38 citations · 65 across the 11 of their papers we have counts for
10 papers · 1 filter
Building a Mind Palace: Structuring Environment-Grounded Semantic Graphs for Effective Long Video Analysis with LLMs
Zeyi Huang, Yuyang Ji, Xiaofang Wang +11
Long-form video understanding with Large Vision Language Models is challenged by the need to analyze temporally dispersed yet spatially concentrated key moments within limited cont…
Apollo: An Exploration of Video Understanding in Large Multimodal Models
Orr Zohar, Xiaohan Wang, Yann Dubois +9
Despite the rapid integration of video perception capabilities into Large Multimodal Models (LMMs), the underlying mechanisms driving their video understanding remain poorly unders…
Accelerating Multimodal Large Language Models by Searching Optimal Vision Token Reduction
Shiyu Zhao, Zhenting Wang, Felix Juefei-Xu +7
Prevailing Multimodal Large Language Models (MLLMs) encode the input image(s) as vision tokens and feed them into the language backbone, similar to how Large Language Models (LLMs)…
Learning and Verification of Task Structure in Instructional Videos
Medhini Narasimhan, Licheng Yu, Sean Bell +2
Given the enormous number of instructional videos available online, learning a diverse array of multi-step task models from videos is an appealing goal. We introduce a new pre-trai…
Tell Me What Happened: Unifying Text-guided Video Completion via Multimodal Masked Video Generation
Tsu-Jui Fu, Licheng Yu, Ning Zhang +4
Generating a video given the first several static frames is challenging as it anticipates reasonable future frames with temporal coherence. Besides video prediction, the ability to…
LoopITR: Combining Dual and Cross Encoder Architectures for Image-Text Retrieval
Jie Lei, Xinlei Chen, Ning Zhang +4
Dual encoders and cross encoders have been widely used for image-text retrieval. Between the two, the dual encoder encodes the image and text independently followed by a dot produc…
Unsupervised Vision-and-Language Pre-training via Retrieval-based Multi-Granular Alignment
Mingyang Zhou, Licheng Yu, Amanpreet Singh +3
Vision-and-Language (V+L) pre-training models have achieved tremendous success in recent years on various multi-modal benchmarks. However, the majority of existing models require p…
CommerceMM: Large-Scale Commerce MultiModal Representation Learning with Omni Retrieval
Licheng Yu, Jun Chen, Animesh Sinha +4
We introduce CommerceMM - a multimodal model capable of providing a diverse and granular understanding of commerce topics associated to the given piece of content (image, text, ima…
VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation
Linjie Li, Jie Lei, Zhe Gan +12
Most existing video-and-language (VidL) research focuses on a single dataset, or multiple datasets of a single task. In reality, a truly useful VidL system is expected to be easily…
Connecting What to Say With Where to Look by Modeling Human Attention Traces
Zihang Meng, Licheng Yu, Ning Zhang +4
We introduce a unified framework to jointly model images, text, and human attention traces. Our work is built on top of the recent Localized Narratives annotation framework [30], w…