This paper introduces a powerful channel augmented joint learning strategy for the visible-
infrared recognition problem. For data augmentation, most existing methods directly adopt …

Image-text matching, as a fundamental task, bridges the gap between vision and language.
The key of this task is to accurately measure similarity between these two modalities. Prior …

In real-world applications, it is common that only a portion of data is aligned across views
due to spatial, temporal, or spatiotemporal asynchronism, thus leading to the so-called …

As a bridge between language and vision domains, cross-modal retrieval between images
and texts is a hot research topic in recent years. It remains challenging because the current …

Cross-modal retrieval has recently attracted growing attention, which aims to match
instances captured from different modalities. The performance of cross-modal retrieval …

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 …

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 …

This paper introduces a simple yet powerful channel augmentation for visible-infrared re-
identification. Most existing augmentation operations designed for single-modality visible …

Image-text retrieval is a fundamental task to bridge vision and language by exploiting
various strategies to fine-grained alignment between regions and words. This is still tough …

Abstract Image-Text Retrieval (ITR) is challenging in bridging visual and lingual modalities.
Contrastive learning has been adopted by most prior arts. Except for limited amount of …