EgoSelf: From Memory to Personalized Egocentric Assistant
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Abstract (arXiv)
Egocentric assistants often rely on first-person view data to capture user behavior and context for personalized services. Since different users exhibit distinct habits, preferences, and routines, such personalization is essential for truly effective assistance. However, effectively integrating long-term user data for personalization remains a key challenge. To address this, we introduce EgoSelf, a system that includes a graph-based interaction memory constructed from past observations and a dedicated learning task for personalization. The memory captures temporal and semantic relationships among interaction events and entities, from which user-specific profiles are derived. The personalized learning task is formulated as a prediction problem where the model predicts possible future interactions from individual user's historical behavior recorded in the graph. Extensive experiments demonstrate the effectiveness of EgoSelf as a personalized egocentric assistant. Code is available at https://abie-e.github.io/EgoSelf/.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 50 of its 63 references that are in the map. First posted on arXiv 21 Apr 2026.
Builds on
50 of 63 references.
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38 more
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