Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (\emph {eg,} social …

The infrared small and dim (S&D) target detection is one of the key techniques in the infrared
search and tracking system. Since the local regions similar to infrared S&D targets spread …

Abstract Objects are usually associated with multiple attributes, and these attributes often
exhibit high correlations. Modeling complex relationships between attributes poses a great …

Recognizing the category and its ingredient composition from food images facilitates
automatic nutrition estimation, which is crucial to various health relevant applications, such …

Image denoising is one of the hottest topics in image restoration area, it has achieved great
progress both in terms of quantity and quality in recent years, especially after the wide and …

Zero-shot learning (ZSL) tackles the unseen class recognition problem by transferring
semantic knowledge from seen classes to unseen ones. Typically, to guarantee desirable …

Ingredient prediction has received more and more attention with the help of image
processing for its diverse real-world applications, such as nutrition intake management and …

The task of image multi-label classification is to accurately recognize multiple objects in an
input image. Most of the recent works need to leverage the label co-occurrence matrix …

Multi-label learning for large-scale data is a grand challenge because of a large number of
labels with a complex data structure. Hence, the existing large-scale multi-label methods …

Multi-label classification aims to recognize multiple objects or attributes from images. The
key to solving this issue relies on effectively characterizing the inter-label correlations or …