---
格式版本: 2
标题: "R4DSG: Relative 4D Scene Graph Memory for Object-Centric Question Answering in Long Egocentric Video"
原文链接: "https://arxiv.org/abs/2608.11017"
发布日期: "2026-08-11"
发布时间校准状态: "found"
发布时间需复核: "否"
发布时间来源: "rule:local:strict_original_body"
发布时间证据: "**\\[v1\\]** Tue, 11 Aug 2026 15:00:15 UTC (3,150 KB)"
发布时间校准原因: "规则确认唯一严格发布时间，来源 local:strict_original_body"
发布时间校准置信度: "high"
发布时间候选数量: 18
发布时间严格候选数量: 6
发布时间原页读取状态: "source template page reused from URL open"
发布时间未找到原因: ""
发布时间校准时间: "2026-08-12T11:29:07+08:00"
发布时间仲裁状态: "skipped"
发布时间仲裁尝试次数: 0
发布时间仲裁耗时毫秒: 0
发现时间: "2026-08-12T11:26:18+08:00"
入库时间: "2026-08-12T03:29:08.005Z"
来源平台: "arXiv 学术论文搜索"
搜索渠道: "source_template"
搜索词: "https://arxiv.org/search/?query=AI&searchtype=all"
匹配关键词:
  - "AI"
相关厂家:
  []
相关专家:
  []
内容类型: "网页"
抓取工具: "Free Fetch + Defuddle"
清洗工具: "Defuddle Markdown + Defuddle/Readability 正文提取"
原始附件:
  []
AI优质: "否"
AI打分: 5
AI分档: "非优质"
AI质检状态: "不通过"
AI打分理由: "论文主题为4D场景图在长视频问答中的应用，与超节点/AI Rack/机柜级AI基础设施完全无关，仅因命中'AI'关键词被检索，属明显无关内容。"
AI质检模型: "ali-deepseek-v4-flash"
AI质检时间: "2026-08-12T11:29:20+08:00"
AI主题相关性: 0
AI来源权威性: 5
AI新颖性: 0
AI技术细节: 0
AI商业部署信号: 0
AI完整性: 0
采集批次: "2026年8月12日2点04分20秒"
采集批次ID: "20260812-020420-289"
去重键: "https://arxiv.org/abs/2608.11017"
---

## Computer Science > Computer Vision and Pattern Recognition

## Title:R4DSG: Relative 4D Scene Graph Memory for Object-Centric Question Answering in Long Egocentric Video

Authors:[Ke Ma](https://arxiv.org/search/cs?searchtype=author&query=Ma,+K), [Yamin Mao](https://arxiv.org/search/cs?searchtype=author&query=Mao,+Y), [Weiming Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+W), [Shuai Tan](https://arxiv.org/search/cs?searchtype=author&query=Tan,+S), [Yijie Zhong](https://arxiv.org/search/cs?searchtype=author&query=Zhong,+Y), [Hao Chen](https://arxiv.org/search/cs?searchtype=author&query=Chen,+H), [Haofen Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+H), [Meng Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+M)

[View PDF](https://arxiv.org/pdf/2608.11017) [HTML (experimental)](https://arxiv.org/html/2608.11017v1)

> Abstract:Long-horizon egocentric video is a rich substrate for wearable AI assistants, but object-centric questions such as where an item was moved, when it last changed state, or why it was relocated remain difficult because caption- and transcript-based memories rarely preserve persistent object identity or structured spatial change. Existing long-video QA methods mainly emphasize temporal grounding and clip retrieval, while prior 3D scene-graph methods typically assume stronger geometry than free-motion wearable RGB video provides, including point clouds, RGB-D input, posed views, sparse reconstruction, or reconstructed scenes. R4DSG introduces a relative 4D scene graph memory for long egocentric video. Instead of storing raw graph sequences, R4DSG converts video into compact queryable memory entries indexed by time, place, persistent objects, anchor-relative change, and local interaction context. The main idea is to separate stable anchors from dynamic objects, maintain persistent object identity across frames, and represent object state through anchor-relative transitions rather than a globally aligned world model. Built on recent RGB-only advances in promptable video segmentation, temporal propagation, and relative 3D lifting, the method produces a retrieval-ready memory directly usable for long-horizon question answering. Evaluation on a 255-question object-related subset from EgoLifeQA shows, under question-only retrieval, a 6.7-point overall gain over EgoRAG-Text and a 12.5-point gain on when questions, which highlights the value of temporally organized object memory. These results position relative 4D scene graphs as a practical memory substrate for wearable assistants, AR systems, and embodied multimedia agents. GitHub Page: [this https URL](https://dualtransparency.github.io/R4DSG/).

| Comments: |  |
| --- | --- |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Multimedia (cs.MM) |
| ACM classes: | I.2.10; H.3.3; H.5.1; I.2.7 |
| Cite as: | [arXiv:2608.11017](https://arxiv.org/abs/2608.11017) \[cs.CV\] |
|  | (or [arXiv:2608.11017v1](https://arxiv.org/abs/2608.11017v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11017](https://doi.org/10.48550/arXiv.2608.11017) |
| Related DOI: | [https://doi.org/10.1145/3767308.3835995](https://doi.org/10.1145/3767308.3835995) |

## Submission history

From: Ke Ma \[[view email](https://arxiv.org/show-email/459e9724/2608.11017)\]  
**\[v1\]** Tue, 11 Aug 2026 15:00:15 UTC (3,150 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.11017) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
