2 citations · 6 across the 6 of their papers we have counts for
6 papers
MaST: Motion-aware Sparse Pipeline for Lightweight Object Tracking
Qingmao Wei, Fagui Liu, Dengke Zhang +2
Transformer-based object trackers are renowned for their strong performance, yet dense token processing often leads to prohibitive computational cost, limiting real-time deployment…
ModuSeg: Decoupling Object Discovery and Semantic Retrieval for Training-Free Weakly Supervised Segmentation
Qingze He, Fagui Liu, Dengke Zhang +2
Weakly supervised semantic segmentation aims to achieve pixel-level predictions using image-level labels. Existing methods typically entangle semantic recognition and object locali…
RTrack: Accelerating Convergence for Visual Object Tracking via Pseudo-Boxes Exploration
Guotian Zeng, Bi Zeng, Hong Zhang +2
Single object tracking (SOT) heavily relies on the representation of the target object as a bounding box. However, due to the potential deformation and rotation experienced by the…
LiteTrack: Layer Pruning with Asynchronous Feature Extraction for Lightweight and Efficient Visual Tracking
Qingmao Wei, Bi Zeng, Jianqi Liu +2
The recent advancements in transformer-based visual trackers have led to significant progress, attributed to their strong modeling capabilities. However, as performance improves, r…
Towards Efficient Training with Negative Samples in Visual Tracking
Qingmao Wei, Bi Zeng, Guotian Zeng
Current state-of-the-art (SOTA) methods in visual object tracking often require extensive computational resources and vast amounts of training data, leading to a risk of overfittin…
Efficient Training for Visual Tracking with Deformable Transformer
Qingmao Wei, Guotian Zeng, Bi Zeng
Recent Transformer-based visual tracking models have showcased superior performance. Nevertheless, prior works have been resource-intensive, requiring prolonged GPU training hours…