48 citations · 147 across the 14 of their papers we have counts for
14 papers
In-Loop Model Adaptation with Coupled Latent-Noise Guidance for High-Fidelity Subject-Driven Text-to-Image Generation
Yushun Tang, Weiming Chen, Siyi Liu +3
Text-to-image diffusion models have achieved remarkable success in generating high-quality images from a given text prompt. Subject-driven generation aims to synthesize customized…
Adaptive Spot-Guided Transformer for Consistent Local Feature Matching
Jiahuan Yu, Jiahao Chang, Jianfeng He +2
Local feature matching aims at finding correspondences between a pair of images. Although current detector-free methods leverage Transformer architecture to obtain an impressive pe…
ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation
Jinze Chen, Yang Wang, Yang Cao +2
Dynamic Vision Sensor (DVS) can asynchronously output the events reflecting apparent motion of objects with microsecond resolution, and shows great application potential in monitor…
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…
Disentangle Your Dense Object Detector
Zehui Chen, Chenhongyi Yang, Qiaofei Li +3
Deep learning-based dense object detectors have achieved great success in the past few years and have been applied to numerous multimedia applications such as video understanding.…
MVT: Mask Vision Transformer for Facial Expression Recognition in the wild
Hanting Li, Mingzhe Sui, Feng Zhao +2
Facial Expression Recognition (FER) in the wild is an extremely challenging task in computer vision due to variant backgrounds, low-quality facial images, and the subjectiveness of…
MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking
Xiao Wang, Xiujun Shu, Shiliang Zhang +4
Many RGB-T trackers attempt to attain robust feature representation by utilizing an adaptive weighting scheme (or attention mechanism). Different from these works, we propose a new…
Tracking by Joint Local and Global Search: A Target-aware Attention based Approach
Xiao Wang, Jin Tang, Bin Luo +3
Tracking-by-detection is a very popular framework for single object tracking which attempts to search the target object within a local search window for each frame. Although such l…
Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer
Yulin Li, Jianfeng He, Tianzhu Zhang +3
Occluded person re-identification (Re-ID) is a challenging task as persons are frequently occluded by various obstacles or other persons, especially in the crowd scenario. To addre…
Action Unit Memory Network for Weakly Supervised Temporal Action Localization
Wang Luo, Tianzhu Zhang, Wenfei Yang +4
Weakly supervised temporal action localization aims to detect and localize actions in untrimmed videos with only video-level labels during training. However, without frame-level an…
Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark
Xiao Wang, Xiujun Shu, Zhipeng Zhang +4
Tracking by natural language specification is a new rising research topic that aims at locating the target object in the video sequence based on its language description. Compared…
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…
A Deep Tree-Structured Fusion Model for Single Image Deraining
Xueyang Fu, Qi Qi, Yue Huang +3
We propose a simple yet effective deep tree-structured fusion model based on feature aggregation for the deraining problem. We argue that by effectively aggregating features, a rel…
A Review of Co-saliency Detection Technique: Fundamentals, Applications, and Challenges
Dingwen Zhang, Huazhu Fu, Junwei Han +2
Co-saliency detection is a newly emerging and rapidly growing research area in computer vision community. As a novel branch of visual saliency, co-saliency detection refers to the…