Deep hashing has shown promising performance in large-scale image retrieval. The
hashing process utilizes Deep Neural Networks (DNNs) to embed images into compact …

Existing deep quantization methods provided an efficient solution for large-scale image
retrieval. However, the significant intra-class variations, like pose, illumination, and …

Hash learning aims to map multimedia data into Hamming space, in which the data point is
represented by low-dimensional binary codes and the similarity relationships are preserved …

In the rapidly evolving field of manufacturing industry, product designers need to be able to
quickly and accurately retrieve and reference existing Computer-Aided Design (CAD) …

Deep hashing models have been proposed as an efficient method for large-scale similarity
search. How-ever, most existing deep hashing methods only utilize fine-level labels for …

This paper introduces a dual-branch fine-grained image retrieval method based on cross-
scale feature fusion. Initially, visual features are extracted from input images using a …

Compared with pairwise/triplet similarity based hashing, central similarity based hashing
can capture data relationships more efficiently. However, previous works still have the …