mlsanityData to 14 Oct 2026

Overview of the NLPCC 2026 Shared Task 1: Difficulty-Aware Multilingual and Multimodal Medical Instructional Video Understanding Evaluation

arXiv 2607.06618 ↗First posted 7 Jul 2026cs.CV cs.AIcited by 1

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Abstract (arXiv)

Following the CMIVQA, MMI-VQA, and M4IVQA challenges in NLPCC 2023--2025, we introduce the Difficulty-Aware Medical Instructional Video Question Answering (DA-MIVQA) shared task for NLPCC 2026. DA-MIVQA extends previous multilingual and multimodal medical video benchmarks by explicitly distinguishing questions according to the type and complexity of evidence required for answering. Specifically, simple questions can often be answered from subtitle-based textual cues, whereas complex questions require visual grounding, procedural understanding, and cross-modal evidence integration. The challenge contains three tracks: Difficulty-Aware Temporal Answer Grounding in Single Video (DA-TAGSV), Difficulty-Aware Video Corpus Retrieval (DA-VCR), and Difficulty-Aware Temporal Answer Grounding in Video Corpus (DA-TAGVC). The dataset is collected from public medical instructional channels, covers diverse scenarios such as first aid, emergency response, rehabilitation, nursing, and general medical education, and is manually verified with difficulty annotations. This paper presents the task motivation, dataset construction, evaluation protocol, participation overview, competition results, and representative systems of DA-MIVQA. DA-MIVQA provides a practical benchmark for evaluating medical instructional video question answering systems under varying textual, visual, temporal, and procedural reasoning requirements.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 33 references that are in the map. First posted on arXiv 7 Jul 2026.

Builds on

10 of 33 references.

References · 10

  1. 2019TVQA+: Spatio-Temporal Grounding for Video Question Answeringcited by 154
  2. 2021Video Corpus Moment Retrieval with Contrastive Learningcited by 60
  3. 2022Towards Visual-Prompt Temporal Answering Grounding in Medical Instructional Videocited by 43
  4. 2022A Dataset for Medical Instructional Video Classification and Question Answeringcited by 26
  5. 2024Assessing Modality Bias in Video Question Answering Benchmarks with Multimodal Large Language Modelscited by 23
  6. 2022Learning to Locate Visual Answer in Video Corpus Using Questioncited by 10
  7. 2022Visual Answer Localization with Cross-modal Mutual Knowledge Transfercited by 7
  8. 2025Overview of the NLPCC 2025 Shared Task 4: Multi-modal, Multilingual, and Multi-hop Medical Instructional Video Question Answering Challengecited by 5
  9. 2025Med-CRAFT: An Information System for Explainable and Configurable Construction of Multimodal Medical QA Datasetscited by 2
  10. 2025Hierarchical Indexing with Knowledge Enrichment for Multilingual Video Corpus Retrievalcited by 1

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