Recent studies have shown promising results on utilizing large pre-trained image-language
models for video question answering. While these image-language models can efficiently …

The goal of this work is to build flexible video-language models that can generalize to
various video-to-text tasks from few examples. Existing few-shot video-language learners …

Strong Artificial Intelligence (Strong AI) or Artificial General Intelligence (AGI) with abstract
reasoning ability is the goal of next-generation AI. Recent advancements in Large Language …

Sequential recommendation systems often suffer from data sparsity, leading to suboptimal
performance. While multimodal content, such as images and text, has been utilized to …

In this study, we uncover the unexpected efficacy of residual-based large language models
(LLMs) as part of encoders for biomedical imaging tasks, a domain traditionally devoid of …

This paper addresses the task of video question answering (videoQA) via a decomposed
multi-stage modular reasoning framework. Previous modular methods have shown promise …

Recent advances in pre-training vision-language models like CLIP have shown great
potential in learning transferable visual representations. Nonetheless, for downstream …

We introduce a new task called R eferring A tomic V ideo A ction R ecognition (RAVAR),
aimed at identifying atomic actions of a particular person based on a textual description and …

The recent explosion of performance of large language models (LLMs) has changed the
field of Natural Language Processing (NLP) more abruptly and seismically than any other …

Recent studies have demonstrated the effectiveness of Large Language Models (LLMs) as
reasoning modules that can deconstruct complex tasks into more manageable sub-tasks …