Retrieval-Augmented Online Textual Memory for Episodic Memory Video Question Answering
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2608. 12627v1 Announce Type: cross Abstract: Long-horizon egocentric memory transforms continuous first-person video and audio into a searchable record of past experiences.
CapMem is a new benchmark for evaluating caption-based episodic memory in egocentric video. It contains 75 videos (33.7 hours total) and 1,000 multiple-choice questions across 16 scenarios, designed to test the Episodic Memory Video Caption QA task. Experiments show that using captions as memory outperforms direct VideoQA on long videos, and a caption-guided retrieve-and-verify approach further boosts accuracy.
arXiv:2609.40195v1 Announce Type: cross Abstract: Long-term egocentric video enables personalized AI assistants to reason about daily life. However, as video histories grow to hundreds of hours spann...
arXiv:2601.19060v2 Announce Type: replace-cross Abstract: Multimodal Retrieval-Augmented Generation (MM-RAG) augments visual question answering (VQA) with external knowledge, but many systems rely on...
BuddyVQA is a new benchmark for companion‑style question answering on egocentric streaming video, comprising 21.6K questions tied to 6K highlight moments across 1,012 long first‑person videos. It emphasizes two often overlooked aspects of daily first‑person QA: ego‑deictic expressions and interactively chained questions, requiring models to resolve visual pronouns and infer user intent within a long‑form streaming context. The authors propose MyBuddy, a multimodal chain‑of‑thought QA assistant that uses a question filter and multi‑level memory to efficiently retrieve visual and QA information, achieving significant performance gains on BuddyVQA and generalizing to other streaming and common video QA benchmarks.
arXiv:2608. 07663v1 Announce Type: cross Abstract: When videos extend from hours to days, directly processing them end-to-end becomes impractical for current Multi-modal Large Language Models (MLLMs).