DAEP: Difficulty-Aware Evidence Planning for Medical Video Corpus Temporal Answer Grounding
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Computer Science > Computer Vision and Pattern Recognition
Title:DAEP: Difficulty-Aware Evidence Planning for Medical Video Corpus Temporal Answer Grounding
Abstract:We describe DAEP, team BIGC's submission to NLPCC 2026 Shared Task 1 Track 3: Difficulty-Aware Temporal Answer Grounding in Video Corpus (DA-TAGVC). The task requires retrieving the target video from 50 candidates and localizing the answer-supporting span. DAEP ranks videos with subtitle, visual, and procedural-context evidence, expands high-scoring anchors into temporal spans, and reranks spans for final output. Its main design is to convert the task-provided simple/complex input label into an inference-time evidence plan controlling modality weights, Top-K aggregation, boundary threshold, expansion length, and reranking strength. In the official evaluation, BIGC ranks first among ten systems with an Average score of 0.2728. Validation ablations show that visual evidence, procedural context, and difficulty-aware planning improve ranking quality, with the largest gain on complex questions.
| Comments: | 12 pages, 2 figures, 5 tables, accepted by NLPCC 2026 Shared Task Track 3 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL) |
| ACM classes: | I.2.7; I.5.4; H.3.3 |
| Cite as: | arXiv:2608.06869 [cs.CV] |
| (or arXiv:2608.06869v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2608.06869
arXiv-issued DOI via DataCite (pending registration)
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