17 citations · 30 across the 6 of their papers we have counts for
7 papers
MI-DETR: A Strong Baseline for Moving Infrared Small Target Detection with Motion Integration
Nian Liu, Jin Gao, Shubo Lin +9
Detecting moving infrared small targets is challenging because tiny, low-contrast targets occupy few pixels and are easily obscured by dynamic backgrounds. Existing multi-frame met…
Look Before You Leap: A GUI-Critic-R1 Model for Pre-Operative Error Diagnosis in GUI Automation
Yuyang Wanyan, Xi Zhang, Haiyang Xu +9
In recent years, Multimodal Large Language Models (MLLMs) have been extensively utilized for multimodal reasoning tasks, including Graphical User Interface (GUI) automation. Unlike…
SynCL: A Synergistic Training Strategy with Instance-Aware Contrastive Learning for End-to-End Multi-Camera 3D Tracking
Shubo Lin, Yutong Kou, Zirui Wu +4
While existing query-based 3D end-to-end visual trackers integrate detection and tracking via the tracking-by-attention paradigm, these two chicken-and-egg tasks encounter optimiza…
An Experimental Study on Exploring Strong Lightweight Vision Transformers via Masked Image Modeling Pre-Training
Jin Gao, Shubo Lin, Shaoru Wang +7
Masked image modeling (MIM) pre-training for large-scale vision transformers (ViTs) has enabled promising downstream performance on top of the learned self-supervised ViT features.…
ZoomTrack: Target-aware Non-uniform Resizing for Efficient Visual Tracking
Yutong Kou, Jin Gao, Bing Li +4
Recently, the transformer has enabled the speed-oriented trackers to approach state-of-the-art (SOTA) performance with high-speed thanks to the smaller input size or the lighter fe…
Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking
Jin Gao, Yan Lu, Xiaojuan Qi +5
Tracking visual objects from a single initial exemplar in the testing phase has been broadly cast as a one-/few-shot problem, i.e., one-shot learning for initial adaptation and few…
Max-Affine Spline Insights Into Deep Network Pruning
Haoran You, Randall Balestriero, Zhihan Lu +6
In this paper, we study the importance of pruning in Deep Networks (DNs) and the yin & yang relationship between (1) pruning highly overparametrized DNs that have been trained from…