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A collection of deep learning based RGB-T-Fusion methods, codes, and datasets. The main directions involved are Multispectral Pedestrian Detection, RGB-T Aerial Object Detection, RGB-T Semantic Segmentation, RGB-T Crowd Counting, RGB-T Fusion Tracking.

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Awesome RGB-T Fusion Awesome -RGBT-red

A collection of deep learning based RGB-T-Fusion methods, codes, and datasets.
The main directions involved are Multispectral Pedestrian Detection, RGB-T Aerial Object Detection, RGB-T Semantic Segmentation, RGB-T Crowd Counting, RGB-T Fusion Tracking.
Feel free to star and fork! Keep updating....🚀

Some News: 🆕

👀 2024.05.23 Add one dataset in RGB-T Aerial Object Detection.

💎 2024.04.23 Add more papers about RGB-T Salient Object Detection.

👀 2024.03.17 Add one CVPR paper.

💎 2024.03.15 Add new content about RGB-T Semantic segmentation.

👀 2024.03.12 Add one our paper and one CVPR paper.

Contents

  1. Multispectral Pedestrian Detection
  2. RGB-T Aerial Object Detection
  3. RGB-T Semantic Segmentation
  4. RGB-T Salient Object Detection
  5. RGB-T Crowd Counting
  6. RGB-T Fusion Tracking

Multispectral Pedestrian Detection

Datasets and Annotations

KAIST dataset, CVC-14 dataset, FLIR dataset, FLIR-aligned dataset, Utokyo, LLVIP dataset, M3FD dataset

Tools

Papers

Fusion Architecture

  1. UniRGB-IR: A Unified Framework for Visible-Infrared Downstream Tasks via Adapter Tuning, arxiv 2024, Maoxun Yuan et al., [PDF][Code]
  2. M2FNet: Mask-guided Multi-level Fusion for RGB-T Pedestrian Detection, TMM 2024, Xiangyang Li et al., [PDF]
  3. Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection, CVPR 2024, Taeheon Kim et al. [PDF][Code]
  4. Removal and Selection: Improving RGB-Infrared Object Detection via Coarse-to-Fine Fusion, arxiv 2024, Tianyi Zhao et al. [PDF][Code][知乎]
  5. ICAFusion: Iterative cross-attention guided feature fusion for multispectral object detection, Pattern Recognition 2024, Shen Jifeng et al. [PDF][Code]
  6. Improving RGB-infrared object detection with cascade alignment-guided transformer, Information Fusion 2024, Maoxun Yuan et al. [PDF]
  7. Multispectral Object Detection via Cross-Modal Conflict-Aware Learning, ACM MM 2023, Xiao He et al. [PDF][Code]
  8. Stabilizing Multispectral Pedestrian Detection with Evidential Hybrid Fusion, TCSVT 2023, Li Qing et al. [PDF]
  9. Multimodal Object Detection by Channel Switching and Spatial Attention, CVPRW 2023, Yue Cao et al. [PDF]
  10. Multi-Modal Feature Pyramid Transformer for RGB-Infrared Object Detection, TITS 2023, Yaohui Zhu et al. [PDF]
  11. Multiscale Cross-modal Homogeneity Enhancement and Confidence-aware Fusion for Multispectral Pedestrian Detection, TMM 2023, Ruimin Li et al. [PDF][Code]
  12. HAFNet: Hierarchical Attentive Fusion Network for Multispectral Pedestrian Detection, Remote Sensing 2023, Peiran Peng et al. [PDF]
  13. Multimodal Object Detection via Probabilistic Ensembling, ECCV2022, Yi-Ting Chen et al. [PDF][Code]
  14. Learning a Dynamic Cross-Modal Network for Multispectral Pedestrian Detection, ACM Multimedia 2022, Jin Xie et al. [PDF]
  15. Confidence-aware Fusion using Dempster-Shafer Theory for Multispectral Pedestrian Detection, TMM 2022, Qing Li et al. [PDF]
  16. Attention-Guided Multi-modal and Multi-scale Fusion for Multispectral Pedestrian Detection, PRCV 2022, Wei Bao et al. [PDF]
  17. Improving RGB-Infrared Pedestrian Detection by Reducing Cross-Modality Redundancy, ICIP2022, Qingwang Wang et al. [PDF]
  18. Spatio-contextual deep network-based multimodal pedestrian detection for autonomous driving, IEEE Transactions on Intelligent Transportation Systems, Kinjal Dasgupta et al. [PDF]
  19. Adopting the YOLOv4 Architecture for Low-LatencyMultispectral Pedestrian Detection in Autonomous Driving, Sensors 2022, Kamil Roszyk et al. [PDF]
  20. Deep Active Learning from Multispectral Data Through Cross-Modality Prediction Inconsistency, ICIP2021, Heng Zhang et al.[PDF]
  21. Attention Fusion for One-Stage Multispectral Pedestrian Detection, Sensors 2021, Zhiwei Cao et al. [PDF]
  22. 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]
  23. 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]
  24. Guided Attentive Feature Fusion for Multispectral Pedestrian Detection, WACV 2021, Heng Zhang et al. [PDF]
  25. Anchor-free Small-scale Multispectral Pedestrian Detection, BMVC 2020, Alexander Wolpert et al. [PDF][Code]
  26. Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks, ICIP 2020, Heng Zhang et al. [PDF]
  27. Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems, ECCV 2020, Kailai Zhou et al. [PDF][Code]
  28. Anchor-free Small-scale Multispectral Pedestrian Detection, BMVC 2020, Alexander Wolpert et al. [PDF][Code]
  29. Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection, ICCV 2019, Lu Zhang et al. [PDF][Code]
  30. 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]
  31. Cross-modality interactive attention network for multispectral pedestrian detection, Information Fusion 2019, Lu Zhang et al.[PDF][Code]
  32. Pedestrian detection with unsupervised multispectral feature learning using deep neural networks, Information Fusion 2019, Cao, Yanpeng et al.[PDF]
  33. Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation, BMVC 2018, Chengyang Li et al.[PDF][Code][Project Link]
  34. Unified Multi-spectral Pedestrian Detection Based on Probabilistic Fusion Networks, Pattern Recognition 2018, Kihong Park et al.[PDF]
  35. Multispectral Deep Neural Networks for Pedestrian Detection, BMVC 2016, Jingjing Liu et al.[PDF][Code]
  36. Multispectral Pedestrian Detection Benchmark Dataset and Baseline, 2015, Soonmin Hwang et al.[PDF][Code]

Pixel-level Fusion for Detection

  1. E2E-MFD: Towards End-to-End Synchronous Multimodal Fusion Detection, arxiv 2024, Jiaqing Zhang et al. [PDF][Code]
  2. Multi-modal Gated Mixture of Local-to-Global Experts for Dynamic Image Fusion, ICCV 2023, Yiming Sun et al.[PDF][Code]
  3. MetaFusion : Infrared and Visible Image Fusion via Meta-Feature Embedding from Object Detection, CVPR 2023, Wenda Zhao et al. [PDF][Code]
  4. Locality guided cross-modal feature aggregation and pixel-level fusion for multispectral pedestrian detection, Information Fusion 2022, Yanpeng Cao et al. [PDF]
  5. Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection, CVPR 2022, Jinyuan Liu et al.[PDF][Code]
  6. DetFusion: A Detection-driven Infrared and Visible Image Fusion Network, ACM Multimedia 2022, Yiming Sun et al. [PDF][Code]

Illumination Aware

  1. Illumination-Guided RGBT Object Detection With Inter- and Intra-Modality Fusion, IEEE Transactions on Instrumentation and Measurement 2023, Yan Zhang et al. [PDF][Code]
  2. IGT: Illumination-guided RGB-T object detection with transformers, Knowledge-Based Systems 2023, Keyu Chen et al. [PDF]
  3. Task-conditioned Domain Adaptation for Pedestrian Detection in Thermal Imagery, ECCV 2020, My Kieu et al. [PDF][Code]
  4. Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection, Information Fusion 2019, Dayan Guan et al.[PDF][Code]
  5. Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection, Pattern Recognition 2018, Chengyang Li et al.[PDF][Code]

Feature Alignment

  1. C2Former: Calibrated and Complementary Transformer for RGB-Infrared Object Detection, TGRS 2024, Maoxun Yuan et al. [PDF][Code]
  2. Cross-Modality Proposal-guided Feature Mining for Unregistered RGB-Thermal Pedestrian Detection, TMM 2024, Chao Tian et al. [PDF]
  3. Attentive Alignment Network for Multispectral Pedestrian Detection, ACM MM 2023, Nuo Chen et al. [PDF]
  4. Towards Versatile Pedestrian Detector with Multisensory-Matching and Multispectral Recalling Memory, AAAI2022, Jung Uk Kim et al. [PDF]
  5. Mlpd: Multi-label pedestrian detector in multispectral domain, IEEE Robotics and Automation Letters 2021, Jiwon Kim et al. [PDF]
  6. Weakly Aligned Feature Fusion for Multimodal Object Detection, ITNNLS 2021, Lu Zhang et al. [PDF]
  7. Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems, ECCV 2020, Kailai Zhou et al. [PDF][Code]
  8. Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection, ICCV 2019, Lu Zhang et al. [PDF] [Code]
  9. Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation, BMVC 2018, Chengyang Li et al. [PDF] [Code]

Mono-Modality

  1. TIRDet: Mono-Modality Thermal InfraRed Object Detection Based on Prior Thermal-To-Visible Translation, ACM MM 2023, Zeyu Wang et al. [PDF][Code]
  2. Towards Versatile Pedestrian Detector with Multisensory-Matching and Multispectral Recalling Memory, AAAI 2022, Kim et al. [PDF]
  3. Robust Thermal Infrared Pedestrian Detection By Associating Visible Pedestrian Knowledge, ICASSP 2022, Sungjune Park et al. [PDF]
  4. Low-cost Multispectral Scene Analysis with Modality Distillation, Zhang Heng et al. [PDF]
  5. Task-conditioned Domain Adaptation for Pedestrian Detection in Thermal Imagery, ECCV 2020, My Kieu et al. [PDF][Code]
  6. 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]

Domain Adaptation

  1. D3T: Distinctive Dual-Domain Teacher Zigzagging Across RGB-Thermal Gap for Domain-Adaptive Object Detection, CVPR 2024, Dinh Phat Do et al., [PDF][Code]
  2. Unsupervised Domain Adaptation for Multispectral Pedestrian Detection, CVPR 2019 Workshop , Dayan Guan et al. [PDF] [Code]
  3. Pedestrian detection with unsupervised multispectral feature learning using deep neural networks, Information Fusion 2019, Y. Cao et al. Information Fusion 2019, [PDF] [Code]
  4. Learning crossmodal deep representations for robust pedestrian detection, CVPR 2017, D. Xu et al.[PDF][Code]

RGB-T Aerial Object Detection

Datasets

DVTOD: misaligned [link]

DroneVehicle: partially aligned [link]

VEDAI: strictly aligned [link]

Papers

  1. Misaligned Visible-Thermal Object Detection: A Drone-based Benchmark and Baseline, TIV 2024, Kechen Song, [PDF][Code]
  2. C2Former: Calibrated and Complementary Transformer for RGB-Infrared Object Detection, TGRS 2024, Maoxun Yuan et al. [PDF][Code]
  3. ICAFusion: Iterative cross-attention guided feature fusion for multispectral object detection, Pattern Recognition 2024, Shen Jifeng et al. [PDF][Code]
  4. Improving RGB-infrared object detection with cascade alignment-guided transformer, Information Fusion 2024, Maoxun Yuan et al. [PDF]
  5. Multispectral Object Detection via Cross-Modal Conflict-Aware Learning, ACM MM 2023, Xiao He et al. [PDF][Code]
  6. LRAF-Net: Long-Range Attention Fusion Network for Visible–Infrared Object Detection, TNNLS 2023, Haolong Fu et al. [PDF]
  7. GF-Detection: Fusion with GAN of Infrared and Visible Images for Vehicle Detection at Nighttime, Remote Sensing 2022, Peng Gao et al. [PDF]
  8. Cross-modality attentive feature fusion for object detection in multispectral remote sensing imagery, Pattern Recognition, Qingyun Fang et al. [PDF]
  9. Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection, ECCV 2022, Maoxun Yuan et al. [PDF]
  10. Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning, TCSVT 2022, Yiming Sun [PDF][Code]
  11. Improving RGB-Infrared Object Detection by Reducing Cross-Modality Redundancy, Remote Sensing 2022, Qingwang Wang et al. [PDF]

RGB-T-Semantic-segmentation

Datasets

MFNet dataset, PST900 dataset, SemanticRT dataset

Papers

  1. Context-Aware Interaction Network for RGB-T Semantic Segmentation, TMM 2024, Ying Lv et al. [PDF][Code]
  2. CACFNet: Cross-Modal Attention Cascaded Fusion Network for RGB-T Urban Scene Parsing, TIV 2023, Wujie Zhou et al., [PDF]
  3. On Exploring Shape and Semantic Enhancements for RGB-X Semantic Segmentation, TIV 2023, Yuanjian Yang et al., [PDF][Code]
  4. Complementarity-aware cross-modal feature fusion network for RGB-T semantic segmentation, PR 2023, Wei Wu et al., [PDF]
  5. MMSMCNet: Modal Memory Sharing and Morphological Complementary Networks for RGB-T Urban Scene Semantic Segmentation, TCSVT 2023, Wujie Zhou et al. [PDF][Code]
  6. SGFNet: Semantic-Guided Fusion Network for RGB-Thermal Semantic Segmentation, TCSVT 2023, Yike Wang et al., [PDF][Code]
  7. DBCNet: Dynamic Bilateral Cross-Fusion Network for RGB-T Urban Scene Understanding in Intelligent Vehicles, TCYB 2023, Wujie Zhou et al., [PDF]
  8. Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks, RAL 2023, Mingjian Liang et al., [PDF][Code]
  9. Embedded Control Gate Fusion and Attention Residual Learning for RGB–Thermal Urban Scene Parsing, TITS 2023, Wujie Zhou et al., [PDF]
  10. UTFNet: Uncertainty-Guided Trustworthy Fusion Network for RGB-Thermal Semantic Segmentation, GRSL 2023, Qingwang Wang et al., [PDF][Code]
  11. Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning, arxiv 2023, Shaohua Dong et al., [PDF][Code]
  12. A RGB-Thermal Image Segmentation Method Based on Parameter Sharing and Attention Fusion for Safe Autonomous Driving, TITS 2023, Guofa Li et al., [PDF]
  13. SFAF-MA: Spatial Feature Aggregation and Fusion With Modality Adaptation for RGB-Thermal Semantic Segmentation, TIM 2023, Xunjie He et al., [PDF][Code]
  14. Edge-aware guidance fusion network for RGB–thermal scene parsing, AAAI 2022, Wujie Zhou et al., [PDF][Code]
  15. CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers, TITS 2022, Jiaming Zhang et al., [PDF][Code]
  16. RGB-T Semantic Segmentation with Location, Activation, and Sharpening, TCSVT 2022, Gongyang Li et al., [PDF][Code]
  17. A Feature Divide-and-Conquer Network for RGB-T Semantic Segmentation, TCSVT 2022, Shenlu Zhao et al., [PDF]
  18. CCAFFMNet: Dual-spectral semantic segmentation network with channel-coordinate attention feature fusion module, Neurocomputing 2022, [PDF]
  19. CGFNet: cross-guided fusion network for RGB-thermal semantic segmentation, The Visual Computer 2022, Yanping Fu et al., [PDF]
  20. MTANet: Multitask-Aware Network with Hierarchical Multimodal Fusion for RGB-T Urban Scene Understanding, TIV 2022, Wujie Zhou et al., [PDF][Code]
  21. GCNet: Grid-Like Context-Aware Network for RGB-Thermal Semantic Segmentation, Neurocomputing 2022, Jinfu Liu et al., [PDF]
  22. ABMDRNet: Adaptive-weighted Bi-directional Modality Difference Reduction Network for RGB-T Semantic Segmentation, CVPR 2021, Qiang Zhang et al., [PDF]
  23. GMNet: Graded-Feature Multilabel-Learning Network for RGB-Thermal Urban Scene Semantic Segmentation, TIP 2021, Wujie Zhou et al., [PDF][Code]
  24. MFFENet: Multiscale Feature Fusion and Enhancement Network for RGBThermal Urban Road Scene Parsing, TMM 2021, Wujie Zhou et al., [PDF]
  25. FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation, IROS 2021, Fuqin Deng et al., [PDF][Code]
  26. HeatNet: Bridging the Day-Night Domain Gap in Semantic Segmentation with Thermal Images, IROS 2021, Johan Vertens et al., [PDF]
  27. Robust semantic segmentation based on RGB-thermal in variable lighting scenes, Measurement 2021, Zhifeng Guo et al., [PDF]
  28. FuseSeg: Semantic segmentation of urban scenes based on RGB and thermal data fusion, TASE 2020, Yuxiang Sun et al., [PDF]
  29. PST900: RGB-Thermal Calibration, Dataset and Segmentation Network, ICRA 2020, Shreyas S. Shivakumar et al., [PDF][Code]
  30. RTFNet: RGB-Thermal Fusion Network for Semantic Segmentation of Urban Scenes, RAL 2019, Yuxiang Sun et al., [PDF][Code]
  31. MFNet: Towards Real-Time Semantic Segmentation for Autonomous Vehicles with Multi-Spectral Scenes, IROS 2019, Qishen Ha et al., [PDF][Code]

RGB-T Salient Object Detection

Datasets

VT821 Dataset [PDF][link], VT1000 Dataset [PDF][link], VT5000 Dataset [PDF][link[9yqv]], VI-RGBT1500 Dataset[PDF][link]

Papers

  1. VST++: Efficient and Stronger Visual Saliency Transformer, TPAMI 2024, Nian Liu et al., [PDF][Code]
  2. UniTR: A Unified TRansformer-based Framework for Co-object and Multi-modal Saliency Detection, TMM 2024, Ruohao Guo et al., [PDF]
  3. Learning Adaptive Fusion Bank for Multi-modal Salient Object Detection, TCSVT 2024, Kunpeng Wang et al., [PDF]
  4. TMNet: Triple-modal interaction encoder and multi-scale fusion decoder network for V-D-T salient object detection, PR 2024, Bin Wan et al., [PDF]
  5. Weighted Guided Optional Fusion Network for RGB-T Salient Object Detection, TOMM 2024, Jie Wang et al., [PDF][Code]
  6. Position-Aware Relation Learning for RGB-Thermal Salient Object Detection, TIP 2023, Heng Zhou et al., [PDF]
  7. WaveNet: Wavelet Network With Knowledge Distillation for RGB-T Salient Object Detection, TIP 2023, Wujie Zhou et al., [PDF][Code]
  8. LSNet: Lightweight Spatial Boosting Network for Detecting Salient Objects in RGB-Thermal Images, TIP 2023, Wujie Zhou et al., [PDF][Code]
  9. CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detection, TIP 2023, Youwei Pang et al., [PDF][Code]
  10. Glass Segmentation With RGB-Thermal Image Pairs, TIP 2023, Dong Huo et al., [PDF][Code]
  11. MFFNet: Multi-modal Feature Fusion Network for V-D-T Salient Object Detection, TMM 2023, Bin Wan et al., [PDF]
  12. LFTransNet: Light Field Salient Object Detection via a Learnable Weight Descriptor, TCSVT 2023, Zhengyi Liu et al., [PDF][Code]
  13. Multiple Graph Affinity Interactive Network and a Variable Illumination Dataset for RGBT Image Salient Object Detection, TCSVT 2023, Kechen Song et al., [PDF][Code]
  14. Cross-Modality Double Bidirectional Interaction and Fusion Network for RGB-T Salient Object Detection, TCSVT 2023, Zhengxuan Xie et al., [PDF]
  15. DBCNet: Dynamic Bilateral Cross-Fusion Network for RGB-T Urban Scene Understanding in Intelligent Vehicles, TCYB 2023, [PDF]
  16. Frequency-aware feature aggregation network with dual-task consistency for RGB-T salient object detection, PR 2023, Heng Zhou et al. [PDF]
  17. Cross-modal co-feedback cellular automata for RGB-T saliency detection, PR 2023, Yu Pang et al., [PDF]
  18. An interactively reinforced paradigm for joint infrared-visible image fusion and saliency object detection, Information Fusion 2023, Di Wang et al., [PDF][Code]
  19. Thermal images-aware guided early fusion network for cross-illumination RGB-T salient object detection, EAAI 2023, Han Wang et al., [PDF][Code]
  20. Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks, RAL 2023, Mingjian Liang et al., [PDF][Code]
  21. MENet: Lightweight multimodality enhancement network for detecting salient objects in RGB-thermal images, Neurocomputing 2023, Junyi Wu et al., [PDF]
  22. Feature aggregation with transformer for RGB-T salient object detection, Neurocomputing 2023, Ping Zhang et al., [PDF][Code]
  23. Texture-Guided Saliency Distilling for Unsupervised Salient Object Detection, CVPR2023， Huajun Zhou et al., [PDF][Code]
  24. Saliency Prototype for RGB-D and RGB-T Salient Object Detection, ACM MM 2023, Zihao Zhang et al., [PDF][Code]
  25. ADNet: An Asymmetric Dual-Stream Network for RGB-T Salient Object Detection, ACM MM 2023, Yaqun Fang et al., [PDF]
  26. Scribble-Supervised RGB-T Salient Object Detection, ICME 2023, Zhengyi Liu et al., [PDF][Code]
  27. Feature Enhancement and Fusion for RGB-T Salient Object Detection, ICIP 2023, Fengming Sun et al., [PDF]
  28. Weakly Alignment-free RGBT Salient Object Detection with Deep Correlation Network, TIP 2022, Zhengzheng Tu et al., [PDF][Code]
  29. Cross-modal co-feedback cellular automata for RGB-T saliency detection, PR 2022, Yu Pang et al., [PDF]
  30. RGBT Salient Object Detection: A Large-Scale Dataset and Benchmark, TMM 2022, Zhengzheng Tu et al., [PDF]
  31. Does Thermal really always matter for RGB-T salient object detection, TMM 2022, Runmin Cong et al., [PDF][Code]
  32. TCNet: Co-Salient Object Detection via Parallel Interaction of Transformers and CNNs, TCSVT 2023, Yanliang Ge et al., [PDF][Code]
  33. Modality-Induced Transfer-Fusion Network for RGB-D and RGB-T Salient Object Detection, TCSVT 2022, Gang Chen et al., [PDF]
  34. Cross-Collaborative Fusion-Encoder Network for Robust RGB-Thermal Salient Object Detection, TCSVT 2022, Guibiao Liao et al., [PDF][Code]
  35. CGMDRNet: Cross-Guided Modality Difference Reduction Network for RGB-T Salient Object Detection, TCSVT 2022, Gang Chen et al., [PDF]
  36. RGB-T Semantic Segmentation With Location, Activation, and Sharpening, TCSVT 2022, Gongyang Li et al., [PDF][Code]
  37. HRTransNet: HRFormer-Driven Two-Modality Salient Object Detection, TCSVT 2022, Bin Tang et al., [PDF][Code]
  38. Asymmetric cross-modal activation network for RGB-T salient object detection, KBS 2022, Chang Xu et al., [PDF][Code]
  39. Three-stream interaction decoder network for RGB-thermal salient object detection, KBS 2022, Fushuo Huo et al., [PDF][Code]
  40. Real-time One-stream Semantic-guided Refinement Network for RGB-Thermal Salient Object Detection, TIM 2022, Fushuo Huo et al., [PDF][Code]
  41. Multi-modal Interactive Attention and Dual Progressive Decoding Network for RGB-D/T Salient Object Detection, Neurocomputing 2022, Yanhua Liang et al., [PDF][Code]
  42. PSNet: Parallel symmetric network for RGB-T salient object detection, Neurocomputing 2022, Hongbo Bi et al., [PDF]
  43. Mirror Complementary Transformer Network for RGB-thermal Salient Object Detection, arxiv 2022, [PDF][Code]
  44. Bimodal Information Fusion Network for Salient Object Detection based on Transformer, PRML 2022, Zhuo Wang et al., [PDF]
  45. Multi-Interactive Dual-Decoder for RGB-Thermal Salient Object Detection, TIP 2021, Zhengzheng Tu et al., [PDF][Code]
  46. ECFFNet: Effective and Consistent Feature Fusion Network for RGB-T Salient Object Detection, TCSVT 2021, Wujie Zhou et al., [PDF]
  47. SwinNet: Swin Transformer drives edge-aware RGB-D and RGB-T salient object detection, TCSVT 2021, [PDF][Code]
  48. Unified Information Fusion Network for Multi-Modal RGB-D and RGB-T Salient Object Detection, TCSVT 2021, Wei Gao et al., [PDF]
  49. Multi-graph Fusion and Learning for RGBT Image Saliency Detection, TCSVT 2021, Liming Huang et al., [PDF]
  50. CGFNet: Cross-Guided Fusion Network for RGB-T Salient Object Detection, TCSVT 2021, Jie Wang et al., [PDF][Code]
  51. Efficient Context-Guided Stacked Refinement Network for RGB-T Salient Object Detection, TCSVT 2021, Fushuo Huo et al., [PDF][Code]
  52. RGB-T Salient Object Detection via Fusing Multi-Level CNN Features, TIP 2020, Qiang Zhang et al., [PDF][Code]
  53. Revisiting Feature Fusion for RGB-T Salient Object Detection, TCSCT 2020, Qiang Zhang et al., [PDF][Code]
  54. Deep Domain Adaptation Based Multi-Spectral Salient Object Detection, TMM 2020, Shaoyue Song et al., [PDF]
  55. Abiotic Stress Prediction from RGB-T Images of Banana Plantlets, ECCV 2020, Sagi Levanon et al., [PDF]
  56. Multi-Spectral Salient Object Detection by Adversarial Domain Adaptation, AAAI 2020, Shaoyue Song et al.[PDF]
  57. Deep Domain Adaptation Based Multi-spectral Salient Object Detection, TMM 2020, Shaoyue Song et al., [PDF]
  58. RGB-T Image Saliency Detection via Collaborative Graph Learning, TMM 2019, Zhengzheng Tu et al., [PDF][Code]
  59. RGBT Salient Object Detection: Benchmark and A Novel Cooperative Ranking Approach, TCSVT 2019, Jin Tang et al., [PDF][Code]
  60. M3S-NIR: Multi-Modal Multi-Scale Noise-Insensitive Ranking for RGB-T Saliency Detection, MIPR 2019, Zhengzheng Tu et al., [PDF][Code]
  61. RGB-T Saliency Detection Benchmark: Dataset, Baselines, Analysis and a Novel Approach, IGTA 2018, Guizhao Wang et al., [PDF][Code]
  62. Learning Multiscale Deep Features and SVM Regressors for Adaptive RGB-T Saliency Detection, ISCID 2017, Yunpeng Ma et al., [PDF]

RGB-T Crowd Counting

Datasets

RGBT-CC[link], DroneRGBT [link]

Papers

Domain Adaptation

  1. RGB-T Crowd Counting from Drone: A Benchmark and MMCCN Network, ACCV2020, Tao Peng et al. [PDF][Code]

Fusion Architecture

  1. CCANet: A Collaborative Cross-modal Attention Network for RGB-D Crowd Counting, TMM2023, Yanbo Liu et al. [PDF]
  2. MC3Net: Multimodality Cross-Guided Compensation Coordination Network for RGB-T Crowd Counting, TITS 2023, Wujie Zhou et al. [PDF]
  3. RGB-T Multi-Modal Crowd Counting Based on Transformer, BMVC 2022, Zhengyi Liu et al. [PDF]
  4. Spatio-channel Attention Blocks for Cross-modal Crowd Counting, ACCV2022, Youjia Zhang et al. [PDF]
  5. DEFNet: Dual-Branch Enhanced Feature Fusion Network for RGB-T Crowd Counting, TITS 2022, Zhou, Wujie et al. [PDF]
  6. MAFNet: A Multi-Attention Fusion Network for RGB-T Crowd Counting, arxiv2022, Pengyu Chen et al. [PDF]
  7. Multimodal Crowd Counting with Mutual Attention Transformers, ICME 2022, Wu, Zhengtao et al. [PDF]
  8. Conditional RGB-T Fusion for Effective Crowd Counting, ICIP 2022, Esha Pahwa et al. [PDF]
  9. Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting, CVPR2021, Lingbo Liu et al. [PDF][Code]

RGB-T Fusion Tracking

Datasets

GTOT [PDF][link], RGBT234 Dataset [PDF][link], LasHeR Dataset [PDF][link]

Papers

  1. MTNet: Learning Modality-aware Representation with Transformer for RGBT Tracking, ICME 2023, Ruichao Hou et al. [PDF]
  2. Visual Prompt Multi-Modal Tracking, CVPR 2023, Jiawen Zhu et al. [PDF][Code]
  3. Efficient RGB-T Tracking via Cross-Modality Distillation, CVPR 2023, Zhang, Tianlu et al. [PDF]
  4. Bridging Search Region Interaction with Template for RGB-T Tracking, Hui, CVPR2023, Tianrui et al.[PDF][Code]
  5. Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking, TIP2021, Zhang, Pengyu et al. [PDF] TIP 2022, Tu, Zhengzheng et al., [PDF]
  6. RGBT tracking via reliable feature configuration, SCIS 2022, Tu, Zhengzheng et al. [PDF]
  7. Attribute-Based Progressive Fusion Network for RGBT Tracking, AAAI 2022, Xiao Yun et al. [PDF][Code]
  8. Dense Feature Aggregation and Pruning for RGBT Tracking, ACM Multimedia 2022, Yabin Zhu et al. [PDF]
  9. Prompting for Multi-Modal Tracking, ACM Multimedia 2022, Jinyu Yang et al. [PDF]
  10. Learning Adaptive Attribute-Driven Representation for Real-Time RGB-T Tracking, IJCV 2021, Zhang, Pengyu et al. [PDF]
  11. Quality-Aware Feature Aggregation Network for Robust RGBT Tracking, TIV 2021, Zhu, Yabin, [PDF]
  12. Challenge-Aware RGBT Tracking, ECCV 2020, Li Chenglong et al. [PDF]
  13. Object fusion tracking based on visible and infrared images: A comprehensive review, Information Fusion 2020, Zhang, Xingchen et al., [PDF]
  14. RGB-T object tracking: Benchmark and baseline, Pattern Recognition 2019, Li, Chenglong et al., [PDF]
  15. Cross-Modal Pattern-Propagation for RGB-T Tracking, CVPR2020, Chaoqun Wang et al., [PDF]

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A collection of deep learning based RGB-T-Fusion methods, codes, and datasets. The main directions involved are Multispectral Pedestrian Detection, RGB-T Aerial Object Detection, RGB-T Semantic Segmentation, RGB-T Crowd Counting, RGB-T Fusion Tracking.

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deep-learning papers feature-fusion rgbt-tracking multispectral-pedestrian-detection rgb-t rgbt-salient-object-detection rgbt-semantic-segmentation rgbt-pedestrian-detection rgbt-crowd-counting rgbt-vehicle-detection rgb-t-aerial-object-detection

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