This repository collects RGB-T-Feature-Fusion methods (deep learning methods mainly), codes, and datasets.
The main directions involved are Multispectral Pedestrian, RGB-T Fusion Tracking, RGB-IR Person Re-identification .etc.
(If you think this is useful, please consider giving a star, thanks! We will continue to update this repository)
- Multispectral Pedestrian
- RGB-T Salient Object Detection
- RGB-IR Person Re-identification
- RGB-T Fusion Tracking
KAIST dataset, CVC-14 dataset , FLIR dataset
- Improved KAIST Testing Annotations provided by Liu et al.Link to download
- Sanitized KAIST Training Annotations provided by Li et al.Link to download
- Improved KAIST Training Annotations provided by Zhang et al.Link to download
- Evalutaion codes.Link to download
- Annotation: vbb format->xml format.Link to download
- Uncertainty-Guided Cross-Modal Learning for Robust Multispectral Pedestrian Detection, IEEE Transactions on Circuits and Systems for Video Technology 2021, Jung Uk Kim et al. [PDF]
- Deep Cross-modal Representation Learning and Distillation for Illumination-invariant Pedestrian Detection, IEEE Transactions on Circuits and Systems for Video Technology 2021, T. Liu et al. [PDF]
- Guided Attentive Feature Fusion for Multispectral Pedestrian Detection, WACV 2021, Heng Zhang et al. [PDF]
- Anchor-free Small-scale Multispectral Pedestrian Detection, BMVC 2020, Alexander Wolpert et al. [PDF][Code]
- Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks, ICIP 2020, Heng Zhang et al. [PDF]
- Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems, ECCV 2020, Kailai Zhou et al. [PDF][Code]
- Anchor-free Small-scale Multispectral Pedestrian Detection, BMVC 2020, Alexander Wolpert et al. [PDF][Code]
- Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection, ICCV 2019, Lu Zhang et al. [PDF][Code]
- Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pesdestrian Detecion, ISPRS Journal of Photogrammetry and Remote Sensing 2019, Yanpeng Cao et al.[PDF][Code]
- Cross-modality interactive attention network for multispectral pedestrian detection, Information Fusion 2019, Lu Zhang et al.[PDF][Code]
- Pedestrian detection with unsupervised multispectral feature learning using deep neural networks, Information Fusion 2019, Cao, Yanpeng et al.[PDF]
- Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation, BMVC 2018, Chengyang Li et al.[PDF][Code][Project Link]
- Unified Multi-spectral Pedestrian Detection Based on Probabilistic Fusion Networks, Pattern Recognition 2018, Kihong Park et al.[PDF]
- Multispectral Deep Neural Networks for Pedestrian Detection, BMVC 2016, Jingjing Liu et al.[PDF][Code]
- Multispectral Pedestrian Detection Benchmark Dataset and Baseline, 2015, Soonmin Hwang et al.[PDF][Code]
- Task-conditioned Domain Adaptation for Pedestrian Detection in Thermal Imagery, ECCV 2020, My Kieu et al. [PDF][Code]
- Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection, Information Fusion 2019, Dayan Guan et al.[PDF][Code]
- Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection, Pattern Recognition 2018, Chengyang Li et al.[PDF][Code]
- Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection, ICCV 2019, Lu Zhang et al. [PDF] [Code]
- Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation, BMVC 2018, Chengyang Li et al. [PDF] [Code]
- Unsupervised Domain Adaptation for Multispectral Pedestrian Detection, CVPR 2019 Workshop , Dayan Guan et al. [PDF] [Code]
- Pedestrian detection with unsupervised multispectral feature learning using deep neural networks, Information Fusion 2019, Y. Cao et al. Information Fusion 2019, [PDF] [Code]
- Learning crossmodal deep representations for robust pedestrian detection, CVPR 2017, D. Xu et al.[PDF][Code]