Vision systems that see and reason about the compositional nature of visual scenes are
fundamental to understanding our world. The complex relations between objects and their …

This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …

Abstract Multi-modal Large Language Models (MLLMs) have demonstrated impressive
instruction abilities across various open-ended tasks. However previous methods have …

Abstract We present Fast Language-Image Pre-training (FLIP), a simple and more efficient
method for training CLIP. Our method randomly masks out and removes a large portion of …

In this paper, we first extend the recent Masked Auto-Encoder (MAE) model from a single
modality to audio-visual multi-modalities. Subsequently, we propose the Contrastive Audio …

Large-scale web-crawled datasets are fundamental for the success of pre-training vision-
language models, such as CLIP. However, the inherent noise and potential irrelevance of …

Medical vision-language models (VLMs) combine computer vision (CV) and natural
language processing (NLP) to analyze visual and textual medical data. Our paper reviews …

Masked image modeling (MIM) has attracted much research attention due to its promising
potential for learning scalable visual representations. In typical approaches, models usually …

Abstract 3D visual language reasoning plays an important role in effective human-computer
interaction. The current approaches for 3D visual reasoning are task-specific, and lack pre …

As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for
sustainable development by harnessing the power of cross-domain data fusion from diverse …