Traditional recommender systems typically use user-item rating histories as their main data
source. However, deep generative models now have the capability to model and sample …

We propose a novel framework for video understanding, called Temporally Contextualized
CLIP (TC-CLIP), which leverages essential temporal information through global interactions …

Building upon the impressive success of CLIP (Contrastive Language-Image Pretraining),
recent pioneer works have proposed to adapt the powerful CLIP to video data, leading to …

Generative models are a class of AI models capable of creating new instances of data by
learning and sampling from their statistical distributions. In recent years, these models have …

Pre-trained vision-language models (VLMs) have shown impressive results in various visual
classification tasks. However, we often fail to fully unleash their potential when adapting …

Tables contain factual and quantitative data accompanied by various structures and
contents that pose challenges for machine comprehension. Previous methods generally …

Transferring visual-language knowledge from large-scale foundation models for video
recognition has proved to be effective. To bridge the domain gap, additional parametric …

Many recommendation systems limit user inputs to text strings or behavior signals such as
clicks and purchases, and system outputs to a list of products sorted by relevance. With the …

In rapidly evolving field of vision-language models (VLMs), contrastive language-image pre-
training (CLIP) has made significant strides, becoming foundation for various downstream …

Large Language Vision Models (LLVMs) have demonstrated effectiveness in processing
internet videos, yet they struggle with the visually perplexing dynamics present in Activities …