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 …

Abstract Contrastive Language-Image Pre-training (CLIP) has significantly boosted the
performance of various vision-language tasks by scaling up the dataset with image-text pairs …

Although an object may appear in numerous contexts, we often describe it in a limited
number of ways. Language allows us to abstract away visual variation to represent and …

Abstract Language-image pre-training largely relies on how precisely and thoroughly a text
describes its paired image. In practice, however, the contents of an image can be so rich that …

While existing backdoor attacks have successfully infected multimodal contrastive learning
models such as CLIP they can be easily countered by specialized backdoor defenses for …

In the field of medical Vision-Language Pretraining (VLP), significant efforts have been
devoted to deriving text and image features from both clinical reports and associated …

Image-text contrastive learning models such as CLIP have demonstrated strong task transfer
ability. The high generality and usability of these visual models is achieved via a web-scale …

Abstract Contrastive Language-Image Pretraining has emerged as a prominent approach for
training vision and text encoders with uncurated image-text pairs from the web. To enhance …

In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …

Contrastive language-image pre-training (CLIP) serves as a de-facto standard to align
images and texts. Nonetheless, the loose correlation between images and texts of web …