In multi-label learning, samples of practical classification task may associated with multiple
labels, it is challenging to acquire all labels of the training samples, the rapid expansion of …

Transformers have revolutionized natural language processing (NLP) and have had a huge
impact on automating tasks. Recently, transformers have led to the development of powerful …

Graph Matching has recently emerged as an attractive technique applied to various
computer vision tasks. Graph Matching based multi-label image classification, in particular …

Incremental learning of new classes is crucial to developing real-world artificial intelligence
systems. However, in some cases, we can only use a limited number of images to train the …

A virtual-image try-on is aimed at warping the target garment image to align with structure of
the human body. In recent studies, flexible flow field estimation has been employed via …

Graph matching aims to establish node correspondences between graphs, which is a
classic combinatorial optimization problem. In recent years,(deep) learning-based methods …

Multi-view multi-label classification is a crucial machine learning paradigm aimed at building
robust multi-label predictors by integrating heterogeneous features from various sources …

Multi-label image classification (MLIC) is a highly practical and challenging task in computer
vision. Compared to traditional single-label image classification, MLIC not only focuses on …

Multi-label image classification is an essential yet challenging task that requires to recognize
multiple objects of images. To this end, recent studies have sought to acquire visual …

Deep learning is currently reaching outstanding performances on different tasks, including
image classification, especially when using large neural networks. The success of these …