao

On this basis, we devise STCFormer by stacking multiple STC blocks and further integrate a new Structure-enhanced Positional Embedding (SPE) into STCFormer to take the structure of human body into consideration.

3D Human Pose Estimation

Global Temporal Difference Network for Action Recognition

no code implementations TMM 2022 Zhao Xie, Jiansong Chen, Kewei Wu, Dan Guo, Richang Hong

In the global aggregation module, the global prior knowledge is learned by aggregating the visual feature sequence of video into a global vector.

Action Recognition

Stereo Image Rain Removal via Dual-View Mutual Attention

no code implementations18 Nov 2022 Yanyan Wei, Zhao Zhang, ZhongQiu Zhao, Yang Zhao, Richang Hong, Yi Yang

Stereo images, containing left and right view images with disparity, are utilized in solving low-vision tasks recently, e. g., rain removal and super-resolution.

Disparity Estimation Image Restoration +2

Decoupled Cross-Scale Cross-View Interaction for Stereo Image Enhancement in The Dark

no code implementations2 Nov 2022 Huan Zheng, Zhao Zhang, Jicong Fan, Richang Hong, Yi Yang, Shuicheng Yan

Specifically, we present a decoupled interaction module (DIM) that aims for sufficient dual-view information interaction.

Image Enhancement

MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation

1 code implementation14 Oct 2022 Kang Liu, Feng Xue, Dan Guo, Le Wu, Shujie Li, Richang Hong

This paper aims at solving the mismatch problem between MFE and UIM, so as to generate high-quality embedding representations and better model multimodal user preferences.

Collaborative Filtering Image Classification

Joint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering for Recommendation

1 code implementation10 Oct 2022 Kang Liu, Feng Xue, Xiangnan He, Dan Guo, Richang Hong

In this work, we propose to model multi-grained popularity features and jointly learn them together with high-order connectivity, to match the differentiation of user preferences exhibited in popularity features.

Collaborative Filtering Recommendation Systems

Seeing Through the Noisy Dark: Towards Real-world Low-Light Image Enhancement and Denoising

no code implementations2 Oct 2022 Jiahuan Ren, Zhao Zhang, Richang Hong, Mingliang Xu, Yi Yang, Shuicheng Yan

Low-light image enhancement (LLIE) aims at improving the illumination and visibility of dark images with lighting noise.

Attribute Denoising +1

Switchable Online Knowledge Distillation

1 code implementation12 Sep 2022 Biao Qian, Yang Wang, Hongzhi Yin, Richang Hong, Meng Wang

Instead of focusing on the accuracy gap at test phase by the existing arts, the core idea of SwitOKD is to adaptively calibrate the gap at training phase, namely distillation gap, via a switching strategy between two modes -- expert mode (pause the teacher while keep the student learning) and learning mode (restart the teacher).

Knowledge Distillation

Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers

no code implementations22 Jul 2022 Jia Li, Jiantao Nie, Dan Guo, Richang Hong, Meng Wang

Here, we regard an expressive face as the comprehensive result of a set of facial muscle movements on one's poker face (i. e., emotionless face), inspired by Facial Action Coding System.

Disentanglement Facial Expression Recognition +1

Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization

no code implementations30 Apr 2022 Yangcheng Gao, Zhao Zhang, Richang Hong, Haijun Zhang, Jicong Fan, Shuicheng Yan

To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics to imitate the distribution of real data, so that the performance degradation is alleviated.

Data Free Quantization Model Compression +1

A Review-aware Graph Contrastive Learning Framework for Recommendation

1 code implementation26 Apr 2022 Jie Shuai, Kun Zhang, Le Wu, Peijie Sun, Richang Hong, Meng Wang, Yong Li

Second, while most current models suffer from limited user behaviors, can we exploit the unique self-supervised signals in the review-aware graph to guide two recommendation components better?

Contrastive Learning Recommendation Systems +1

Vibration-based Uncertainty Estimation for Learning from Limited Supervision

no code implementations29 Sep 2021 Hengtong Hu, Lingxi Xie, Yinquan Wang, Richang Hong, Meng Wang, Qi Tian

We investigate the problem of estimating uncertainty for training data, so that deep neural networks can make use of the results for learning from limited supervision.

Active Learning

Few-shot Learning with Global Relatedness Decoupled-Distillation

no code implementations12 Jul 2021 Yuan Zhou, Yanrong Guo, Shijie Hao, Richang Hong, ZhengJun Zha, Meng Wang

To overcome these problems, we propose a new Global Relatedness Decoupled-Distillation (GRDD) method using the global category knowledge and the Relatedness Decoupled-Distillation (RDD) strategy.

Few-Shot Learning Metric Learning

Privileged Graph Distillation for Cold Start Recommendation

no code implementations31 May 2021 Shuai Wang, Kun Zhang, Le Wu, Haiping Ma, Richang Hong, Meng Wang

The teacher model is composed of a heterogeneous graph structure for warm users and items with privileged CF links.

Attribute Collaborative Filtering +1

Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation

1 code implementation16 May 2021 Lei Chen, Le Wu, Kun Zhang, Richang Hong, Meng Wang

Despite the performance gain of these implicit feedback based models, the recommendation results are still far from satisfactory due to the sparsity of the observed item set for each user.

Collaborative Filtering

Few-shot Partial Multi-view Learning

no code implementations5 May 2021 Yuan Zhou, Yanrong Guo, Shijie Hao, Richang Hong, Jiebo Luo

The challenges of this task are twofold: (i) it is difficult to overcome the impact of data scarcity under the interference of missing views; (ii) the limited number of data exacerbates information scarcity, thus making it harder to address the view-missing issue in turn.


Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning

1 code implementation6 Apr 2021 Jianfeng Dong, Zhe Ma, Xiaofeng Mao, Xun Yang, Yuan He, Richang Hong, Shouling Ji

In this similarity paradigm, one should pay more attention to the similarity in terms of a specific design/attribute between fashion items.


Revisiting Local Descriptor for Improved Few-Shot Classification

1 code implementation30 Mar 2021 Jun He, Richang Hong, Xueliang Liu, Mingliang Xu, Qianru Sun

Few-shot classification studies the problem of quickly adapting a deep learner to understanding novel classes based on few support images.

Classification Decision Making +1

Learning Fair Representations for Recommendation: A Graph-based Perspective

1 code implementation18 Feb 2021 Le Wu, Lei Chen, Pengyang Shao, Richang Hong, Xiting Wang, Meng Wang

For each user, this transformation is achieved under the adversarial learning of a user-centric graph, in order to obfuscate each sensitive feature between both the filtered user embedding and the sub graph structures of this user.

Fairness Recommendation Systems

One-bit Supervision for Image Classification

1 code implementation NeurIPS 2020 Hengtong Hu, Lingxi Xie, Zewei Du, Richang Hong, Qi Tian

Instead of training a model upon the accurate label of each sample, our setting requires the model to query with a predicted label of each sample and learn from the answer whether the guess is correct.

Classification General Classification +1

RGCF: Refined Graph Convolution Collaborative Filtering with concise and expressive embedding

1 code implementation7 Jul 2020 Kang Liu, Feng Xue, Richang Hong

In this work, we develop a new GCN-based Collaborative Filtering model, named Refined Graph convolution Collaborative Filtering(RGCF), where the construction of the embeddings of users (items) are delicately redesigned from several aspects during the aggregation on the graph.

Collaborative Filtering

Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation

no code implementations24 May 2020 Le Wu, Yonghui Yang, Lei Chen, Defu Lian, Richang Hong, Meng Wang

The transfer network is designed to approximate the learned item embeddings from graph neural networks by taking each item's visual content as input, in order to tackle the new segment problem in the test phase.

Transfer Learning

Memory-Augmented Relation Network for Few-Shot Learning

no code implementations9 May 2020 Jun He, Richang Hong, Xueliang Liu, Mingliang Xu, Zheng-Jun Zha, Meng Wang

Metric-based few-shot learning methods concentrate on learning transferable feature embedding that generalizes well from seen categories to unseen categories under the supervision of limited number of labelled instances.

Few-Shot Learning Metric Learning +2

Real-world Person Re-Identification via Degradation Invariance Learning

no code implementations CVPR 2020 Yukun Huang, Zheng-Jun Zha, Xueyang Fu, Richang Hong, Liang Li

Person re-identification (Re-ID) in real-world scenarios usually suffers from various degradation factors, e. g., low-resolution, weak illumination, blurring and adverse weather.

Image Restoration Person Re-Identification +2

Creating Something from Nothing: Unsupervised Knowledge Distillation for Cross-Modal Hashing

1 code implementation CVPR 2020 Hengtong Hu, Lingxi Xie, Richang Hong, Qi Tian

In recent years, cross-modal hashing (CMH) has attracted increasing attentions, mainly because its potential ability of mapping contents from different modalities, especially in vision and language, into the same space, so that it becomes efficient in cross-modal data retrieval.

Knowledge Distillation Retrieval

Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems

no code implementations21 Feb 2020 Wenqiang Lei, Xiangnan He, Yisong Miao, Qingyun Wu, Richang Hong, Min-Yen Kan, Tat-Seng Chua

Recommender systems are embracing conversational technologies to obtain user preferences dynamically, and to overcome inherent limitations of their static models.

Recommendation Systems

Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

2 code implementations28 Jan 2020 Lei Chen, Le Wu, Richang Hong, Kun Zhang, Meng Wang

Second, we propose a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user-item interaction data.

Collaborative Filtering Recommendation Systems +1

DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation

2 code implementations15 Jan 2020 Le Wu, Junwei Li, Peijie Sun, Richang Hong, Yong Ge, Meng Wang

Recently, we propose a preliminary work of a neural influence diffusion network (i. e., DiffNet) for social recommendation (Diffnet), which models the recursive social diffusion process to capture the higher-order relationships for each user.

Collaborative Filtering

Diversifying Inference Path Selection: Moving-Mobile-Network for Landmark Recognition

no code implementations1 Dec 2019 Biao Qian, Yang Wang, Zhao Zhang, Richang Hong, Meng Wang, Ling Shao

We intuitively find that M$^2$Net can essentially promote the diversity of the inference path (selected blocks subset) selection, so as to enhance the recognition accuracy.

Landmark Recognition

MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video

1 code implementation ACM International Conference on Multimedia 2019 Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, Tat-Seng Chua

Existing works on multimedia recommendation largely exploit multi-modal contents to enrich item representations, while less effort is made to leverage information interchange between users and items to enhance user representations and further capture user's fine-grained preferences on different modalities.

Microvideo Recommendation Micro-video recommendations +4

A Coarse-to-Fine Multi-stream Hybrid Deraining Network for Single Image Deraining

no code implementations28 Aug 2019 Yanyan Wei, Zhao Zhang, Haijun Zhang, Richang Hong, Meng Wang

To obtain the negative rain streaks during training process more accurately, we present a new module named dual path residual dense block, i. e., Residual path and Dense path.

Single Image Deraining SSIM

Robust Subspace Discovery by Block-diagonal Adaptive Locality-constrained Representation

no code implementations4 Aug 2019 Zhao Zhang, Jiahuan Ren, Sheng Li, Richang Hong, Zheng-Jun Zha, Meng Wang

Leveraging on the Frobenius-norm based latent low-rank representation model, rBDLR jointly learns the coding coefficients and salient features, and improves the results by enhancing the robustness to outliers and errors in given data, preserving local information of salient features adaptively and ensuring the block-diagonal structures of the coefficients.

Representation Learning

Joint Subspace Recovery and Enhanced Locality Driven Robust Flexible Discriminative Dictionary Learning

no code implementations11 Jun 2019 Zhao Zhang, Jiahuan Ren, Weiming Jiang, Zheng Zhang, Richang Hong, Shuicheng Yan, Meng Wang

We propose a joint subspace recovery and enhanced locality based robust flexible label consistent dictionary learning method called Robust Flexible Discriminative Dictionary Learning (RFDDL).

Dictionary Learning

Learning to Compose and Reason with Language Tree Structures for Visual Grounding

no code implementations5 Jun 2019 Richang Hong, Daqing Liu, Xiaoyu Mo, Xiangnan He, Hanwang Zhang

Grounding natural language in images, such as localizing "the black dog on the left of the tree", is one of the core problems in artificial intelligence, as it needs to comprehend the fine-grained and compositional language space.

Visual Grounding Visual Reasoning

Personalized Multimedia Item and Key Frame Recommendation

no code implementations1 Jun 2019 Le Wu, Lei Chen, Yonghui Yang, Richang Hong, Yong Ge, Xing Xie, Meng Wang

We argue that the key challenge of this problem lies in discovering users' visual profiles for key frame recommendation, as most recommendation models would fail without any users' fine-grained image behavior.

Online Filter Clustering and Pruning for Efficient Convnets

no code implementations28 May 2019 Zhengguang Zhou, Wengang Zhou, Richang Hong, Houqiang Li

Pruning filters is an effective method for accelerating deep neural networks (DNNs), but most existing approaches prune filters on a pre-trained network directly which limits in acceleration.


A Neural Influence Diffusion Model for Social Recommendation

2 code implementations20 Apr 2019 Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang

The key idea of our proposed model is that we design a layer-wise influence propagation structure to model how users' latent embeddings evolve as the social diffusion process continues.

Collaborative Filtering Recommendation Systems

Cross-Entropy Adversarial View Adaptation for Person Re-identification

no code implementations3 Apr 2019 Lin Wu, Richang Hong, Yang Wang, Meng Wang

The main contribution is to learn coupled asymmetric mappings regarding view characteristics which are adversarially trained to address the view discrepancy by optimising the cross-entropy view confusion objective.

Person Re-Identification

Fast Matrix Factorization with Non-Uniform Weights on Missing Data

1 code implementation11 Nov 2018 Xiangnan He, Jinhui Tang, Xiaoyu Du, Richang Hong, Tongwei Ren, Tat-Seng Chua

This poses an imbalanced learning problem, since the scale of missing entries is usually much larger than that of observed entries, but they cannot be ignored due to the valuable negative signal.

Deep Item-based Collaborative Filtering for Top-N Recommendation

1 code implementation11 Nov 2018 Feng Xue, Xiangnan He, Xiang Wang, Jiandong Xu, Kai Liu, Richang Hong

In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items.

Collaborative Filtering Decision Making +1

SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation

no code implementations7 Nov 2018 Le Wu, Peijie Sun, Richang Hong, Yanjie Fu, Xiting Wang, Meng Wang

Based on a classical CF model, the key idea of our proposed model is that we borrow the strengths of GCNs to capture how users' preferences are influenced by the social diffusion process in social networks.

Collaborative Filtering Recommendation Systems

A Hierarchical Attention Model for Social Contextual Image Recommendation

1 code implementation3 Jun 2018 Le Wu, Lei Chen, Richang Hong, Yanjie Fu, Xing Xie, Meng Wang

After that, we design a hierarchical attention network that naturally mirrors the hierarchical relationship (elements in each aspects level, and the aspect level) of users' latent interests with the identified key aspects.

Multi-Cue Correlation Filters for Robust Visual Tracking

1 code implementation CVPR 2018 Ning Wang, Wengang Zhou, Qi Tian, Richang Hong, Meng Wang, Houqiang Li

By combining different types of features, our approach constructs multiple experts through Discriminative Correlation Filter (DCF) and each of them tracks the target independently.

Visual Tracking

Interleaved Structured Sparse Convolutional Neural Networks

no code implementations CVPR 2018 Guotian Xie, Jingdong Wang, Ting Zhang, Jian-Huang Lai, Richang Hong, Guo-Jun Qi

In this paper, we study the problem of designing efficient convolutional neural network architectures with the interest in eliminating the redundancy in convolution kernels.

IGCV$2$: Interleaved Structured Sparse Convolutional Neural Networks

2 code implementations17 Apr 2018 Guotian Xie, Jingdong Wang, Ting Zhang, Jian-Huang Lai, Richang Hong, Guo-Jun Qi

In this paper, we study the problem of designing efficient convolutional neural network architectures with the interest in eliminating the redundancy in convolution kernels.

Self-Supervised Video Hashing with Hierarchical Binary Auto-encoder

no code implementations7 Feb 2018 Jingkuan Song, Hanwang Zhang, Xiangpeng Li, Lianli Gao, Meng Wang, Richang Hong

Existing video hash functions are built on three isolated stages: frame pooling, relaxed learning, and binarization, which have not adequately explored the temporal order of video frames in a joint binary optimization model, resulting in severe information loss.

Binarization Retrieval +1

Enhancing Person Re-identification in a Self-trained Subspace

1 code implementation20 Apr 2017 Xun Yang, Meng Wang, Richang Hong, Qi Tian, Yong Rui

To address this problem, in this paper, we propose a self-trained subspace learning paradigm for person re-ID which effectively utilizes both labeled and unlabeled data to learn a discriminative subspace where person images across disjoint camera views can be easily matched.

Person Re-Identification

Point-of-Interest Recommendations: Learning Potential Check-ins from Friends

1 code implementation1 Aug 2016 Huayu Li, Yong Ge, Richang Hong, Hengshu Zhu

The emergence of Location-based Social Network (LBSN) services provides a wonderful opportunity to build personalized Point-of-Interest (POI) recommender systems.

Decision Making Recommendation Systems

Cascaded Interactional Targeting Network for Egocentric Video Analysis

no code implementations CVPR 2016 Yang Zhou, Bingbing Ni, Richang Hong, Xiaokang Yang, Qi Tian

Firstly, a novel EM-like learning framework is proposed to train the pixel-level deep convolutional neural network (DCNN) by seamlessly integrating weakly supervised data (i. e., massive bounding box annotations) with a small set of strongly supervised data (i. e., fully annotated hand segmentation maps) to achieve state-of-the-art hand segmentation performance.

Action Recognition Foreground Segmentation +4

Pooling the Convolutional Layers in Deep ConvNets for Action Recognition

no code implementations6 Nov 2015 Shichao Zhao, Yanbin Liu, Yahong Han, Richang Hong

It achieves the accuracy of 93. 78\% on UCF101 which is the state-of-the-art and the accuracy of 65. 62\% on HMDB51 which is comparable to the state-of-the-art.

Action Recognition Image Classification +1

Interaction Part Mining: A Mid-Level Approach for Fine-Grained Action Recognition

no code implementations CVPR 2015 Yang Zhou, Bingbing Ni, Richang Hong, Meng Wang, Qi Tian

Secondly, these object regions are matched and tracked across frames to form a large spatio-temporal graph based on the appearance matching and the dense motion trajectories through them.

Fine-grained Action Recognition Human-Object Interaction Detection +2

Crowded Scene Analysis: A Survey

no code implementations6 Feb 2015 Teng Li, Huan Chang, Meng Wang, Bingbing Ni, Richang Hong, Shuicheng Yan

Then, existing models, popular algorithms, evaluation protocols, as well as system performance are provided corresponding to different aspects of crowded scene analysis.

Anomaly Detection

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