6 papers · 1 filter
Mixture of Scale Experts for Alignment-free RGBT Video Object Detection and A Unified Benchmark
Qishun Wang, Zhengzheng Tu, Kunpeng Wang +2
Existing RGB-Thermal Video Object Detection (RGBT VOD) methods predominantly rely on the manual alignment of image pairs, that is both labor-intensive and time-consuming. This depe…
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…
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…
Learning Adaptive Fusion Bank for Multi-modal Salient Object Detection
Kunpeng Wang, Zhengzheng Tu, Chenglong Li +2
Multi-modal salient object detection (MSOD) aims to boost saliency detection performance by integrating visible sources with depth or thermal infrared ones. Existing methods genera…
Alignment-Free RGBT Salient Object Detection: Semantics-guided Asymmetric Correlation Network and A Unified Benchmark
Kunpeng Wang, Danying Lin, Chenglong Li +2
RGB and Thermal (RGBT) Salient Object Detection (SOD) aims to achieve high-quality saliency prediction by exploiting the complementary information of visible and thermal image pair…