207 citations · 479 across the 22 of their papers we have counts for
22 papers
Diffusion Model for Camouflaged Object Detection
Zhennan Chen, Rongrong Gao, Tian-Zhu Xiang +1
Camouflaged object detection is a challenging task that aims to identify objects that are highly similar to their background. Due to the powerful noise-to-image denoising capabilit…
Tracking Anything in High Quality
Jiawen Zhu, Zhenyu Chen, Zeqi Hao +9
Visual object tracking is a fundamental video task in computer vision. Recently, the notably increasing power of perception algorithms allows the unification of single/multiobject…
3rd Place Solution for PVUW2023 VSS Track: A Large Model for Semantic Segmentation on VSPW
Shijie Chang, Zeqi Hao, Ben Kang +6
In this paper, we introduce 3rd place solution for PVUW2023 VSS track. Semantic segmentation is a fundamental task in computer vision with numerous real-world applications. We have…
Noise-Resistant Multimodal Transformer for Emotion Recognition
Yuanyuan Liu, Haoyu Zhang, Yibing Zhan +4
Multimodal emotion recognition identifies human emotions from various data modalities like video, text, and audio. However, we found that this task can be easily affected by noisy…
All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation
Liyao Tang, Zhe Chen, Shanshan Zhao +2
Pseudo-labels are widely employed in weakly supervised 3D segmentation tasks where only sparse ground-truth labels are available for learning. Existing methods often rely on empiri…
Pose-disentangled Contrastive Learning for Self-supervised Facial Representation
Yuanyuan Liu, Wenbin Wang, Yibing Zhan +3
Self-supervised facial representation has recently attracted increasing attention due to its ability to perform face understanding without relying on large-scale annotated datasets…
Transformer-based Context Condensation for Boosting Feature Pyramids in Object Detection
Zhe Chen, Jing Zhang, Yufei Xu +1
Current object detectors typically have a feature pyramid (FP) module for multi-level feature fusion (MFF) which aims to mitigate the gap between features from different levels and…
CLAMP: Prompt-based Contrastive Learning for Connecting Language and Animal Pose
Xu Zhang, Wen Wang, Zhe Chen +3
Animal pose estimation is challenging for existing image-based methods because of limited training data and large intra- and inter-species variances. Motivated by the progress of v…
Contrastive Boundary Learning for Point Cloud Segmentation
Liyao Tang, Yibing Zhan, Zhe Chen +2
Point cloud segmentation is fundamental in understanding 3D environments. However, current 3D point cloud segmentation methods usually perform poorly on scene boundaries, which deg…
SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection
Chen Chen, Zhe Chen, Jing Zhang +1
Although point-based networks are demonstrated to be accurate for 3D point cloud modeling, they are still falling behind their voxel-based competitors in 3D detection. We observe t…
Recurrent Glimpse-based Decoder for Detection with Transformer
Zhe Chen, Jing Zhang, Dacheng Tao
Although detection with Transformer (DETR) is increasingly popular, its global attention modeling requires an extremely long training period to optimize and achieve promising detec…
Expression Snippet Transformer for Robust Video-based Facial Expression Recognition
Yuanyuan Liu, Wenbin Wang, Chuanxu Feng +3
The recent success of Transformer has provided a new direction to various visual understanding tasks, including video-based facial expression recognition (FER). By modeling visual…
VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows
Xiao Wang, Jianing Li, Lin Zhu +6
Different from visible cameras which record intensity images frame by frame, the biologically inspired event camera produces a stream of asynchronous and sparse events with much lo…
Dynamic Attention guided Multi-Trajectory Analysis for Single Object Tracking
Xiao Wang, Zhe Chen, Jin Tang +4
Most of the existing single object trackers track the target in a unitary local search window, making them particularly vulnerable to challenging factors such as heavy occlusions a…
Recent Progress in Appearance-based Action Recognition
Jack Humphreys, Zhe Chen, Dacheng Tao
Action recognition, which is formulated as a task to identify various human actions in a video, has attracted increasing interest from computer vision researchers due to its import…
Condensing Two-stage Detection with Automatic Object Key Part Discovery
Zhe Chen, Jing Zhang, Dacheng Tao
Modern two-stage object detectors generally require excessively large models for their detection heads to achieve high accuracy. To address this problem, we propose that the model…
Towards High Performance Human Keypoint Detection
Jing Zhang, Zhe Chen, Dacheng Tao
Human keypoint detection from a single image is very challenging due to occlusion, blur, illumination and scale variance. In this paper, we address this problem from three aspects…
A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection
Zhe Chen, Wanli Ouyang, Tongliang Liu +1
Deep learning-based computer vision is usually data-hungry. Many researchers attempt to augment datasets with synthesized data to improve model robustness. However, the augmentatio…
Human Keypoint Detection by Progressive Context Refinement
Jing Zhang, Zhe Chen, Dacheng Tao
Human keypoint detection from a single image is very challenging due to occlusion, blur, illumination and scale variance of person instances. In this paper, we find that context in…
Progressive LiDAR Adaptation for Road Detection
Zhe Chen, Jing Zhang, Dacheng Tao
Despite rapid developments in visual image-based road detection, robustly identifying road areas in visual images remains challenging due to issues like illumination changes and bl…
Pedestrian Attribute Recognition: A Survey
Xiao Wang, Shaofei Zheng, Rui Yang +4
Recognizing pedestrian attributes is an important task in the computer vision community due to it plays an important role in video surveillance. Many algorithms have been proposed…
An Experimental Survey on Correlation Filter-based Tracking
Zhe Chen, Zhibin Hong, Dacheng Tao
Over these years, Correlation Filter-based Trackers (CFTs) have aroused increasing interests in the field of visual object tracking, and have achieved extremely compelling results…