Cross-modal hashing (CMH) has gained much attention due to its effectiveness and
efficiency in facilitating efficient retrieval between different modalities. Whereas, most …

Learning with noisy labels (LNL) aims to ensure model generalization given a label-
corrupted training set. In this work, we investigate a rarely studied scenario of LNL on fine …

In this paper, we investigate the problem of unsupervised domain adaptive hashing, which
leverage knowledge from a label-rich source domain to expedite learning to hash on a label …

Due to its low memory and computational requirements, hashing techniques are widely
applied for cross-modal retrieval. However, there are still two unresolved issues: 1) the class …

Image retrieval (IR) methods extract the most relevant images to the query images from an
image database. The existing IR methods, which retrieve images with a low degree of …

Graph-based change point detection (CPD) plays an irreplaceable role in discovering
anomalous graphs in a time-varying network. While several techniques have been proposed …

Hashing and quantization methods based on deep learning are being widely used in large-
scale image retrieval. Most methods use traditional supervised models to train models …

Existing multi-view representation learning methods typically follow a specific-to-uniform
pipeline, extracting latent features from each view and then fusing or aligning them to obtain …

This paper studies the problem of unsupervised domain adaptive hashing, which is less-
explored but emerging for efficient image retrieval, particularly for cross-domain retrieval …

This paper studies unsupervised domain adaptive hashing, which aims to transfer a hashing
model from a label-rich source domain to a label-scarce target domain. Current state-of-the …