An action seen once and the same action seen three times receive the same action-recognition label, so that supervision does not require a model to count. Answers shared across histories that differ i
Visible-State Supervision and Test-Time Causal View Search improve streaming action recognition by separating representation learning from evidence selection, boosting accuracy with fewer frames.
An action seen once and the same action seen three times receive the same action-recognition label, so that supervision does not require a model to count. Answers shared across histories that differ in event order, accumulation, or relations leave those distinctions unconstrained; we call this state under-specification. Visible-State Supervision (ViSS) addresses it by training on source-grounded probes of event identity, order, accumulation, relation change, and answer sufficiency; under the same recent-frame interface it improves OVO-Bench over a strict-matched generic temporal supervision baseline while remaining comparable on StreamingBench, with the largest gain in hallucination…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Supervising the Visible State: Separating Representation from View Selection in Streaming Video was first seen on Hugging Face · Daily papers on Nov 3, 2026. Since then it has appeared in 4 checks on 1 different day. Its best position was #28 of 30, reached on Nov 3, 13:28 UTC. It dropped off the list after Nov 3, 13:28 UTC.
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#28 on Hugging Face · Daily papers, on Nov 3, 13:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 3, 2026 and has been on the list on 1 day (4 checks) since.