Whether what you see in Figure 1 is a" flamingo" or a" bird", is the question we ask in this
paper. While fine-grained visual classification (FGVC) strives to arrive at the former, for the …

In modern industry, large-scale fault diagnosis of complex systems is emerging and
becoming increasingly important. Most deep learning-based methods perform well on small …

Though impressive performance has been achieved in specific visual realms (eg faces,
dogs, and places), an omni-vision representation generalizing to many natural visual …

Magnetic resonance imaging (MRI) is a prevailing method of modal infant brain tissue
analysis that precisely segments brain tissue and is vitally important for diagnosis …

In the era of big data, both the size and the number of features, samples, and classes
continue to increase, resulting in high-dimensional classification tasks. One characteristic …

In this paper, we study the fine-grained categorization problem under the few-shot setting, ie,
each fine-grained class only contains a few labeled examples, termed Fine-Grained Few …

Hierarchical classification identifies a sample from the root node to a leaf node along the
hierarchical structures of labels. It is often difficult to perform leaf-node prediction owing to …

Robot visual control aims to achieve three general objectives, namely, smoothness, rapidity,
and target keeping. In practice, such conflicting objectives make robot visual control, which …

The core of fine-grained recognition is to distinguish different subcategories within a same
broad category through subtle differences in images. Yet two important factors are less …

Fuzzy decision trees (FDTs) is one of the considerably excellent methods. Most of the
existing FDTs' methods are oriented to classification tasks. Applying FDTs to regression …