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Spatio-Temporal Conditional Denoising Transformer for Modality-Missing RGBT Tracking
Andong Lu, Ziyi Zha, Jiandong Jin +4
Missing modalities in RGBT tracking often lead to incomplete and unstable multimodal feature representations that greatly degrade the performance. Existing methods typically attemp…
Breaking Shallow Limits: Task-Driven Pixel Fusion for Gap-free RGBT Tracking
Andong Lu, Yuanzhi Guo, Wanyu Wang +3
Current RGBT tracking methods often overlook the impact of fusion location on mitigating modality gap, which is key factor to effective tracking. Our analysis reveals that shallowe…
Towards General Multimodal Visual Tracking
Andong Lu, Mai Wen, Jinhu Wang +4
Existing multimodal tracking studies focus on bi-modal scenarios such as RGB-Thermal, RGB-Event, and RGB-Language. Although promising tracking performance is achieved through lever…
Alignment-Free RGB-T Salient Object Detection: A Large-scale Dataset and Progressive Correlation Network
Kunpeng Wang, Keke Chen, Chenglong Li +2
Alignment-free RGB-Thermal (RGB-T) salient object detection (SOD) aims to achieve robust performance in complex scenes by directly leveraging the complementary information from una…
Breaking Modality Gap in RGBT Tracking: Coupled Knowledge Distillation
Andong Lu, Jiacong Zhao, Chenglong Li +2
Modality gap between RGB and thermal infrared (TIR) images is a crucial issue but often overlooked in existing RGBT tracking methods. It can be observed that modality gap mainly li…
Cross-modulated Attention Transformer for RGBT Tracking
Yun Xiao, Jiacong Zhao, Andong Lu +4
Existing Transformer-based RGBT trackers achieve remarkable performance benefits by leveraging self-attention to extract uni-modal features and cross-attention to enhance multi-mod…