Ambient @ EgoProactive 2026 : Proactive Egocentric Assistance with Visually Grounded Supervision

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egocentric-vision wearable-ai proactive-assistance visual-grounding single-token-classification video-agent

Summary

This paper presents a proactive egocentric assistance method using single-token classification and visually grounded supervision, ranking first in the large-model division of the ECCV 2026 Wearable AI Challenge.

We present our submission to the EgoProactive track of the ECCV 2026 Wearable AI Challenge, which ranked first in the large-model division and second in the <=2B division. The task requires a wearable assistant to decide after each eight-second segment of egocentric video whether to intervene or remain silent. Our approach has two main components. First, we reformulate intervention timing as single-token classification. Rather than generating either interrupt<utterance> or silent, the model predicts yes or no, and we derive the decision from the renormalised probabilities of these two tokens. This formulation improved macro-F1 by 0.249 and G-mean by 0.30 over free-form generation. Second, because labelled data were limited to the released validation set, we generated additional supervision using a tool-calling video agent that inspects each clip and assigns intervention timestamps. A narration-only alternative was four times larger and ten times cheaper, but transferred worse than supervision from an unrelated real corpus, suggesting that visual grounding is more important than annotation volume for this task.
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Paper page - Ambient @ EgoProactive 2026 : Proactive Egocentric Assistance with Visually Grounded Supervision

Source: https://huggingface.co/papers/2609.07099

Abstract

The approach reformulates intervention timing as single-token classification and uses a video agent for visual supervision to improve wearable assistant decision-making.

We present our submission to the EgoProactive track of the ECCV 2026 Wearable AI Challenge, which ranked first in the large-model division and second in the <=2B division. The task requires a wearable assistant to decide after each eight-second segment of egocentric video whether to intervene or remain silent. Our approach has two main components. First, we reformulate intervention timing assingle-token classification. Rather than generating either interrupt<utterance> or silent, the model predicts yes or no, and we derive the decision from therenormalised probabilitiesof these two tokens. This formulation improvedmacro-F1by 0.249 andG-meanby 0.30 overfree-form generation. Second, because labelled data were limited to the released validation set, we generated additional supervision using atool-calling video agentthat inspects each clip and assigns intervention timestamps. A narration-only alternative was four times larger and ten times cheaper, but transferred worse than supervision from an unrelated real corpus, suggesting thatvisual groundingis more important than annotation volume for this task.

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