1 code implementation • 15 Dec 2023 • Xiaofeng Zhang, Zishan Xu, Hao Tang, Chaochen Gu, Wei Chen, Shanying Zhu, Xinping Guan
Low-light image enhancement is a crucial visual task, and many unsupervised methods tend to overlook the degradation of visible information in low-light scenes, which adversely affects the fusion of complementary information and hinders the generation of satisfactory results.
no code implementations • 3 Oct 2023 • Luyi Qiu, Xiaofeng Zhang, Chaochen Gu, and ShanYing Zhu
Remote sensing change detection between bi-temporal images receives growing concentration from researchers.
2 code implementations • 17 Jun 2023 • Qihan Zhao, Xiaofeng Zhang, Hao Tang, Chaochen Gu, Shanying Zhu
Image restoration is a low-level visual task, and most CNN methods are designed as black boxes, lacking transparency and intrinsic aesthetics.
1 code implementation • 1 Jun 2023 • Xiaofeng Zhang, Chaochen Gu, Shanying Zhu
The challenges surrounding the application of image shadow removal to real-world images and not just constrained datasets like ISTD/SRD have highlighted an urgent need for zero-shot learning in this field.
no code implementations • 26 Jul 2022 • Yudi Zhao, Kuangrong Hao, Chaochen Gu, Bing Wei
To address the trade-off problem of quality-diversity for the generated images in imbalanced classification tasks, we research on over-sampling based methods at the feature level instead of the data level and focus on searching the latent feature space for optimal distributions.
1 code implementation • CVPR 2023 • Tongkun Guan, Chaochen Gu, Jingzheng Tu, Xue Yang, Qi Feng, Yudi Zhao, Xiaokang Yang, Wei Shen
Supervised attention can alleviate the above issue, but it is character category-specific, which requires extra laborious character-level bounding box annotations and would be memory-intensive when handling languages with larger character categories.
Ranked #2 on Scene Text Recognition on ICDAR 2003
1 code implementation • 25 Oct 2021 • Tongkun Guan, Chaochen Gu, Changsheng Lu, Jingzheng Tu, Qi Feng, Kaijie Wu, Xinping Guan
Then, an attentive refinement network is developed by the attention map to rectify the location deviation of candidate boxes.
1 code implementation • ICCV 2021 • Fei Zhang, Chaochen Gu, Chenyue Zhang, Yuchao Dai
Therefore, a CAM with more information related to object seeds can be obtained by narrowing down the gap between the sum of CAMs generated by the CP Pair and the original CAM.
no code implementations • 23 Jul 2021 • Fei Zhang, Chaochen Gu, Feng Yang
Deep Q Network (DQN) has several limitations when applied in planning a path in environment with a number of dilemmas according to our experiment.