In NLPCC 2026, we introduce the Difficulty-Aware Medical Instructional Video Question Answering (DA-MIVQA) shared task, aiming to extend previous multilingual and multimodal medical video benchmarks. The task explicitly distinguishes questions based on the type and complexity of evidence required for answering. Simple questions can often be addressed using subtitle-based textual cues, while complex questions necessitate visual grounding, procedural understanding, and cross-modal evidence integration.
The DA-MIVQA task comprises three tracks:
- Difficulty-Aware Temporal Answer Grounding in Single Video (DA-TAGSV)
- Difficulty-Aware Video Corpus Retrieval (DA-VCR)
- Difficulty-Aware Temporal Answer Grounding in Video Corpus (DA-TAGVC)
The dataset is collected from public medical instructional channels, covering various 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.
Blogger's Review: The introduction of this task not only enriches the research field of medical video question answering but also promotes deeper development in multimodal learning. By explicitly distinguishing the complexity of questions, DA-MIVQA offers more targeted standards for system evaluation, making it worthy of researchers' attention.