Capstone 12 — Video Understanding Pipeline (Scene, QA, Search)
Twelve Labs productized Marengo + Pegasus. VideoDB shipped the CRUD-for-video API. AI2's Molmo 2 published open VLM checkpoints. Gemini long-context handles hours of video natively. TimeLens-100K defined temporal grounding at scale. The 2026 pipeline is settled: scene segmentation, per-scene caption + embedding, transcript alignment, multi-vector index, and a query that answers with (start, end) timestamps plus frame previews. The capstone is ingesting 100 hours, hitting public benchmarks, and measuring hallucination on counting and action questions. Phases exercised: P4 · P6 · P7 · P11 · P12 · P17 Long-form video QA is the most bandwidth-hungry multimodal problem at 2026 scale. Gemini 2.5 Pro can read a 2-hour video natively, but ingesting 100 hours of video into a queryable corpus still requires a scene-level index. The production shape combines scene segmentation (TransNetV2 or PySceneDetect), per-scene captioning with a VLM (Gemini 2.5, Qwen3-VL-Max, or Molmo 2), transcript alignment (Whisper-v3-turbo with word timestamps), and a multi-vector index that stores caption, frame embedding, and transcript side by side. The query pipeline answers with (start, end) timestamps plus frame previews. Benchmarks are public (ActivityNet-QA, NeXT-GQA) plus your own 100-query custom set. Hallucination on counting and action-type questions is the known-hard failure class; the capstone explicitly measures it. Three pipelines run in parallel at ingest. Scene segmentation cuts the video into scenes. VLM captioning generates a…
Capstone 12 — Video Understanding Pipeline (Scene, QA, Search): Twelve Labs productized Marengo + Pegasus. VideoDB shipped the CRUD-for-video API. AI2's Molmo…
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