arXiv cs.AI· Zhenqi He, Dunyuan Xu, Yang Bo, Kien T. Pham, Yanghao Wang, Shuhan Li, Xintong Guo, Yuyang He, Fang Liu, Huankang Guan, Jingyan Chen, Rui Liu, Long Chen·· 3 小时前AI 评分36
MemInStream：重新思考流式视频记忆能力评测
MemInStream: Memorizing or Merely Recognizing? Rethinking Streaming Video Memory Evaluation
AI 导读
研究者提出 MemInStream 基准，专门评测流式视频理解中的记忆能力。该基准采用证据控制的验证协议，为每道题标注时间定位的证据区间，只保留模型能依据证据作答、但无法仅凭近期观察、证据后观察或纯文本回答的题目，以区分记忆缺陷与理解失败。基准覆盖情节检索、状态跟踪与更新、多证据整合和情境记忆四大类别。
正文 · 原文
Authors:Zhenqi He, Dunyuan Xu, Yang Bo, Kien T. Pham, Yanghao Wang, Shuhan Li, Xintong Guo, Yuyang He, Fang Liu, Huankang Guan, Jingyan Chen, Rui Liu, Long Chen
Abstract:Streaming video understanding aims to continuously process incoming visual observations, making the retention and retrieval of relevant information increasingly important as the video history grows. Although existing benchmarks include memory-related questions, question-answering accuracy alone does not reliably measure memory capabilities. Incorrect predictions may arise from failures in visual understanding rather than forgetting, whereas correct predictions may rely on linguistic priors or recent observations without requiring historical memory. To address these ambiguities, we introduce MemInStream, a benchmark dedicated to evaluating memory capabilities in streaming video understanding. We develop an evidence-controlled validation protocol that associates each question with temporally localized evidence intervals. The protocol validates evidence solvability and memory necessity by retaining questions that models can answer from the annotated evidence but cannot resolve using recent observations, post-evidence observations, or textual input alone. This helps disentangle memory limitations from understanding failures while reducing memory-independent shortcuts. MemInStream provides a systematic evaluation of memory capabilities through four major categories, covering Episodic Retrieval, State Tracking \& Updating, Multi-Evidence Integration, and Contextual Memory across diverse video scenarios. Overall, \textsc{MemInStream} provides a comprehensive and reliable benchmark for systematically evaluating memory capabilities in streaming video understanding.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.15640 [cs.CV] |
| (or arXiv:2611.15640v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.15640 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhenqi He [view email]
[v1]
Wed, 11 Nov 2026 15:52:19 UTC (1,349 KB)
来源：arXiv cs.AI · arxiv.org