arXiv Computer Vision
Oct 23

Retrieval-Augmented Online Textual Memory for Episodic Memory Video Question Answering

The paper introduces a retrieval-augmented online textual memory for episodic memory video question answering, focusing on long egocentric videos. It evaluates how different retrieval units, query formulations, context budgets, and reranking strategies affect multiple-choice answering on QAEgo4D-Closed, keeping the generated memory fixed. The best configuration, Parent, matches strong baselines while exposing only a small fraction of the textual memory, and the study shows that better temporal coverage does not always improve accuracy, highlighting the need for evidence-aware representations.

By Raffaele Cali, Giuseppe Lando, Rosario Forte, Antonino Furnari