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SF20K competition shows narrative understanding, not model size, is key for video QA

The first Short-Films 20K (SF20K) Competition, held alongside ICCV 2025, focused on advancing story-level video understanding through an open-ended question-answering task. Using a benchmark of amateur short films and evaluated by GPT-4.1-nano, the competition saw 22 teams submit entries. Analysis of the results indicates that narrative-aware, shot-level processing and multi-stage pipelines are more effective than simple frame sampling, and that subtitle quality significantly impacts performance. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Highlights that information selection and reasoning structure, rather than raw model capacity, are key challenges in long-form video question-answering.

RANK_REASON This is a summary of findings from a competition detailed in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

COVERAGE [1]

  1. arXiv cs.CV TIER_1 · Ridouane Ghermi, Xi Wang, Vicky Kalogeiton, Ivan Laptev ·

    SF20K Competition 2025: Summary and findings

    arXiv:2605.01496v1 Announce Type: new Abstract: This report presents the results and findings of the first edition of the Short-Films 20K (SF20K) Competition, held in conjunction with the SLoMO Workshop at ICCV 2025. The competition is designed to advance story-level video unders…