3 accepted papers
Multimodal contrastive learning (MCL) aims to embed data from two modalities in a shared embedding space. However, in practice, representations of images and text occupy completely separate regions of embedding space, a phenomenon called the modality gap. Moreover, experimental findings on how the s…
The emergence of Vision-Language Models (VLMs) represents a significant advancement in integrating computer vision with Large Language Models (LLMs) to generate detailed text descriptions from visual inputs. Despite their growing importance, the security of VLMs, particularly against backdoor attack…
Deep learning has achieved significant success by training on balanced datasets. However, real-world data often exhibit long-tailed distributions. Empirical studies have revealed that long-tailed data skew data representations, where head classes dominate the feature space. Many methods have been pr…