1.2k citations · 4.8k across the 80 of their papers we have counts for
80 papers
DPL: Decoupled Prompt Learning for Vision-Language Models
Chen Xu, Yuhan Zhu, Guozhen Zhang +5
Prompt learning has emerged as an efficient and effective approach for transferring foundational Vision-Language Models (e.g., CLIP) to downstream tasks. However, current methods t…
MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking
Ruopeng Gao, Limin Wang
As a video task, Multiple Object Tracking (MOT) is expected to capture temporal information of targets effectively. Unfortunately, most existing methods only explicitly exploit the…
SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes
Yutao Cui, Chenkai Zeng, Xiaoyu Zhao +3
Multi-object tracking in sports scenes plays a critical role in gathering players statistics, supporting further analysis, such as automatic tactical analysis. Yet existing MOT ben…
SparseFormer: Sparse Visual Recognition via Limited Latent Tokens
Ziteng Gao, Zhan Tong, Limin Wang +1
Human visual recognition is a sparse process, where only a few salient visual cues are attended to rather than traversing every detail uniformly. However, most current vision netwo…
Efficient Video Action Detection with Token Dropout and Context Refinement
Lei Chen, Zhan Tong, Yibing Song +2
Streaming video clips with large-scale video tokens impede vision transformers (ViTs) for efficient recognition, especially in video action detection where sufficient spatiotempora…
Progressive Visual Prompt Learning with Contrastive Feature Re-formation
Chen Xu, Yuhan Zhu, Haocheng Shen +4
Prompt learning has been designed as an alternative to fine-tuning for adapting Vision-language (V-L) models to the downstream tasks. Previous works mainly focus on text prompt whi…