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 …
J Pang, Z Wang, J Tang, M Xiao, N Yin - Proceedings of the 31st ACM …, 2023 - dl.acm.org
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 …
T Pan, F Xu, X Yang, S He, C Jiang… - Proceedings of the …, 2023 - openaccess.thecvf.com
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 …
C Zhang, J He, L Shang - Personal and Ubiquitous Computing, 2024 - Springer
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 …