Extending Lifelog Retrieval to Multi-stream Video Retrieval at the CASTLE Challenge 2025

摘要

We present the DCU team's system for the CASTLE Challenge at ACM Multimedia 2025, which explores video retrieval and question answering in egocentric, multi-user environments. Our system adapts techniques developed for lifelogging, particularly event-based semantic retrieval and QA pipelines, to the CASTLE dataset with minimal architectural changes. It combines vision-language embeddings, transcript-based retrieval, and person tracking to support both automatic and interactive search workflows. In the interactive track, we introduce a modular interface for narrative reconstruction and exploratory search. Qualitative results show that the system can generate plausible, evidence-based answers to complex multimodal queries. These findings suggest that lifelog retrieval systems offer a viable foundation for broader egocentric video analysis.