Gaze Archive: Enhancing Human Memory through Active Visual Logging on Smart Glasses
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911295957106688 |
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| author | Ren, Haoxin Lu, Feng |
| author_facet | Ren, Haoxin Lu, Feng |
| contents | People today are overwhelmed by massive amounts of information, leading to cognitive overload and memory burden. Traditional visual memory augmentation methods are either effortful and disruptive or fail to align with user intent. To address these limitations, we propose Gaze Archive, a novel visual memory enhancement paradigm through active logging on smart glasses. It leverages human gaze as a natural attention indicator, enabling both intent-precise capture and effortless-and-unobtrusive interaction. To implement Gaze Archive, we develop GAHMA, a technical framework that enables compact yet intent-aligned memory encoding and intuitive memory recall based on natural language queries. Quantitative experiments on our newly constructed GAVER dataset show that GAHMA achieves more intent-precise logging than non-gaze baselines. Through extensive user studies in both laboratory and real-world scenarios, we compare Gaze Archive with other existing memory augmentation methods. Results demonstrate its advantages in perceived effortlessness, unobtrusiveness and overall preference, showing strong potential for real-world deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16214 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Gaze Archive: Enhancing Human Memory through Active Visual Logging on Smart Glasses Ren, Haoxin Lu, Feng Human-Computer Interaction People today are overwhelmed by massive amounts of information, leading to cognitive overload and memory burden. Traditional visual memory augmentation methods are either effortful and disruptive or fail to align with user intent. To address these limitations, we propose Gaze Archive, a novel visual memory enhancement paradigm through active logging on smart glasses. It leverages human gaze as a natural attention indicator, enabling both intent-precise capture and effortless-and-unobtrusive interaction. To implement Gaze Archive, we develop GAHMA, a technical framework that enables compact yet intent-aligned memory encoding and intuitive memory recall based on natural language queries. Quantitative experiments on our newly constructed GAVER dataset show that GAHMA achieves more intent-precise logging than non-gaze baselines. Through extensive user studies in both laboratory and real-world scenarios, we compare Gaze Archive with other existing memory augmentation methods. Results demonstrate its advantages in perceived effortlessness, unobtrusiveness and overall preference, showing strong potential for real-world deployment. |
| title | Gaze Archive: Enhancing Human Memory through Active Visual Logging on Smart Glasses |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2511.16214 |