NEST: Narrative Event Structures in Time for Long Video Understanding
Recent progress in vision-language models has enabled processing of increasingly long video sequences, but handling extended token streams does not translate to understanding complex narrative structure in long videos. Existing long-video benchmarks focus on needle-in-a-haystack retrieval rather than evaluating how low-level actions form events, interact across time, and drive narratives, for example whether a model can connect an early job loss to a later relationship breakup despite intervening scenes or flashbacks. We introduce NEST (Narrative Event Structures in Time for Long Video Understanding), a dataset of 1,005 full-length movies (avg. 98 minutes), each annotated with about 103 m...
| Authors | Ali Asgarov, Kaushik Narasimhan, Najibul Haque Sarker et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 June 2026 |
| Last updated at source | 5 September 2026 |
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