Author

Changbeen Kim

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#artificial intelligence Preprint Sep 2026

VidOmni-Bench: A Benchmark for Fine-Grained Video Understanding via Spatio-Temporal Event Verification across Complexity and Duration

VidOmni-Bench is introduced, a benchmark that requires models to verify whether each event in dense video captions is supported by the video, and reveals three key findings: Video-LLMs frequently generate hallucinated descriptions in dense video captioning, and they also struggle as verifiers.

Changbeen Kim, Jun-Qing Chang, Kipyo Kim et al. · 0 citations

Preprint Aug 2026

Beyond Visual Boundaries: Rethinking Scene Segmentation for Movie RAG

Understanding long-form video remains a fundamental challenge for multimodal large language models (MLLMs). Sparse frame sampling fails to capture fine-grained visual details, while dense sampling quickly exceeds context length limits. Retrieval-augmented generation (RAG) offers a promising middle ground by selectively...

Dong-Hee Kim, Seonwoo Choi, Changbeen Kim et al. · 0 citations