57 citations · 58 across the 8 of their papers we have counts for
10 papers
ATT-CR: Adaptive Triangular Transformer for Cloud Removal
Yang Wu, Ye Deng, Pengna Li +4
Cloud removal aims to accurately reconstruct the ground objects obscured by clouds in remote sensing images. Existing Transformer-based methods utilizing self-attention have shown…
Task-Guided Prompting for Unified Remote Sensing Image Restoration
Wenli Huang, Yang Wu, Xiaomeng Xin +3
Remote sensing image restoration (RSIR) is essential for recovering high-fidelity imagery from degraded observations, enabling accurate downstream analysis. However, most existing…
FreqGRL: Suppressing Low-Frequency Bias and Mining High-Frequency Knowledge for Cross-Domain Few-Shot Learning
Siqi Hui, Sanping Zhou, Ye deng +2
Cross-domain few-shot learning (CD-FSL) aims to recognize novel classes with only a few labeled examples under significant domain shifts. While recent approaches leverage a limited…
Enhancing Mamba Decoder with Bidirectional Interaction in Multi-Task Dense Prediction
Mang Cao, Sanping Zhou, Yizhe Li +3
Sufficient cross-task interaction is crucial for success in multi-task dense prediction. However, sufficient interaction often results in high computational complexity, forcing exi…
Autoregressive Images Watermarking through Lexical Biasing: An Approach Resistant to Regeneration Attack
Siqi Hui, Yiren Song, Sanping Zhou +3
Autoregressive (AR) image generation models have gained increasing attention for their breakthroughs in synthesis quality, highlighting the need for robust watermarking to prevent…
Attentive Contextual Attention for Cloud Removal
Wenli Huang, Ye Deng, Yang Wu +1
Cloud cover can significantly hinder the use of remote sensing images for Earth observation, prompting urgent advancements in cloud removal technology. Recently, deep learning stra…
Understanding the Overfitting of the Episodic Meta-training
Siqi Hui, Sanping Zhou, Ye deng +1
Despite the success of two-stage few-shot classification methods, in the episodic meta-training stage, the model suffers severe overfitting. We hypothesize that it is caused by ove…
T-former: An Efficient Transformer for Image Inpainting
Ye Deng, Siqi Hui, Sanping Zhou +2
Benefiting from powerful convolutional neural networks (CNNs), learning-based image inpainting methods have made significant breakthroughs over the years. However, some nature of C…
Auxiliary Loss Reweighting for Image Inpainting
Siqi Hui, Sanping Zhou, Ye Deng +2
Image Inpainting is a task that aims to fill in missing regions of corrupted images with plausible contents. Recent inpainting methods have introduced perceptual and style losses a…
Distributed Generative Adversarial Net
Xiaoyu Wang, Ye Deng, Jinjun Wang
Recently the Generative Adversarial Network has become a hot topic. Considering the application of GAN in multi-user environment, we propose Distributed-GAN. It enables multiple us…