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ProactEgoMem: Benchmarking Proactive Memory in Streaming Egocentric Video

Paper recorded by Signals 4 on 2026-10-21 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-21 on arXiv · recorded by Signals 4 on 2026-10-22

Category: cs.CV · 计算机视觉 · first seen 2026-10-22

Abstract

Today's multimodal assistants are typically prompted to interpret the visual evidence presented to them. To offload the cognitive burden of remembering across long horizons, wearable assistants should instead proactively acquire and maintain useful memory as the user's surroundings evolve. Doing so on the user's behalf requires identifying what matters, understanding the scene in real time, and in

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#192 most recent of 756 cs.CV papers we have recorded · ↑ newer: ALPS: Visual State Encoders for World Models · ↓ older: Attn-Forcing: Learning Full-Context Attention Proxies for Streaming Vi
Cite this page: ProactEgoMem: Benchmarking Proactive Memory in Streaming Egocentric Video: the #192 most recent of 756 cs.CV papers we have recorded (as of 2026-10-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/proactegomem-benchmarking-proactive-memory-in-streaming-egocentric-video.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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