1 code implementation • 18 May 2024 • Zhuangzhuang He, Yifan Wang, Yonghui Yang, Peijie Sun, Le Wu, Haoyue Bai, Jinqi Gong, Richang Hong, Min Zhang
To tackle the above limitations, we propose a Double Correction Framework for Denoising Recommendation (DCF), which contains two correction components from views of more precise sample dropping and avoiding more sparse data.
no code implementations • 17 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.
1 code implementation • 14 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.
1 code implementation • 5 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.
1 code implementation • 5 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.
no code implementations • 24 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.
no code implementations • 27 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.
1 code implementation • 9 Dec 2023 • Yin Chen, Jia Li, Shiguang Shan, Meng Wang, Richang Hong
And the TMAs capture and model the relationships of dynamic changes in facial expressions, effectively extending the pre-trained image model for videos.
Ranked #1 on Facial Expression Recognition (FER) on RAF-DB
Dynamic Facial Expression Recognition Facial Expression Recognition +1
no code implementations • 3 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.
no code implementations • 28 Nov 2023 • Jie Li, Zhixin Li, Zhi Liu, Pengyuan Zhou, Richang Hong, Qiyue Li, Han Hu
To our knowledge, this is the first comprehensive study of viewport prediction in volumetric video streaming.
no code implementations • 26 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.
no code implementations • 20 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.
no code implementations • 27 Oct 2023 • Zijie Song, Zhenzhen Hu, Richang Hong
Unsupervised representation learning for image clustering is essential in computer vision.
no code implementations • 19 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.
1 code implementation • 11 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.
no code implementations • 18 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.
1 code implementation • 16 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.
no code implementations • 16 May 2023 • Youze Wang, WenBo Hu, Richang Hong
Multimodal learning involves developing models that can integrate information from various sources like images and texts.
no code implementations • 9 May 2023 • Wei Qin, Zetong Chen, Lei Wang, Yunshi Lan, Weijieying Ren, Richang Hong
This paper proposes a new depression detection system based on LLMs that is both interpretable and interactive.
1 code implementation • 16 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.
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.
1 code implementation • 27 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 #14 on Video Question Answering on NExT-QA (using extra training data)
1 code implementation • 19 Feb 2023 • Biao Qian, Yang Wang, Richang Hong, Meng Wang
how to generate the samples with desirable adaptability to benefit the quantized network?
1 code implementation • 13 Feb 2023 • Lei Chen, Le Wu, Kun Zhang, Richang Hong, Defu Lian, Zhiqiang Zhang, Jun Zhou, Meng Wang
We augment imbalanced training data towards balanced data distribution to improve fairness.
no code implementations • 4 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.
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.
Ranked #6 on 3D Human Pose Estimation on MPI-INF-3DHP
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.
Ranked #62 on Action Recognition on Something-Something V2
no code implementations • 18 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.
no code implementations • 2 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.
1 code implementation • 14 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.
1 code implementation • 10 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.
no code implementations • 2 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.
1 code implementation • 12 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).
no code implementations • 22 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.
Ranked #5 on Facial Expression Recognition (FER) on FER+
no code implementations • 30 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.
1 code implementation • 26 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?
no code implementations • 16 Apr 2022 • Suiyi Zhao, Zhao Zhang, Richang Hong, Mingliang Xu, Yi Yang, Meng Wang
Blind image deblurring (BID) remains a challenging and significant task.
no code implementations • 1 Mar 2022 • Yanrong Guo, Chenyang Zhu, Shijie Hao, Richang Hong
Depression is one of the most prevalent mental disorders, which seriously affects one's life.
1 code implementation • CVPR 2022 • Zhao Zhang, Huan Zheng, Richang Hong, Mingliang Xu, Shuicheng Yan, Meng Wang
Current low-light image enhancement methods can well improve the illumination.
no code implementations • 29 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.
no code implementations • 12 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.
no code implementations • 31 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.
1 code implementation • 16 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.
no code implementations • 5 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.
1 code implementation • 6 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.
1 code implementation • 30 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.
1 code implementation • 18 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.
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.
1 code implementation • 7 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.
no code implementations • 25 May 2020 • Le Wu, Yonghui Yang, Kun Zhang, Richang Hong, Yanjie Fu, Meng Wang
Therefore, item recommendation and attribute inference have become two main tasks in these platforms.
no code implementations • 24 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.
1 code implementation • 22 May 2020 • Shijie Hao, Yuan Zhou, Yanrong Guo, Richang Hong, Jun Cheng, Meng Wang
In SGCPNet, we propose the strategy of spatial-detail guided context propagation.
no code implementations • 9 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.
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.
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.
no code implementations • 21 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.
2 code implementations • 28 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.
2 code implementations • 15 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.
no code implementations • 1 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.
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.
Ranked #1 on Multi-Media Recommendation on MovieLens 10M
no code implementations • 28 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.
no code implementations • 4 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.
no code implementations • 11 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).
no code implementations • 5 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.
no code implementations • 1 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.
no code implementations • 28 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.
2 code implementations • 20 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.
no code implementations • 3 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.
1 code implementation • 11 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.
1 code implementation • 11 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.
no code implementations • 7 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.
1 code implementation • 3 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.
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.
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.
no code implementations • 25 Apr 2018 • Bo Wang, Youjiang Xu, Yahong Han, Richang Hong
Movies provide us with a mass of visual content as well as attracting stories.
2 code implementations • 17 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.
no code implementations • 7 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.
1 code implementation • 20 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.
1 code implementation • 1 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.
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.
no code implementations • 6 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.
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
no code implementations • 6 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.