Cross-modal retrieval aims to retrieve relevant samples across different media modalities.
Existing cross-modal retrieval approaches are contingent on learning common …

Recently, convolutional neural network (CNN) visual features have demonstrated their
powerful ability as a universal representation for various recognition tasks. In this paper …

With the exponential surge in diverse multi-modal data, traditional uni-modal retrieval
methods struggle to meet the needs of users demanding access to data from various …

Abstract recommendation, and marketing services. Extensive efforts have been made to
conquer the cross-modal retrieval problem in the general domain. When it comes to E …

Large-scale single-stream pre-training has shown dramatic performance in image-text
retrieval. Regrettably, it faces low inference efficiency due to heavy attention layers …

Cross-modal hashing encodes the multimedia data into a common binary hash space in
which the correlations among the samples from different modalities can be effectively …

Audio-text retrieval aims at retrieving a target audio clip or caption from a pool of candidates
given a query in another modality. Solving such cross-modal retrieval task is challenging …

Deep cross-modal hashing has become an essential tool for supervised multimodal search.
These models tend to be optimized with large, curated multimodal datasets, where most …

Visual-semantic embedding aims to find a shared latent space where related visual and
textual instances are close to each other. Most current methods learn injective embedding …

Many recent studies leverage the pre-trained CLIP for text-video cross-modal retrieval by
tuning the backbone with additional heavy modules, which not only brings huge …