no code implementations • 7 Feb 2025 • Mengdi Liu, Zhangyang Gao, Hong Chang, Stan Z. Li, Shiguang Shan, Xinlin Chen
Discovering the genotype-phenotype relationship is crucial for genetic engineering, which will facilitate advances in fields such as crop breeding, conservation biology, and personalized medicine.
1 code implementation • 19 Dec 2024 • Jie Huang, Ruibing Hou, Jiahe Zhao, Hong Chang, Shiguang Shan
Human-centric perceptions play a crucial role in real-world applications.
no code implementations • 25 Nov 2024 • Yiheng Li, Ruibing Hou, Hong Chang, Shiguang Shan, Xilin Chen
Human pose plays a crucial role in the digital age.
no code implementations • 22 Nov 2024 • Zhuo Li, Mingshuang Luo, Ruibing Hou, Xin Zhao, Hao liu, Hong Chang, Zimo Liu, Chen Li
Human motion generation plays a vital role in applications such as digital humans and humanoid robot control.
1 code implementation • 11 Nov 2024 • Jiachen Liang, Ruibing Hou, Minyang Hu, Hong Chang, Shiguang Shan, Xilin Chen
Under this unsupervised multi-domain setting, we have identified inherent model bias within CLIP, notably in its visual and text encoders.
no code implementations • 9 Oct 2024 • Keliang Li, Zaifei Yang, Jiahe Zhao, Hongze Shen, Ruibing Hou, Hong Chang, Shiguang Shan, Xilin Chen
The significant advancements in visual understanding and instruction following from Multimodal Large Language Models (MLLMs) have opened up more possibilities for broader applications in diverse and universal human-centric scenarios.
no code implementations • NeurIPS 2023 • Jiachen Liang, Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen
In this paper, we propose a novel SSL setting, where unlabeled samples are drawn from a mixed distribution that deviates from the feature distribution of labeled samples.
1 code implementation • 25 May 2024 • Mingshuang Luo, Ruibing Hou, Zhuo Li, Hong Chang, Zimo Liu, YaoWei Wang, Shiguang Shan
Third, M$^3$GPT learns to model the connections and synergies among various motion-relevant tasks.
no code implementations • 15 Apr 2024 • Jiahe Zhao, Ruibing Hou, Hong Chang, Xinqian Gu, Bingpeng Ma, Shiguang Shan, Xilin Chen
Current clothes-changing person re-identification (re-id) approaches usually perform retrieval based on clothes-irrelevant features, while neglecting the potential of clothes-relevant features.
no code implementations • 17 Mar 2024 • Xuetong Li, Yuan Gao, Hong Chang, Danyang Huang, Yingying Ma, Rui Pan, Haobo Qi, Feifei Wang, Shuyuan Wu, Ke Xu, Jing Zhou, Xuening Zhu, Yingqiu Zhu, Hansheng Wang
A huge amount of statistical methods for massive data computation have been rapidly developed in the past decades.
no code implementations • 15 Mar 2024 • Yiheng Li, Hongyang Li, Zehao Huang, Hong Chang, Naiyan Wang
The versatility of SparseFusion is also validated in the temporal object detection task and 3D lane detection task.
1 code implementation • 6 Mar 2024 • Minyang Hu, Hong Chang, Zong Guo, Bingpeng Ma, Shiguan Shan, Xilin Chen
In this paper, we try to understand FSL by delving into two key questions: (1) How to quantify the relationship between \emph{training} and \emph{novel} tasks?
no code implementations • 25 Aug 2023 • Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen
Learning generalizable representation and classifier for class-imbalanced data is challenging for data-driven deep models.
1 code implementation • CVPR 2023 • Wenrui Liu, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen
In this paper, to better handle the tradeoff problem, we propose Diversity-Measurable Anomaly Detection (DMAD) framework to enhance reconstruction diversity while avoid the undesired generalization on anomalies.
Ranked #1 on
Anomaly Detection
on UCSD Ped2
Anomaly Detection In Surveillance Videos
Defect Detection
+2
1 code implementation • CVPR 2022 • Xinqian Gu, Hong Chang, Bingpeng Ma, Shutao Bai, Shiguang Shan, Xilin Chen
In this paper, we propose a Clothes-based Adversarial Loss (CAL) to mine clothes-irrelevant features from the original RGB images by penalizing the predictive power of re-id model w. r. t.
Ranked #1 on
Multiview Gait Recognition
on CASIA-B
Clothes Changing Person Re-Identification
Multiview Gait Recognition
1 code implementation • 22 Mar 2022 • Botao Ye, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen
The current popular two-stream, two-stage tracking framework extracts the template and the search region features separately and then performs relation modeling, thus the extracted features lack the awareness of the target and have limited target-background discriminability.
Ranked #4 on
Visual Tracking
on TNL2K
1 code implementation • CVPR 2022 • Shutao Bai, Bingpeng Ma, Hong Chang, Rui Huang, Xilin Chen
To further improve SBM, an Integration-and-Distribution Module (IDM) is introduced to enhance frame-level representations.
1 code implementation • 24 Jun 2021 • Ruibing Hou, Bingpeng Ma, Hong Chang, Xinqian Gu, Shiguang Shan, Xilin Chen
Our method significantly outperforms existing methods on the occlusion datasets, while remains top even superior performance on holistic datasets.
1 code implementation • CVPR 2021 • Ruibing Hou, Hong Chang, Bingpeng Ma, Rui Huang, Shiguang Shan
Detail Branch processes frames at original resolution to preserve the detailed visual clues, and Context Branch with a down-sampling strategy is employed to capture long-range contexts.
no code implementations • 18 Apr 2021 • Shen Li, Bingpeng Ma, Hong Chang, Shiguang Shan, Xilin Chen
This paper proposes a novel model, named Continuity-Discrimination Convolutional Neural Network (CD-CNN), for visual object tracking.
1 code implementation • 2 Sep 2020 • Ruibing Hou, Bingpeng Ma, Hong Chang, Xinqian Gu, Shiguang Shan, Xilin Chen
Furthermore, a Channel IAU (CIAU) module is designed to model the semantic contextual interactions between channel features to enhance the feature representation, especially for small-scale visual cues and body parts.
2 code implementations • ECCV 2020 • Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen
This paper proposes a Temporal Complementary Learning Network that extracts complementary features of consecutive video frames for video person re-identification.
1 code implementation • ECCV 2020 • Xinqian Gu, Hong Chang, Bingpeng Ma, Hongkai Zhang, Xilin Chen
Due to the imperfect person detection results and posture changes, temporal appearance misalignment is unavoidable in video-based person re-identification (ReID).
3 code implementations • ECCV 2020 • Hongkai Zhang, Hong Chang, Bingpeng Ma, Naiyan Wang, Xilin Chen
For example, the fixed label assignment strategy and regression loss function cannot fit the distribution change of proposals and thus are harmful to training high quality detectors.
Ranked #89 on
Object Detection
on COCO test-dev
(using extra training data)
1 code implementation • NeurIPS 2019 • Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen
The unseen classes and low-data problem make few-shot classification very challenging.
1 code implementation • ICCV 2019 • Xinqian Gu, Bingpeng Ma, Hong Chang, Shiguang Shan, Xilin Chen
With back propagation, temporal knowledge can be transferred to enhance the image features and the information asymmetry problem can be alleviated.
Ranked #9 on
Person Re-Identification
on iLIDS-VID
no code implementations • CVPR 2019 • Ruibing Hou, Bingpeng Ma, Hong Chang, Xinqian Gu, Shiguang Shan, Xilin Chen
For one thing, the spatial structure of a pedestrian frame can be used to predict the occluded body parts from the unoccluded body parts of this frame.
1 code implementation • CVPR 2019 • Ruibing Hou, Bingpeng Ma, Hong Chang, Xinqian Gu, Shiguang Shan, Xilin Chen
Person re-identification (reID) benefits greatly from deep convolutional neural networks (CNNs) which learn robust feature embeddings.
no code implementations • 16 Jul 2019 • Hongkai Zhang, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen
Recent researches attempt to improve the detection performance by adopting the idea of cascade for single-stage detectors.
no code implementations • 19 Feb 2019 • Chen Change Loy, Dahua Lin, Wanli Ouyang, Yuanjun Xiong, Shuo Yang, Qingqiu Huang, Dongzhan Zhou, Wei Xia, Quanquan Li, Ping Luo, Junjie Yan, Jian-Feng Wang, Zuoxin Li, Ye Yuan, Boxun Li, Shuai Shao, Gang Yu, Fangyun Wei, Xiang Ming, Dong Chen, Shifeng Zhang, Cheng Chi, Zhen Lei, Stan Z. Li, Hongkai Zhang, Bingpeng Ma, Hong Chang, Shiguang Shan, Xilin Chen, Wu Liu, Boyan Zhou, Huaxiong Li, Peng Cheng, Tao Mei, Artem Kukharenko, Artem Vasenin, Nikolay Sergievskiy, Hua Yang, Liangqi Li, Qiling Xu, Yuan Hong, Lin Chen, Mingjun Sun, Yirong Mao, Shiying Luo, Yongjun Li, Ruiping Wang, Qiaokang Xie, Ziyang Wu, Lei Lu, Yiheng Liu, Wengang Zhou
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian.
1 code implementation • 27 Apr 2018 • Kongming Liang, Yuhong Guo, Hong Chang, Xilin Chen
In this paper, we propose a novel framework, called Deep Structural Ranking, for visual relationship detection.
no code implementations • 1 Jan 2018 • Hong Chang, Dit-yan Yeung
In this paper, based on M-estimation from robust statistics, we develop a robust path-based spectral clustering method by defining a robust path-based similarity measure for spectral clustering under both unsupervised and semi-supervised settings.
no code implementations • 18 Dec 2017 • Xu Jia, Hong Chang, Tinne Tuytelaars
In this work, we revisit the more traditional interpolation-based methods, that were popular before, now with the help of deep learning.
no code implementations • ICCV 2015 • Kongming Liang, Hong Chang, Shiguang Shan, Xilin Chen
Attributes are mid-level semantic properties of objects.
no code implementations • NeurIPS 2014 • Zhen Cui, Hong Chang, Shiguang Shan, Xilin Chen
In this paper, we propose a generalized Unsupervised Manifold Alignment (GUMA) method to build the connections between different but correlated datasets without any known correspondences.
no code implementations • CVPR 2014 • Meina Kan, Shiguang Shan, Hong Chang, Xilin Chen
Identifying subjects with variations caused by poses is one of the most challenging tasks in face recognition, since the difference in appearances caused by poses may be even larger than the difference due to identity.
no code implementations • 10 Feb 2014 • Wen Wang, Zhen Cui, Hong Chang, Shiguang Shan, Xilin Chen
In this paper, we propose a simple but effective coupled neural network, called Deeply Coupled Autoencoder Networks (DCAN), which seeks to build two deep neural networks, coupled with each other in every corresponding layers.