Existing temporal video grounding (TVG) benchmarks primarily focus on localizing target video segments according to descriptive text queries of the visual content. However, real-world queries are more diverse and often require the model to first understand the user's intent before localization, such as finding visual e...
Yue Feng, Bo-Shen Xu, Zihao Yue et al.· 0 citations
Recent multimodal large language models (MLLMs) achieve strong results on video question answering, yet often fail to recover the temporal structure that makes a video coherent. They may merge neighboring events, miss single-event progression, or infer implausible relations across events. We study structured temporal u...
Guo-Wen Zhang, Bo-Shen Xu, Zihao Yue et al.· 0 citations
This work proposes Vid-PRE (Video Prompt Reasoner and Enhancer), a model-agnostic prompt rewriter that offloads the cognitive burden of reasoning to a dedicated VLM, and introduces VWG-Bench, a comprehensive benchmark spanning 9 reasoning dimensions and 38 fine-grained tasks.
Meng Luo, Yi-Chen Liu, Jia-Hao Wang et al.· 1 citation
PlayWorld is introduced, a benchmark providing 171 scenarios, each with a specified objective, to assess models along four core dimensions: geometry consistency, interaction fidelity, out-of-sight evolution, and insight evolution, and basic ability metrics for video quality and controllability.
Kai Ding, Xi Chen, Ming-Hong Cai et al.· 3 citations
TimePLE is proposed, which reformulates VTG from endpoint prediction to interval-native grounding by predicting a single joint distribution over valid temporal intervals, and curate 90K-scale grounded samples and human-verify 3K-scale benchmark annotations.
Yuhui Zeng, Xin-Yu Mao, Xiaokun Liu et al.· arXiv.org· 0 citations
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