| Densefuse: A Fusion Approach for Infrared and Visible Images H Li, X Wu IEEE Transactions on Image Processing 28 (5), 2614-2623, 2019 | 1073 | 2019 |
| The seventh visual object tracking vot2019 challenge results M Kristan, J Matas, A Leonardis, M Felsberg, R Pflugfelder, ... Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 510 | 2019 |
| Infrared and Visible Image Fusion using a Deep Learning Framework H Li, X Wu, J Kittler 2018 24th International Conference on Pattern Recognition (ICPR), 2705 - 2710, 2018 | 498 | 2018 |
| RFN-Nest: An end-to-end residual fusion network for infrared and visible images H Li, XJ Wu, J Kittler Information Fusion 73, 72-86, 2021 | 415 | 2021 |
| NestFuse: An Infrared and Visible Image Fusion Architecture based on Nest Connection and Spatial/Channel Attention Models H Li, XJ Wu, T Durrani IEEE Transactions on Instrumentation and Measurement 69 (12), 9645-9656, 2020 | 373 | 2020 |
| MDLatLRR: A novel decomposition method for infrared and visible image fusion H Li, XJ Wu, J Kittler IEEE Transactions on Image Processing 29, 4733-4746, 2020 | 356 | 2020 |
| The eighth visual object tracking VOT2020 challenge results M Kristan, A Leonardis, J Matas, M Felsberg, R Pflugfelder, ... Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020 …, 2020 | 284 | 2020 |
| Infrared and visible image fusion with ResNet and zero-phase component analysis H Li, X Wu, TS Durrani Infrared Physics & Technology 102, 103039, 2019 | 284 | 2019 |
| Infrared and visible image fusion using latent low-rank representation H Li, XJ Wu arXiv preprint arXiv:1804.08992, 2018 | 174 | 2018 |
| Multi-focus image fusion using dictionary learning and low-rank representation H Li, XJ Wu Image and Graphics: 9th International Conference, ICIG 2017, Shanghai, China …, 2017 | 105 | 2017 |
| The ninth visual object tracking vot2021 challenge results M Kristan, J Matas, A Leonardis, M Felsberg, R Pflugfelder, ... Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 100 | 2021 |
| SwinFuse: A Residual Swin Transformer Fusion Network for Infrared and Visible Images Z Wang, Y Chen, W Shao, H Li, L Zhang IEEE Transactions on Instrumentation and Measurement 71, 5016412, 2022 | 88 | 2022 |
| LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images H Li, T Xu, XJ Wu, J Lu, J Kittler IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (9), 11040 …, 2023 | 41 | 2023 |
| MSDNet for medical image fusion X Song, XJ Wu, H Li Image and Graphics: 10th International Conference, ICIG 2019, Beijing, China …, 2019 | 35 | 2019 |
| The tenth visual object tracking vot2022 challenge results M Kristan, A Leonardis, J Matas, M Felsberg, R Pflugfelder, ... European Conference on Computer Vision, 431-460, 2022 | 30 | 2022 |
| Exploring fusion strategies for accurate RGBT visual object tracking Z Tang, T Xu, H Li, XJ Wu, XF Zhu, J Kittler Information Fusion 99, 101881, 2023 | 22 | 2023 |
| Multi-focus noisy image fusion using low-rank representation H Li, XJ Wu, T Durrani arXiv preprint arXiv:1804.09325, 2018 | 19 | 2018 |
| UMFA: a photorealistic style transfer method based on U-Net and multi-layer feature aggregation D Rao, XJ Wu, H Li, J Kittler, T Xu Journal of electronic imaging 30 (5), 053013-053013, 2021 | 7 | 2021 |
| CrossFuse: A novel cross attention mechanism based infrared and visible image fusion approach H Li, XJ Wu Information Fusion 103, 102147, 2024 | 4 | 2024 |
| A medical image fusion method based on mdlatlrrv2 X Song, XJ Wu, H Li arXiv preprint arXiv:2206.15179, 2022 | 4 | 2022 |
Xiao-Jun WuSchool of Artificial Intelligence and Computer Science, Jiangnan UniversityVerified email at jiangnan.edu.cn
