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

Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks
(DNNs) training, and they usually train a DNN for each single visual recognition task …

Multimodal learning, which aims to understand and analyze information from multiple
modalities, has achieved substantial progress in the supervised regime in recent years …

Contrastive pretraining on parallel image-text data has attained great success in vision-
language processing (VLP), as exemplified by CLIP and related methods. However, prior …

The ability to associate touch with other modalities has huge implications for humans and
computational systems. However multimodal learning with touch remains challenging due to …

Text-supervised semantic segmentation is a novel research topic that allows semantic
segments to emerge with image-text contrasting. However, pioneering methods could be …

Generalizable Face anti-spoofing (FAS) approaches have recently garnered considerable
attention due to their robustness in unseen scenarios. Some recent methods incorporate …

Prompt learning has emerged as an effective and data-efficient technique in large Vision-
Language Models (VLMs). However when adapting VLMs to specialized domains such as …

The recent advancement in vision-and-language pretraining (VLP) has significantly
improved the performance of cross-modal image-text retrieval (ITR) systems. However, the …

Large-scale pre-trained Vision-Language Models (VLMs), such as CLIP, establish the
correlation between texts and images, achieving remarkable success on various …