2 citations · 2 across the 2 of their papers we have counts for
4 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…
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…