185 citations · 197 across the 8 of their papers we have counts for
8 papers
Sparse-Dense Mixture of Experts Adapter for Multi-Modal Tracking
Yabin Zhu, Jianqi Li, Chenglong Li +3
Parameter-efficient fine-tuning (PEFT) techniques, such as prompts and adapters, are widely used in multi-modal tracking because they alleviate issues of full-model fine-tuning, in…
Visible-Thermal Multiple Object Tracking: Large-scale Video Dataset and Progressive Fusion Approach
Yabin Zhu, Qianwu Wang, Chenglong Li +2
The complementary benefits from visible and thermal infrared data are widely utilized in various computer vision task, such as visual tracking, semantic segmentation and object det…
CRSOT: Cross-Resolution Object Tracking using Unaligned Frame and Event Cameras
Yabin Zhu, Xiao Wang, Chenglong Li +5
Existing datasets for RGB-DVS tracking are collected with DVS346 camera and their resolution () is low for practical applications. Actually, only visible cameras ar…
Uncertainty-aware Bridge based Mobile-Former Network for Event-based Pattern Recognition
Haoxiang Yang, Chengguo Yuan, Yabin Zhu +3
The mainstream human activity recognition (HAR) algorithms are developed based on RGB cameras, which are easily influenced by low-quality images (e.g., low illumination, motion blu…
RGBT Tracking via Progressive Fusion Transformer with Dynamically Guided Learning
Yabin Zhu, Chenglong Li, Xiao Wang +2
Existing Transformer-based RGBT tracking methods either use cross-attention to fuse the two modalities, or use self-attention and cross-attention to model both modality-specific an…
Tiny Object Tracking: A Large-scale Dataset and A Baseline
Yabin Zhu, Chenglong Li, Yao Liu +4
Tiny objects, frequently appearing in practical applications, have weak appearance and features, and receive increasing interests in meany vision tasks, such as object detection an…
Dense Feature Aggregation and Pruning for RGBT Tracking
Yabin Zhu, Chenglong Li, Bin Luo +2
How to perform effective information fusion of different modalities is a core factor in boosting the performance of RGBT tracking. This paper presents a novel deep fusion algorithm…
FANet: Quality-Aware Feature Aggregation Network for Robust RGB-T Tracking
Yabin Zhu, Chenglong Li, Bin Luo +1
This paper investigates how to perform robust visual tracking in adverse and challenging conditions using complementary visual and thermal infrared data (RGBT tracking). We propose…