342 citations · 953 across the 32 of their papers we have counts for
30 papers · 1 filter
Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning
Shaofeng Zhang, Feng Zhu, Rui Zhao +1
We propose ADCLR: A ccurate and D ense Contrastive Representation Learning, a novel self-supervised learning framework for learning accurate and dense vision representation. To ext…
Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis
Xiaoshi Wu, Yiming Hao, Keqiang Sun +4
Recent text-to-image generative models can generate high-fidelity images from text inputs, but the quality of these generated images cannot be accurately evaluated by existing eval…
Instruct-ReID: A Multi-purpose Person Re-identification Task with Instructions
Weizhen He, Yiheng Deng, Shixiang Tang +9
Human intelligence can retrieve any person according to both visual and language descriptions. However, the current computer vision community studies specific person re-identificat…
CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching
Xiaoshi Wu, Feng Zhu, Rui Zhao +1
Open-vocabulary detection (OVD) is an object detection task aiming at detecting objects from novel categories beyond the base categories on which the detector is trained. Recent OV…
HumanBench: Towards General Human-centric Perception with Projector Assisted Pretraining
Shixiang Tang, Cheng Chen, Qingsong Xie +9
Human-centric perceptions include a variety of vision tasks, which have widespread industrial applications, including surveillance, autonomous driving, and the metaverse. It is des…
Human Preference Score: Better Aligning Text-to-Image Models with Human Preference
Xiaoshi Wu, Keqiang Sun, Feng Zhu +2
Recent years have witnessed a rapid growth of deep generative models, with text-to-image models gaining significant attention from the public. However, existing models often genera…
UniHCP: A Unified Model for Human-Centric Perceptions
Yuanzheng Ci, Yizhou Wang, Meilin Chen +7
Human-centric perceptions (e.g., pose estimation, human parsing, pedestrian detection, person re-identification, etc.) play a key role in industrial applications of visual models.…
Saliency Guided Contrastive Learning on Scene Images
Meilin Chen, Yizhou Wang, Shixiang Tang +6
Self-supervised learning holds promise in leveraging large numbers of unlabeled data. However, its success heavily relies on the highly-curated dataset, e.g., ImageNet, which still…
Domain Invariant Masked Autoencoders for Self-supervised Learning from Multi-domains
Haiyang Yang, Meilin Chen, Yizhou Wang +5
Generalizing learned representations across significantly different visual domains is a fundamental yet crucial ability of the human visual system. While recent self-supervised lea…
Feature Erasing and Diffusion Network for Occluded Person Re-Identification
Zhikang Wang, Feng Zhu, Shixiang Tang +3
Occluded person re-identification (ReID) aims at matching occluded person images to holistic ones across different camera views. Target Pedestrians (TP) are usually disturbed by No…
Revisiting the Transferability of Supervised Pretraining: an MLP Perspective
Yizhou Wang, Shixiang Tang, Feng Zhu +4
The pretrain-finetune paradigm is a classical pipeline in visual learning. Recent progress on unsupervised pretraining methods shows superior transfer performance to their supervis…
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
Jiawei Yu, Ye Zheng, Xiang Wang +4
Unsupervised anomaly detection and localization is crucial to the practical application when collecting and labeling sufficient anomaly data is infeasible. Most existing representa…
Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization
Ye Zheng, Xiang Wang, Rui Deng +3
The essence of unsupervised anomaly detection is to learn the compact distribution of normal samples and detect outliers as anomalies in testing. Meanwhile, the anomalies in real-w…
Improving Facial Attribute Recognition by Group and Graph Learning
Zhenghao Chen, Shuhang Gu, Feng Zhu +2
Exploiting the relationships between attributes is a key challenge for improving multiple facial attribute recognition. In this work, we are concerned with two types of correlation…
Neighbourhood-guided Feature Reconstruction for Occluded Person Re-Identification
Shijie Yu, Dapeng Chen, Rui Zhao +2
Person images captured by surveillance cameras are often occluded by various obstacles, which lead to defective feature representation and harm person re-identification (Re-ID) per…
Multiple Domain Experts Collaborative Learning: Multi-Source Domain Generalization For Person Re-Identification
Shijie Yu, Feng Zhu, Dapeng Chen +5
Recent years have witnessed significant progress in person re-identification (ReID). However, current ReID approaches still suffer from considerable performance degradation when un…
Self-distillation with Batch Knowledge Ensembling Improves ImageNet Classification
Yixiao Ge, Xiao Zhang, Ching Lam Choi +6
The recent studies of knowledge distillation have discovered that ensembling the "dark knowledge" from multiple teachers or students contributes to creating better soft targets for…
Progressive Correspondence Pruning by Consensus Learning
Chen Zhao, Yixiao Ge, Feng Zhu +3
Correspondence selection aims to correctly select the consistent matches (inliers) from an initial set of putative correspondences. The selection is challenging since putative matc…
Self-supervising Fine-grained Region Similarities for Large-scale Image Localization
Yixiao Ge, Haibo Wang, Feng Zhu +2
The task of large-scale retrieval-based image localization is to estimate the geographical location of a query image by recognizing its nearest reference images from a city-scale d…
Continual Representation Learning for Biometric Identification
Bo Zhao, Shixiang Tang, Dapeng Chen +2
With the explosion of digital data in recent years, continuously learning new tasks from a stream of data without forgetting previously acquired knowledge has become increasingly i…
Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID
Yixiao Ge, Feng Zhu, Dapeng Chen +2
Domain adaptive object re-ID aims to transfer the learned knowledge from the labeled source domain to the unlabeled target domain to tackle the open-class re-identification problem…
COCAS: A Large-Scale Clothes Changing Person Dataset for Re-identification
Shijie Yu, Shihua Li, Dapeng Chen +3
Recent years have witnessed great progress in person re-identification (re-id). Several academic benchmarks such as Market1501, CUHK03 and DukeMTMC play important roles to promote…
Learning to Cluster Faces via Confidence and Connectivity Estimation
Lei Yang, Dapeng Chen, Xiaohang Zhan +3
Face clustering is an essential tool for exploiting the unlabeled face data, and has a wide range of applications including face annotation and retrieval. Recent works show that su…
Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID
Yixiao Ge, Feng Zhu, Dapeng Chen +3
Unsupervised domain adaptation (UDA) aims at adapting the model trained on a labeled source-domain dataset to an unlabeled target-domain dataset. The task of UDA on open-set person…