# DAEP: Difficulty-Aware Evidence Planning for Medical Video Corpus Temporal Answer Grounding

arXiv: https://arxiv.org/abs/2608.06869 · HTML: https://mlsanity.com/paper/2608.06869/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 31 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Localizing Moments in Video with Natural Language](https://mlsanity.com/paper/1708.01641/) (2017)
- [TALL: Temporal Activity Localization via Language Query](https://mlsanity.com/paper/1705.02101/) (2017)
- [Span-based Localizing Network for Natural Language Video Localization](https://mlsanity.com/paper/2004.13931/) (2020)
- [TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval](https://mlsanity.com/paper/2001.09099/) (2020)
- [UniVTG: Towards Unified Video-Language Temporal Grounding](https://mlsanity.com/paper/2307.16715/) (2023)
- [Dense Regression Network for Video Grounding](https://mlsanity.com/paper/2004.03545/) (2020)
- [TVQA+: Spatio-Temporal Grounding for Video Question Answering](https://mlsanity.com/paper/1904.11574/) (2019)
- [Video Corpus Moment Retrieval with Contrastive Learning](https://mlsanity.com/paper/2105.06247/) (2021)
- [Towards Visual-Prompt Temporal Answering Grounding in Medical Instructional Video](https://mlsanity.com/paper/2203.06667/) (2022)
- [CONQUER: Contextual Query-aware Ranking for Video Corpus Moment Retrieval](https://mlsanity.com/paper/2109.10016/) (2021)
- [A Dataset for Medical Instructional Video Classification and Question Answering](https://mlsanity.com/paper/2201.12888/) (2022)
- [Text-based Localization of Moments in a Video Corpus](https://mlsanity.com/paper/2008.08716/) (2020)
- [Learning to Locate Visual Answer in Video Corpus Using Question](https://mlsanity.com/paper/2210.05423/) (2022)
- [Visual Answer Localization with Cross-modal Mutual Knowledge Transfer](https://mlsanity.com/paper/2210.14823/) (2022)
- [Improving Video Corpus Moment Retrieval with Partial Relevance Enhancement](https://mlsanity.com/paper/2402.13576/) (2024)
- [Overview of the NLPCC 2025 Shared Task 4: Multi-modal, Multilingual, and Multi-hop Medical Instructional Video Question Answering Challenge](https://mlsanity.com/paper/2505.06814/) (2025)
- [Understanding Complexity in VideoQA via Visual Program Generation](https://mlsanity.com/paper/2505.13429/) (2025)
- [M3-Med: A Benchmark for Multi-lingual, Multi-modal, and Multi-hop Reasoning in Medical Instructional Video Understanding](https://mlsanity.com/paper/2507.04289/) (2025)
- [An Entropy Clustering Approach for Assessing Visual Question Difficulty](https://mlsanity.com/paper/2004.05595/) (2020)
- [Overview of the NLPCC 2026 Shared Task 1: Difficulty-Aware Multilingual and Multimodal Medical Instructional Video Understanding Evaluation](https://mlsanity.com/paper/2607.06618/) (2026)

## Abstract (arXiv)

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.