8 papers · 1 filter
RGBT Tracking via All-layer Multimodal Interactions with Progressive Fusion Mamba
Andong Lu, Wanyu Wang, Chenglong Li +2
Existing RGBT tracking methods often design various interaction models to perform cross-modal fusion of each layer, but can not execute the feature interactions among all layers, w…
Modality-missing RGBT Tracking: Invertible Prompt Learning and High-quality Benchmarks
Andong Lu, Jiacong Zhao, Chenglong Li +2
Current RGBT tracking research relies on the complete multi-modal input, but modal information might miss due to some factors such as thermal sensor self-calibration and data trans…
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…
Adapting Segment Anything Model to Multi-modal Salient Object Detection with Semantic Feature Fusion Guidance
Kunpeng Wang, Danying Lin, Chenglong Li +2
Although most existing multi-modal salient object detection (SOD) methods demonstrate effectiveness through training models from scratch, the limited multi-modal data hinders these…
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…
Unified-modal Salient Object Detection via Adaptive Prompt Learning
Kunpeng Wang, Chenglong Li, Zhengzheng Tu +2
Existing single-modal and multi-modal salient object detection (SOD) methods focus on designing specific architectures tailored for their respective tasks. However, developing comp…