Recent days witness significant progress in various multi-modal tasks made by Contrastive
Language-Image Pre-training (CLIP), a multi-modal large-scale model that learns visual …

Image–text matching is a crucial branch in multimedia retrieval which relies on learning inter-
modal correspondences. Most existing methods focus on global or local correspondence …

Image and text matching bridges visual and textual modality differences and plays a
considerable role in cross-modal retrieval. Much progress has been achieved through …

Hashing has produced enormous potentials in cross-modal image–text search, which learns
compact binary codes by exploring the correlations between distinct modalities. However …

Visual dialog, a visual-language task, enables an AI agent to engage in conversation with
humans grounded in a given image. To generate appropriate answers for a series of …

Hashing has been shown to be successful in a number of Approximate Nearest Neighbor
(ANN) domains, ranging from medicine, computer vision to information retrieval. However …

Image captioning aims to generate descriptive sentences to describe image main contents.
Existing attention-based approaches mainly focus on the salient visual features in the …

Remarkable success of the existing Near-InfraRed and VISible (NIR-VIS) approaches owes
to sufficient labeled training data. However, collecting and tagging data from different …

With its merits in query speed and memory footprint, hashing has elicited considerable
monument in cross-media similarity retrieval applications. Many label-dependent supervised …

Recently, due to the low storage consumption and high search efficiency of hashing
methods and the powerful feature extraction capability of deep neural networks, deep cross …