ambient-intelligence-labs/egoproactive-synth-annotations
EgoProactive synthetic proactive annotations Everything produced by the annotation and synthesis pipelines for the AI Wearables Challenge 2026 EgoProactive Dense timestamped proactive walkthroughs generated with the ambient agent (orchestrator deepseek/deepseek-v4-flash-0731 + vision Qwen3.6-27B), using the held-out-validated dense policy (setup-phase coverage, repetition-collapse, fire-at-onset) and a -0.5s onset correction at chunk-binning. set clips median events/clip… See the full description on the dataset page: https://huggingface.co/datasets/ambient-intelligence-labs/egoproactive-synth-annotations.
EgoProactive synthetic proactive annotations
Everything produced by the annotation and synthesis pipelines for the AI Wearables Challenge 2026 EgoProactive
Dense timestamped proactive walkthroughs generated with the ambient agent (orchestrator deepseek/deepseek-v4-flash-0731 + vision Qwen3.6-27B), using the held-out-validated dense policy (setup-phase coverage, repetition-collapse, fire-at-onset) and a -0.5s onset correction at chunk-binning.
Source videos: facebook/wearable-ai (egoconv/val, egolongqa/val) — disjoint from egoproactive val. egolongqa clips were first screened by an LLM procedural-content filter (egolongqa_procedural.json).
Converted per-8s-chunk training rows (*_rows.jsonl, *_train.jsonl) follow the egoproactive format: answers[j] = $interrupt$<utterance> | $silent$ aligned to video_intervals.
Measured effect (Qwen3.5-4B verbalizer, LoRA r32): egoconv-dense +0.021 G-mean cross-domain (HoloAssist-140: 0.557 -> 0.577, interrupt-F1 +0.064) but -0.042 in-domain (egoproactive-140) — i.e. it trades in-domain fit for out-of-distribution robustness.