In this paper, we introduce Saliency-Based Adaptive Masking (SBAM), a novel and cost-
effective approach that significantly enhances the pre-training performance of Masked …

CLIP has become a promising language-supervised visual pre-training framework and
achieves excellent performance over a wide range of tasks. This paper aims to distill small …

In this paper, we introduce DetailCLIP: A Detail-Oriented CLIP to address the limitations of
contrastive learning-based vision-language models, particularly CLIP, in handling detail …

Vision-language foundation models, represented by Contras-tive Language-Image Pre-
training (CLIP), have gained increasing attention for jointly understanding both vision and …

Large-scale digital whole slide image (WSI) datasets analysis have gained significant
attention in computer-aided cancer diagnosis. Content-based histopathological image …

Abstract Contrastive Language-Image Pre-training (CLIP) has become a promising
language-supervised visual pre-training framework. This paper aims to distill small CLIP …

We present a novel LLM-based pipeline for creating contextual descriptions of human body
poses in images using only auxiliary attributes. This approach facilitates the creation of the …

Traffic anomaly detection (TAD) in driving videos is critical for ensuring the safety of
autonomous driving and advanced driver assistance systems. Previous single-stage TAD …

Prompt learning has become one of the most efficient paradigms for adapting large pre-
trained vision-language models to downstream tasks. Current state-of-the-art methods, like …

The development of Large Language Models (LLM) and Diffusion Models brings the boom
of Artificial Intelligence Generated Content (AIGC). It is essential to build an effective quality …