Fetching the paper…
Reading the bibliography…
Multi-modality image fusion (MMIF) aims to integrate complementary information from different modalities into a single fused image to represent the imaging scene and facilitate downstream visual tasks comprehensively.
A new approach to linear filtering and prediction problems
Rudolph Emil Kalman. 1960 · 1960
Earlier work this paper cites.
Medical image fusion: A survey of the state of the art
Alex Pappachen James and Belur V Dasarathy. 2014 · 2014
Earlier work this paper cites.
You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition . 779–788
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. 2016 · 2016
Earlier work this paper cites.
Multi-exposure image fusion by optimizing a structural similarity index
Kede Ma, Zhengfang Duanmu, Hojatollah Yeganeh, and Zhou Wang. 2017 · 2017
Earlier work this paper cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision . 618–626
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. 2017 · 2017
Earlier work this paper cites.
DenseFuse: A fusion approach to infrared and visible images
Hui Li and Xiao-Jun Wu. 2018 · 2018
Earlier work this paper cites.
Adaptive near-infrared and visible fusion for fast image enhancement
Mohamed Awad, Ahmed Elliethy, and Hussein A Aly. 2019 · 2019
Earlier work this paper cites.
Perceptual-sensitive gan for generating adversarial patches. In Proceedings of the AAAI conference on artificial intelligence , Vol. 33. 1028–1035
Aishan Liu, Xianglong Liu, Jiaxin Fan, Yuqing Ma, Anlan Zhang, Huiyuan Xie, and Dacheng Tao. 2019 · 2019
Earlier work this paper cites.
FusionGAN: A generative adversarial network for infrared and visible image fusion
Jiayi Ma, Wei Yu, Pengwei Liang, Chang Li, and Junjun Jiang. 2019 · 2019
Earlier work this paper cites.
Basnet: Boundary-aware salient object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 7479–7489
Xuebin Qin, Zichen Zhang, Chenyang Huang, Chao Gao, Masood Dehghan, and Martin Jagersand. 2019 · 2019
Earlier work this paper cites.
Spectral-depth imaging with deep learning based reconstruction
Mingde Yao, Zhiwei Xiong, Lizhi Wang, Dong Liu, and Xuejin Chen. 2019 · 2019
Earlier work this paper cites.
Yolov4: Optimal speed and accuracy of object detection
Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. 2020 · 2020
Earlier work this paper cites.
NestFuse: An infrared and visible image fusion architecture based on nest connection and spatial/channel attention models
Hui Li, Xiao-Jun Wu, and Tariq Durrani. 2020 · 2020
Earlier work this paper cites.
DDcGAN: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion
Jiayi Ma, Han Xu, Junjun Jiang, Xiaoguang Mei, and Xiao-Ping Zhang. 2020a · 2020
Earlier work this paper cites.
GANMcC: A generative adversarial network with multiclassification constraints for infrared and visible image fusion
Jiayi Ma, Hao Zhang, Zhenfeng Shao, Pengwei Liang, and Han Xu. 2020b · 2020
Earlier work this paper cites.
Forward and backward information retention for accurate binary neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 2250–2259
Haotong Qin, Ruihao Gong, Xianglong Liu, Mingzhu Shen, Ziran Wei, Fengwei Yu, and Jingkuan Song. 2020 · 2020
Earlier work this paper cites.
U2Fusion: A unified unsupervised image fusion network
Han Xu, Jiayi Ma, Junjun Jiang, Xiaojie Guo, and Haibin Ling. 2020a · 2020
Earlier work this paper cites.
DIDFuse: Deep image decomposition for infrared and visible image fusion
Zixiang Zhao, Shuang Xu, Chunxia Zhang, Junmin Liu, Pengfei Li, and Jiangshe Zhang. 2020b · 2020
Earlier work this paper cites.
Bayesian fusion for infrared and visible images
Zixiang Zhao, Shuang Xu, Chunxia Zhang, Junmin Liu, and Jiangshe Zhang. 2020a · 2020
Cited alongside, same era.
Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Ré. 2021a · 2021
Cited alongside, same era.
Combining recurrent, convolutional, and continuous-time models with linear state space layers
Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, and Christopher Ré. 2021b · 2021
Cited alongside, same era.
RFN-Nest: An end-to-end residual fusion network for infrared and visible images
Hui Li, Xiao-Jun Wu, and Josef Kittler. 2021 · 2021
Cited alongside, same era.
Training robust deep neural networks via adversarial noise propagation
Aishan Liu, Xianglong Liu, Hang Yu, Chongzhi Zhang, Qiang Liu, and Dacheng Tao. 2021c · 2021
Cited alongside, same era.
SwinFusion: Cross-domain long-range learning for general image fusion via swin transformer
Jiayi Ma, Linfeng Tang, Fan Fan, Jun Huang, Xiaoguang Mei, and Yong Ma. 2022 · 2022
Later among the works it cites.
S4nd: Modeling images and videos as multidimensional signals with state spaces
Eric Nguyen, Karan Goel, Albert Gu, Gordon Downs, Preey Shah, Tri Dao, Stephen Baccus, and Christopher Ré. 2022 · 2022
Later among the works it cites.
Bibert: Accurate fully binarized bert
Haotong Qin, Yifu Ding, Mingyuan Zhang, Qinghua Yan, Aishan Liu, Qingqing Dang, Ziwei Liu, and Xianglong Liu. 2022 · 2022
Later among the works it cites.
Transmef: A transformer-based multi-exposure image fusion framework using self-supervised multi-task learning. In Proceedings of the AAAI conference on artificial intelligence , Vol. 36. 2126–2134
Linhao Qu, Shaolei Liu, Manning Wang, and Zhijian Song. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning a deep multi-scale feature ensemble and an edge-attention guidance for image fusion
Jinyuan Liu, Xin Fan, Ji Jiang, Risheng Liu, and Zhongxuan Luo. 2021a · 2021
Cited alongside, same era.
STDFusionNet: An infrared and visible image fusion network based on salient target detection
Jiayi Ma, Linfeng Tang, Meilong Xu, Hao Zhang, and Guobao Xiao. 2021 · 2021
Cited alongside, same era.
Robustart: Benchmarking robustness on architecture design and training techniques
Shiyu Tang, Ruihao Gong, Yan Wang, Aishan Liu, Jiakai Wang, Xinyun Chen, Fengwei Yu, Xianglong Liu, Dawn Song, Alan Yuille, et al · 2021
Cited alongside, same era.
Dual attention suppression attack: Generate adversarial camouflage in physical world. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 8565–8574
Jiakai Wang, Aishan Liu, Zixin Yin, Shunchang Liu, Shiyu Tang, and Xianglong Liu. 2021 · 2021
Cited alongside, same era.
Early convolutions help transformers see better
Tete Xiao, Mannat Singh, Eric Mintun, Trevor Darrell, Piotr Dollár, and Ross Girshick. 2021 · 2021
Cited alongside, same era.
EMFusion: An unsupervised enhanced medical image fusion network
Han Xu and Jiayi Ma. 2021 · 2021
Cited alongside, same era.
SDNet: A versatile squeeze-and-decomposition network for real-time image fusion
Hao Zhang and Jiayi Ma. 2021 · 2021
Cited alongside, same era.
Jimmy TH Smith, Andrew Warrington, and Scott W Linderman. 2022 · 2022
Later among the works it cites.
PIAFusion: A progressive infrared and visible image fusion network based on illumination aware
Linfeng Tang, Jiteng Yuan, Hao Zhang, Xingyu Jiang, and Jiayi Ma. 2022 · 2022
Later among the works it cites.
Sir-former: Stereo image restoration using transformer. In Proceedings of the 30th ACM International Conference on Multimedia . 6377–6385
Zizheng Yang, Mingde Yao, Jie Huang, Man Zhou, and Feng Zhao. 2022 · 2022
Later among the works it cites.
Changer: Feature interaction is what you need for change detection
Sheng Fang, Kaiyu Li, and Zhe Li. 2023 · 2023
Later among the works it cites.
Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao. 2023 · 2023
Later among the works it cites.
Hqg-net: Unpaired medical image enhancement with high-quality guidance
Chunming He, Kai Li, Guoxia Xu, Jiangpeng Yan, Longxiang Tang, Yulun Zhang, Yaowei Wang, and Xiu Li. 2023 · 2023
Later among the works it cites.
Efficient movie scene detection using state-space transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 18749–18758
Md Mohaiminul Islam, Mahmudul Hasan, Kishan Shamsundar Athrey, Tony Braskich, and Gedas Bertasius. 2023 · 2023
Later among the works it cites.
Distribution-sensitive information retention for accurate binary neural network
Haotong Qin, Xiangguo Zhang, Ruihao Gong, Yifu Ding, Yi Xu, and Xianglong Liu. 2023 · 2023
Later among the works it cites.
Selective structured state-spaces for long-form video understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 6387–6397
Jue Wang, Wentao Zhu, Pichao Wang, Xiang Yu, Linda Liu, Mohamed Omar, and Raffay Hamid. 2023 · 2023
Later among the works it cites.
Dif-fusion: Towards high color fidelity in infrared and visible image fusion with diffusion models
Jun Yue, Leyuan Fang, Shaobo Xia, Yue Deng, and Jiayi Ma. 2023 · 2023
Later among the works it cites.
Pan-Mamba: Effective pan-sharpening with State Space Model
Xuanhua He, Ke Cao, Keyu Yan, Rui Li, Chengjun Xie, Jie Zhang, and Man Zhou. 2024 · 2024
Closest in time.
Vmamba: Visual state space model
Yue Liu, Yunjie Tian, Yuzhong Zhao, Hongtian Yu, Lingxi Xie, Yaowei Wang, Qixiang Ye, and Yunfan Liu. 2024 · 2024
Closest in time.
U-mamba: Enhancing long-range dependency for biomedical image segmentation
Jun Ma, Feifei Li, and Bo Wang. 2024 · 2024
Closest in time.
Vision mamba: Efficient visual representation learning with bidirectional state space model
Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang. 2024 · 2024
Closest in time.