The long-term goal of machine learning is to learn general visual
repres...
Handwriting authentication is a valuable tool used in various fields, su...
Through experiments on various meta-learning methods, task samplers, and...
In recent years, self-supervised learning (SSL) has emerged as a promisi...
Diffusion models are a powerful class of generative models that can prod...
Due to limitations in data quality, some essential visual tasks are diff...
Generative Adversarial Networks (GANs) and their variants have achieved
...
Generative adversarial networks (GANs) have achieved remarkable progress...
Benefiting from the injection of human prior knowledge, graphs, as deriv...
Text-guided diffusion models have shown superior performance in image/vi...
As a successful approach to self-supervised learning, contrastive learni...
While self-supervised learning techniques are often used to mining impli...
Few-shot learning models learn representations with limited human
annota...
The prevailing graph neural network models have achieved significant pro...
Contrastive learning (CL)-based self-supervised learning models learn vi...
Recently, significant progress has been made in masked image modeling to...
Vision-language models are pre-trained by aligning image-text pairs in a...
What matters for contrastive learning? We argue that contrastive learnin...
Unsupervised domain adaptation (UDA) requires source domain samples with...
Recent works explore learning graph representations in a self-supervised...
Although vision Transformers have achieved excellent performance as back...
Multi-view representation learning captures comprehensive information fr...