Temporal Zoom Networks: Distance Regression and Continuous Depth for Efficient Action Localization

• URL: http://arxiv.org/abs/2511.03943v2
• Date: Sun, 09 Nov 2025 10:03:03 GMT
• Title: Temporal Zoom Networks: Distance Regression and Continuous Depth for Efficient Action Localization
• Abstract summary: Boundary Distance Regression (BDR) replaces classification with signed-distance regression and zero-crossing extraction.<n>BDR retrofits to existing methods with about 50 lines of code, improving mAP@0.7 by 1.8 to 3.1 percent (average +2.4)<n>On THUMOS14, ATR achieves 56.5 percent mAP@0.7 at 151G FLOPs versus 53.6 percent at 198G for the Uniform-6 baseline.
• Score: 6.908972852063454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal action localization requires precise boundaries, yet most methods apply uniform computation despite varying boundary difficulty. We propose two complementary contributions. Boundary Distance Regression (BDR) replaces classification with signed-distance regression and zero-crossing extraction. Under idealized assumptions (i.i.d. Laplace noise, uniform stride, sufficient capacity), BDR approaches the Cramer-Rao lower bound, yielding variance on the order of (Delta t)^2 / T (appearing as O((Delta t)^2) for fixed-video inference). The variance ratio R = Var[b_BDR] / Var[b_cls] scales as O((Delta t)^2 / W) for plateau width W approx 2*kappa, with empirical scaling appearing stronger (O((Delta t)^2 / W^2)) due to amplification factors (see Section~4). Empirically, BDR reduces boundary variance by 3.3x to 16.7x (R = 0.06 to 0.30) via four amplification factors. BDR retrofits to existing methods with about 50 lines of code, improving mAP@0.7 by 1.8 to 3.1 percent (average +2.4). Adaptive Temporal Refinement (ATR) learns continuous depth allocation tau in [0,1] to adapt computation, avoiding discrete routing complexity. On THUMOS14, ATR achieves 56.5 percent mAP@0.7 at 151G FLOPs versus 53.6 percent at 198G for the Uniform-6 baseline (24 percent FLOPs reduction, 118 ms vs. 167 ms latency). Gains scale with boundary heterogeneity: THUMOS14 (+2.9), FineAction (+2.7), ActivityNet (+1.8). Training overhead (1.29x baseline) is mitigated via knowledge distillation, with students retaining 99.5 percent performance. Code will be released.

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