In person re-identification (ReID) task, it is still challenging to learn discriminative
representation by deep learning, due to limited data. Generally speaking, the model will get …

In this paper, we propose Hypergraph-Induced Semantic Tuplet (HIST) loss for deep metric
learning that leverages the multilateral semantic relations of multiple samples to multiple …

Graph neural networks (GNNs) have achieved state-of-the-art results on graph classification
tasks. They have been primarily studied in cases of supervised end-to-end training, which …

Graph neural networks (GNNs) have achieved impressive impressions for graph-related
tasks. However, most GNNs are primarily studied under the cases of signal domain with …

Fine-grained image classification can be considered as a discriminative learning process
where images of different subclasses are separated from each other while the same …

Image retrieval plays an important role in the Internet world. Usually, the core parts of
mainstream visual retrieval systems include an online service of the embedding model and …

Employing additional prior knowledge to model local features as a final fine-grained object
representation has become a trend for fine-grained object retrieval (FGOR). A potential …

Visual representation for fine-grained visual recognition can be learned by mandatorily
enforcing all samples of the same category into a uniform representation. This strict training …

Medical image classification has become popular in computer-aided diagnosis (CAD) of
pneumoconiosis. However, most current work focuses on improving the accuracy of …

In this article, we propose a novel method to simultaneously solve the data problem of dirty
quality and poor quantity for person reidentification (ReID). Dirty quality refers to the wrong …