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 …

Enabling bi-directional retrieval of images and texts is important for understanding the
correspondence between vision and language. Existing methods leverage the attention …

Cross-modal retrieval has been a compelling topic in the multimodal community. Recently,
to mitigate the high cost of data collection, the co-occurred pairs (eg, image and text) could …

Recently, cross-modal retrieval is emerging with the help of deep multimodal learning.
However, even for unimodal data, collecting large-scale well-annotated data is expensive …

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 …

Visual appearance is considered to be the most important cue to understand images for
cross-modal retrieval, while sometimes the scene text appearing in images can provide …

Cross-modal retrieval across image and text modalities is a challenging task due to its
inherent ambiguity: An image often exhibits various situations, and a caption can be coupled …

The abundance of multimodal data (eg social media posts) has inspired interest in cross-
modal retrieval methods. Popular approaches rely on a variety of metric learning losses …