![]() Deep Active Learning from Multispectral Data Through Cross-Modality Prediction Inconsistency, ICIP2021, Heng Zhang et al.Adopting the YOLOv4 Architecture for Low-LatencyMultispectral Pedestrian Detection in Autonomous Driving, Sensors 2022, Kamil Roszyk et al.Spatio-contextual deep network-based multimodal pedestrian detection for autonomous driving, IEEE Transactions on Intelligent Transportation Systems, Kinjal Dasgupta et al.Improving RGB-Infrared Pedestrian Detection by Reducing Cross-Modality Redundancy, ICIP2022, Qingwang Wang et al.Confidence-aware Fusion using Dempster-Shafer Theory for Multispectral Pedestrian Detection, TMM 2022, Qing Li et al.Learning a Dynamic Cross-Modal Network for Multispectral Pedestrian Detection, MM 2022, Jin Xie et al.Multimodal Object Detection via Probabilistic Ensembling, ECCV2022, Yi-Ting Chen et al.Improved KAIST Training Annotations provided by Zhang et al.Sanitized KAIST Training Annotations provided by Li et al.Improved KAIST Testing Annotations provided by Liu et al.Multispectral-Pedestrian Datasets and Annotations (If you think this is useful, please consider giving a star, thanks! We will continue to update this repository) Contents The main directions involved are Multispectral Pedestrian, RGB-T Vehicle Detection, RGB-T Crowd Counting, RGB-IR Person Re-identification. ![]() This repository collects RGB-T-Feature-Fusion methods (deep learning methods mainly), codes, and datasets.
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