4 citations · 10 across the 6 of their papers we have counts for
6 papers
Towards Visual Query Segmentation in the Wild
Bing Fan, Minghao Li, Hanzhi Zhang +6
In this paper, we introduce visual query segmentation (VQS), a new paradigm of visual query localization (VQL) that aims to segment all pixel-level occurrences of an object of inte…
DMTrack: Spatio-Temporal Multimodal Tracking via Dual-Adapter
Weihong Li, Shaohua Dong, Haonan Lu +3
In this paper, we explore adapter tuning and introduce a novel dual-adapter architecture for spatio-temporal multimodal tracking, dubbed DMTrack. The key of our DMTrack lies in two…
LoReTrack: Efficient and Accurate Low-Resolution Transformer Tracking
Shaohua Dong, Yunhe Feng, Qing Yang +2
High-performance Transformer trackers have shown excellent results, yet they often bear a heavy computational load. Observing that a smaller input can immediately and conveniently…
VastTrack: Vast Category Visual Object Tracking
Liang Peng, Junyuan Gao, Xinran Liu +5
In this paper, we introduce a novel benchmark, dubbed VastTrack, towards facilitating the development of more general visual tracking via encompassing abundant classes and videos.…
Beyond MOT: Semantic Multi-Object Tracking
Yunhao Li, Qin Li, Hao Wang +5
Current multi-object tracking (MOT) aims to predict trajectories of targets (i.e., ''where'') in videos. Yet, knowing merely ''where'' is insufficient in many crucial applications.…
Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning
Shaohua Dong, Yunhe Feng, Qing Yang +3
Multimodal (e.g., RGB-Depth/RGB-Thermal) fusion has shown great potential for improving semantic segmentation in complex scenes (e.g., indoor/low-light conditions). Existing approa…