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arXiv · 2609.35416

When Should the Count Change? Learning State Maintenance for Causal Video Counting

Pengyiang Liu·Dongyue Lyu·Junbo Niu·Zhongyue Shi·Jiahao Xie·Si Liu
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Abstract

Continuous video counting requires distinguishing new observations from new objects or completed events. We introduce StaMina (State Maintenance), which learns to maintain counting state through state-conditioned updates. Recurrent visual context supports recognition; learned transitions maintain visibility, persistent identities, and completed-event records. A differentiable recurrence trains event transitions over legal paths constrained by count endpoints; visibility and association objectives train the object branch. A multi-source pipeline organizes 39.8K spatial queries and complementary event annotations into counting trajectories. On SVCBench, we evaluate counting adaptation with partial video overlap and held-out groups of linked annotations. Under prefix replay (Full) and persistent streaming (Stream), 4B and 8B models reach 41.9/36.4 and 44.9/38.2 Gaussian Precision Accuracy, respectively. The 8B model gains 10.9/3.2 points over Counting-SFT on the same queries. Matched-graph comparisons isolate phase conditioning and trajectory supervision, assessing training objectives alongside hard decisions. Online video benchmarks and count-conditioned decisions assess online understanding and task eligibility. Project Page: https://PLACEHOLDER.github.io/StaMina/

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Pengyiang Liu, Dongyue Lyu, Junbo Niu, Zhongyue Shi, Jiahao Xie, Si Liu. 2026-09-28. When Should the Count Change? Learning State Maintenance for Causal Video Counting. https://arxiv.org/abs/2609.35416

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