Signals 4 · free daily AI digest

VideoTapestry: Query-Adaptive Memory Refinement for Multi-Agent Long-Video Understanding

Paper recorded by Signals 4 on 2026-10-05 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-05 on arXiv · recorded by Signals 4 on 2026-10-06

Category: cs.CV · 计算机视觉 · first seen 2026-10-06

Abstract

Long-video understanding places substantial demands on memory, as answering questions often requires retrieving information distributed across extended temporal spans. Existing approaches broadly follow two paradigms: query-driven exploration, which is sensitive to localization errors, and query-independent memory construction, which may omit question-specific details. We introduce VideoTapestry,

Read on arXiv →

#359 most recent of 756 cs.CV papers we have recorded · ↑ newer: Detecting Nighttime Anomalies from NASA Black Marble Using a Generaliz · ↓ older: Cross-dataset harmonization for robust endoscopic image analysis
Cite this page: VideoTapestry: Query-Adaptive Memory Refinement for Multi-Agent Long-Video Understanding: the #359 most recent of 756 cs.CV papers we have recorded (as of 2026-10-05). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/videotapestry-query-adaptive-memory-refinement-for-multi-agent-long-video-unders.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.CV papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions