---
title: "EgoMemory — paper by Tang et al. (2026) — summary and ideas"
description: "The authors build EgoMemory on top of Ego4D. 9.04 points. 69 references. Among them: 0."
authors: ["Yuanmin Tang", "Jue Zhang", "Xiaoting Qin", "Jing Yu", "Meikang Qiu", "Gaopeng Gou", "Gang Xiong", "Qingwei Lin", "Saravan Rajmohan", "Dongmei Zhang", "Qi Wu"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.362/"
doi: "10.18653/v1/2026.findings-acl.362"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/tang-2026-egomemory-memory-augmented-personalized-retrieval/"
---
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# EgoMemory: Memory-Augmented Personalized Retrieval for Long-Context Egocentric Video

**Authors:** Yuanmin Tang, [Jue Zhang](https://md.research.elamur.ai/free/en/authors/jue-zhang/), Xiaoting Qin, Jing Yu, Meikang Qiu, Gaopeng Gou, Gang Xiong, [Qingwei Lin](https://md.research.elamur.ai/free/en/authors/qingwei-lin/), [Saravan Rajmohan](https://md.research.elamur.ai/free/en/authors/saravan-rajmohan/), [Dongmei Zhang](https://md.research.elamur.ai/free/en/authors/dongmei-zhang/) and [Qi Wu](https://md.research.elamur.ai/free/en/authors/qi-wu/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.362/) · [PDF](https://aclanthology.org/2026.findings-acl.362.pdf) · [DOI 10.18653/v1/2026.findings-acl.362](https://doi.org/10.18653/v1/2026.findings-acl.362) · [OpenAlex](https://openalex.org/W7166852908)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Wearable cameras such as AR glasses could let people search their own past experiences with natural-language questions. Existing egocentric video benchmarks, however, test retrieval inside single clips or short time spans and ignore whether queried objects belong to the user. The authors build EgoMemory on top of Ego4D. They group each participant's recordings into one personal history, and an MLLM extracts structured metadata (category, colour, status and so on) about user-linked objects, forming a per-user memory bank. Ego4D NLQ queries are screened first by GPT-4o and then by human annotators, so that only queries that truly depend on personal context remain. The paper also proposes EgoRetriever, a training-free, one-stage method. A multimodal LLM follows a reflective Chain-of-Thought prompt: it describes the reference frame, proposes intents consistent with the memory, and discards implausible ones. It then writes a detailed target-clip description that is used for ranking. On EgoMemory, EgoCVR and EgoLife, EgoRetriever outperforms prior baselines at lower cost and latency. This suggests that grounding queries in personal history meaningfully improves lifelog search.

## Key points
- EgoMemory covers 245 Ego4D videos from 45 participants, with 165,795 user-specific object annotations. It has 639 human-verified queries (about 91.6% personal) over a pool of 2,228 candidate clips totalling 64.25 h.
- In the authors' analysis, 88.4% of Ego4D queries explicitly refer to personal objects, which motivates the personalization focus.
- EgoRetriever with GPT-4o averages 48.2 versus 41.3 for TFR-CVR. It also cuts latency from 1.00 s to 0.70 s and per-query cost from $0.007 to $0.004. The GPT-4o-mini variant runs at 0.50 s and $0.002.
- A larger memory bank helps consistently. Using the full bank instead of a single sample raises mR@3 by about 20 points, and growing memory from 20% to 100% of videos adds 9.04 points on average.
- Most errors come from object disambiguation (74%) or context misinterpretation (21%). Annotations, prompts and code are promised for release.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Yuanmin Tang, Jue Zhang, Xiaoting Qin, Jing Yu, Meikang Qiu, Gaopeng Gou, Gang Xiong, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang and Qi Wu. 2026. EgoMemory: Memory-Augmented Personalized Retrieval for Long-Context Egocentric Video. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{tang2026egomemory,
  title = {{EgoMemory: Memory-Augmented Personalized Retrieval for Long-Context Egocentric Video}},
  author = {Yuanmin Tang, Jue Zhang, Xiaoting Qin, Jing Yu, Meikang Qiu, Gaopeng Gou, Gang Xiong, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang and Qi Wu},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.362},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.362}
}
```

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