6 citations · 10 across the 6 of their papers we have counts for
6 papers
Modularized Dynamic-Granularity Video LLM for Multi-Event Long Video Understanding
Wei Feng, Xin Wang, Yu-Wei Zhan +2
Video Large Language Models (Video LLMs) have made significant advancements in various video understanding tasks. However, long-video scenarios remain challenging due to the tensio…
Think with Grounding: Curriculum Reinforced Reasoning with Video Grounding for Long Video Understanding
Houlun Chen, Xin Wang, Guangyao Li +4
Long video understanding is challenging due to rich and complicated multimodal clues in long temporal range.Current methods adopt reasoning to improve the model's ability to analyz…
Multi-modal Generative AI: Multi-modal LLMs, Diffusions, and the Unification
Xin Wang, Yuwei Zhou, Bin Huang +2
Multi-modal generative AI (Artificial Intelligence) has attracted increasing attention from both academia and industry. Particularly, two dominant families of techniques have emerg…
DisenStudio: Customized Multi-subject Text-to-Video Generation with Disentangled Spatial Control
Hong Chen, Xin Wang, Yipeng Zhang +4
Generating customized content in videos has received increasing attention recently. However, existing works primarily focus on customized text-to-video generation for single subjec…
LLM4VG: Large Language Models Evaluation for Video Grounding
Wei Feng, Xin Wang, Hong Chen +7
Recently, researchers have attempted to investigate the capability of LLMs in handling videos and proposed several video LLM models. However, the ability of LLMs to handle video gr…
VideoDreamer: Customized Multi-Subject Text-to-Video Generation with Disen-Mix Finetuning on Language-Video Foundation Models
Hong Chen, Xin Wang, Guanning Zeng +5
Customized text-to-video generation aims to generate text-guided videos with user-given subjects, which has gained increasing attention. However, existing works are primarily limit…