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Visible-infrared image pairs provide complementary information, enhancing the reliability and robustness of object detection applications in real-world scenarios.
Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. , vol. 13, no. 4, pp. 600–612, 2004
2004
Earlier work this paper cites.
S. J. Krotosky and M. M. Trivedi, “On color-, infrared-, and multimodal-stereo approaches to pedestrian detection,” IEEE Trans. Intell. Transport. Syst. , vol. 8, no. 4, pp. 619–629, 2007
2007
Earlier work this paper cites.
Q. Huynh-Thu and M. Ghanbari, “Scope of validity of psnr in image/video quality assessment,” Elec. Letters , vol. 44, pp. 800–801, 2008
2008
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. J. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , 2014
2014
Earlier work this paper cites.
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in Proc. Int. Conf. Mach. Learn. (ICML) , 2015
2015
Earlier work this paper cites.
S. Hwang, J. Park, N. Kim, Y. Choi, and I. S. Kweon, “Multispectral pedestrian detection: Benchmark dataset and baselines,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2015
2015
Earlier work this paper cites.
S. Ren, K. He, R. B. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 39, pp. 1137–1149, 2015
2015
Earlier work this paper cites.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. E. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , 2015
2015
Earlier work this paper cites.
S. Zagoruyko and N. Komodakis, “Wide residual networks,” arXiv preprint arXiv 1605.07146 , 2016
2016
Earlier work this paper cites.
S. Razakarivony and F. Jurie, “Vehicle detection in aerial imagery: A small target detection benchmark,” J. Vis. Commun. Image Represent. , vol. 34, pp. 187–203, 2016
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Proc. Proc. Adv. Neural Inform. Process. Syst. (NeurIPS) , 2017
2017
Earlier work this paper cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Proc. Adv. Neural Inform. Process. Syst. (NeurIPS) , vol. 30, 2017
2017
Earlier work this paper cites.
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in Int. Conf. Comput. Vis. (ICCV) , 2017
2017
Earlier work this paper cites.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in IEEE Conf. Comput. Vis. Pattern Recog. (CVPR) , 2017
2017
Earlier work this paper cites.
C. Li, C. Zhu, J. Zhang, B. Luo, X. Wu, and J. Tang, “Learning local-global multi-graph descriptors for rgb-t object tracking,” IEEE Trans. Circuit Syst. Video Technol. , vol. 29, no. 10, pp. 2913–2926, 2018
2018
Earlier work this paper cites.
T. F., “Free flir thermal dataset for algorithm training,” 2018, https://www.flir.com/oem/adas/adas-dataset-form/
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” arXiv preprint arXiv 1804.02767 , 2018
2018
Earlier work this paper cites.
X. Li, J. Wu, Z. Lin, H. Liu, and H. Zha, “Recurrent squeeze-and-excitation context aggregation net for single image deraining,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , 2018, pp. 254–269
2018
Earlier work this paper cites.
R. Qian, R. T. Tan, W. Yang, J. Su, and J. Liu, “Attentive generative adversarial network for raindrop removal from a single image,” in IEEE Conf. Comput. Vis. Pattern Recog. (CVPR) , 2018
2018
Earlier work this paper cites.
J. Yao, Y. Zhang, F. Liu, and Y.-c. Liu, “Object detection based on decision level fusion,” in Chinese Automation Congress (CAC) . IEEE, 2019, pp. 3257–3262
2019
Earlier work this paper cites.
T. Wang, X. Yang, K. Xu, S. Chen, Q. Zhang, and R. W. H. Lau, “Spatial attentive single-image deraining with a high quality real rain dataset,” in IEEE Conf. Comput. Vis. Pattern Recog. (CVPR) , 2019
2019
Earlier work this paper cites.
X. Liu, M. Suganuma, Z. Sun, and T. Okatani, “Dual residual networks leveraging the potential of paired operations for image restoration,” in IEEE Conf. Comput. Vis. Pattern Recog. (CVPR) , 2019
2019
Earlier work this paper cites.
H. Zhang, E. Fromont, S. Lefevre, and B. Avignon, “Multispectral fusion for object detection with cyclic fuse-and-refine blocks,” in Proceedings of the IEEE International conference on image processing (ICIP) , 2020
2020
Earlier work this paper cites.
Z. Li, H.-M. Hu, W. Zhang, S. Pu, and B. Li, “Spectrum characteristics preserved visible and near-infrared image fusion algorithm,” IEEE Trans. Multimedia , vol. 23, pp. 306–319, 2020
2020
Earlier work this paper cites.
J. Ma, H. Zhang, Z. Shao, P. Liang, and H. Xu, “Ganmcc: A generative adversarial network with multiclassification constraints for infrared and visible image fusion,” IEEE Trans. Instrum. Meas. , vol. 70, pp. 1–14, 2020
2020
Earlier work this paper cites.
R. Li, R. T. Tan, and L.-F. Cheong, “All in one bad weather removal using architectural search,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2020, pp. 3175–3185
2020
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Proc. Adv. Neural Inform. Process. Syst. (NeurIPS) , vol. 33, pp. 6840–6851, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
H. Xu, J. Ma, Z. Le, J. Jiang, and X. Guo, “Fusiondn: A unified densely connected network for image fusion,” in Proc. AAAI Conf. Artif. Intell. (AAAI) , 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
G. Jocher, “YOLOv5 by Ultralytics,” May 2020
2020
Cited alongside, same era.
W.-T. Chen, H.-Y. Fang, J.-J. Ding, C.-C. Tsai, and S.-Y. Kuo, “Jstasr: Joint size and transparency-aware snow removal algorithm based on modified partial convolution and veiling effect removal,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , 2020
2020
Cited alongside, same era.
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , 2020, pp. 213–229
2020
Cited alongside, same era.
2020
Cited alongside, same era.
L. Tang, J. Yuan, H. Zhang, X. Jiang, and J. Ma, “Piafusion: A progressive infrared and visible image fusion network based on illumination aware,” Information Fusion , vol. 83, pp. 79–92, 2022
2022
Later among the works it cites.
J. Liu, X. Fan, Z. Huang, G. Wu, R. Liu, W. Zhong, and Z. Luo, “Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2022
2022
Later among the works it cites.
J. Xiao, X. Fu, A. Liu, F. Wu, and Z. Zha, “Image de-raining transformer,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 45, pp. 12 978–12 995, 2022
2022
Later among the works it cites.
L. Wang, X. Zhang, Z. Song, J. Bi, G. Zhang, H. Wei, L. Tang, L. Yang, J. Li, C. Jia et al. , “Multi-modal 3d object detection in autonomous driving: A survey and taxonomy,” IEEE Trans. Intell. Vehi. , vol. 8, no. 7, pp. 3781–3798, 2023
2023
Later among the works it cites.
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2021
Cited alongside, same era.
H. Zhang, E. Fromont, S. Lefevre, B. Avignon, and U. de Rennes, “Guided attentive feature fusion for multispectral pedestrian detection,” in Proc. IEEE Winter Conf. Appl. Comput. Vis. (WACV) , 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
X. Feng, W. Pei, Z. Jia, F. Chen, D. Zhang, and G. Lu, “Deep-masking generative network: A unified framework for background restoration from superimposed images,” IEEE Trans. Image Process. , vol. 30, pp. 4867–4882, 2021
2021
Cited alongside, same era.
K. Zhang, D. Li, W. Luo, and W. Ren, “Dual attention-in-attention model for joint rain streak and raindrop removal,” IEEE Trans. Image Process. , vol. 30, pp. 7608–7619, 2021
2021
Cited alongside, same era.
P. Dhariwal and A. Nichol, “Diffusion models beat gans on image synthesis,” Proc. Adv. Neural Inform. Process. Syst. (NeurIPS) , vol. 34, pp. 8780–8794, 2021
2021
Cited alongside, same era.
J. Choi, S. Kim, Y. Jeong, Y. Gwon, and S. Yoon, “Ilvr: Conditioning method for denoising diffusion probabilistic models,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV) , 2021
2021
Cited alongside, same era.
Y. Ling, Z. Zhong, Z. Luo, S. Li, and N. Sebe, “Bridge gap in pixel and feature level for cross-modality person re-identification,” IEEE Trans. Circuit Syst. Video Technol. , 2023
2023
Later among the works it cites.
R. Li, J. Xiang, F. Sun, Y. Yuan, L. Yuan, and S. Gou, “Multiscale cross-modal homogeneity enhancement and confidence-aware fusion for multispectral pedestrian detection,” IEEE Trans. Multimedia , vol. 26, pp. 852–863, 2023
2023
Later among the works it cites.
J. Liu, S. Li, H. Liu, R. Dian, and X. Wei, “A lightweight pixel-level unified image fusion network,” IEEE Trans. Neural Netw. Learn. Syst. , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
R. Zhang, L. Li, Q. Zhang, J. Zhang, L. Xu, B. Zhang, and B. Wang, “Differential feature awareness network within antagonistic learning for infrared-visible object detection,” IEEE Trans. Circuit Syst. Video Technol. , 2023
2023
Later among the works it cites.
Z. Zhao, H. Bai, J. Zhang, Y. Zhang, S. Xu, Z. Lin, R. Timofte, and L. Van Gool, “Cddfuse: Correlation-driven dual-branch feature decomposition for multi-modality image fusion,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2023
2023
Later among the works it cites.
C. Cheng, T. Xu, and X.-J. Wu, “Mufusion: A general unsupervised image fusion network based on memory unit,” Information Fusion , vol. 92, pp. 80–92, 2023
2023
Later among the works it cites.
F. Zhao, W. Zhao, and H. Lu, “Interactive feature embedding for infrared and visible image fusion,” IEEE Trans. Neural Netw. Learn. Syst. , 2023
2023
Later among the works it cites.
J. Li, B. Li, Y. Jiang, L. Tian, and W. Cai, “Mrfddgan: Multireceptive field feature transfer and dual discriminator-driven generative adversarial network for infrared and color visible image fusion,” IEEE Trans. Instrum. Meas. , vol. 72, pp. 1–28, 2023
2023
Later among the works it cites.
O. Özdenizci and R. Legenstein, “Restoring vision in adverse weather conditions with patch-based denoising diffusion models,” IEEE Trans. Pattern Anal. Mach. Intell. , pp. 1–12, 2023
2023
Later among the works it cites.
C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2023, pp. 7464–7475
2023
Later among the works it cites.
J. Glenn, C. Ayush, and Q. Jing, “YOLOv8 by Ultralytics,” July 2023
2023
Later among the works it cites.
Y. Cao, J. Bin, J. Hamari, E. Blasch, and Z. Liu, “Multimodal object detection by channel switching and spatial attention,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2023, pp. 403–411
2023
Later among the works it cites.
L. Tang, X. Xiang, H. Zhang, M. Gong, and J. Ma, “Divfusion: Darkness-free infrared and visible image fusion,” Inform. Fusion , vol. 91, pp. 477–493, 2023
2023
Later among the works it cites.
A. Wu, C. Lin, and W.-S. Zheng, “Asymmetric mutual learning for unsupervised transferable visible-infrared re-identification,” IEEE Trans. Circuit Syst. Video Technol. , 2024
2024
Closest in time.
2024
Closest in time.
Y. Zeng, T. Liang, Y. Jin, and Y. Li, “Mmi-det: Exploring multi-modal integration for visible and infrared object detection,” IEEE Trans. Circuit Syst. Video Technol. , 2024
2024
Closest in time.
Y. Bai, M. Gao, S. Li, P. Wang, N. Guan, H. Yin, and Y. Yan, “Ibfusion: An infrared and visible image fusion method based on infrared target mask and bimodal feature extraction strategy,” IEEE Trans. Multimedia , 2024
2024
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2024
Closest in time.
2024
Closest in time.
T. Wang, K. Zhang, Z. Shao, W. Luo, B. Stenger, T. Lu, T.-K. Kim, W. Liu, and H. Li, “Gridformer: Residual dense transformer with grid structure for image restoration in adverse weather conditions,” Int. J. Comput. Vis. , vol. 132, no. 10, pp. 4541–4563, 2024
2024
Closest in time.
A. Wang, H. Chen, L. Liu, K. Chen, Z. Lin, J. Han et al. , “Yolov10: Real-time end-to-end object detection,” Proc. Adv. Neural Inform. Process. Syst. (NeurIPS) , vol. 37, pp. 107 984–108 011, 2024
2024
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2024
Closest in time.
X. Li, J. Liu, Z. Chen, Y. Zou, L. Ma, X. Fan, and R. Liu, “Contourlet residual for prompt learning enhanced infrared image super-resolution,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , 2025, pp. 270–288
2025
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