51 citations · 78 across the 11 of their papers we have counts for
6 papers · 1 filter
Sustainable Self-evolution Adversarial Training
Wenxuan Wang, Chenglei Wang, Huihui Qi +4
With the wide application of deep neural network models in various computer vision tasks, there has been a proliferation of adversarial example generation strategies aimed at deepl…
Task-Adapter: Task-specific Adaptation of Image Models for Few-shot Action Recognition
Congqi Cao, Yueran Zhang, Yating Yu +3
Existing works in few-shot action recognition mostly fine-tune a pre-trained image model and design sophisticated temporal alignment modules at feature level. However, simply fully…
A self-supervised CNN for image watermark removal
Chunwei Tian, Menghua Zheng, Tiancai Jiao +3
Popular convolutional neural networks mainly use paired images in a supervised way for image watermark removal. However, watermarked images do not have reference images in the real…
Perceptive self-supervised learning network for noisy image watermark removal
Chunwei Tian, Menghua Zheng, Bo Li +3
Popular methods usually use a degradation model in a supervised way to learn a watermark removal model. However, it is true that reference images are difficult to obtain in the rea…
DDF: A Novel Dual-Domain Image Fusion Strategy for Remote Sensing Image Semantic Segmentation with Unsupervised Domain Adaptation
Lingyan Ran, Lushuang Wang, Tao Zhuo +1
Semantic segmentation of remote sensing images is a challenging and hot issue due to the large amount of unlabeled data. Unsupervised domain adaptation (UDA) has proven to be advan…
Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey
Lingyan Ran, Yali Li, Guoqiang Liang +1
Semantic segmentation is an important and popular research area in computer vision that focuses on classifying pixels in an image based on their semantics. However, supervised deep…