Main

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2025-04-16 11:09:27

expired found date

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Server

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Open Graph

title

description

nextqa

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site name

author

updated

2026-02-03 14:08:29

raw text

vstar VSTAR: A Video-grounded Dialogue Dataset for Situated Semantic Understanding with Scene and Topic Transitions Yuxuan Wang Zilong Zheng Xueliang Zhao Jinpeng Li Yueqian Wang Dongyan Zhao Introduction Video-grounded dialogue understanding is a challenging problem that requires machine to perceive, parse and reason over situated semantics extracted from weakly aligned video and dialogues. Most existing benchmarks treat both modalities the same as a frame-independent visual understanding task, while neglecting the intrinsic attributes in multimodal dialogues, such as scene and topic transitions. In this work, we present Video-grounded Scene&Topic AwaRe dialogue (VSTAR) dataset, a large scale video-grounded dialogue understanding dataset based on 395 TV series. Based on VSTAR, we propose two benchmarks for video-grounded dialogue understanding: scene segmentation and ...

Text analysis

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