FDDet: Frequency-Decoupling for Boundary Refinement in Temporal Action Detection

• URL: http://arxiv.org/abs/2504.00647v1
• Date: Tue, 01 Apr 2025 10:57:37 GMT
• Title: FDDet: Frequency-Decoupling for Boundary Refinement in Temporal Action Detection
• Authors: Xinnan Zhu, Yicheng Zhu, Tixin Chen, Wentao Wu, Yuanjie Dang,
• Abstract summary: Large-scale pre-trained video encoders tend to introduce background clutter and irrelevant semantics, leading to context confusion and boundaries.<n>We propose a frequency-aware decoupling network that improves action discriminability by filtering out noisy semantics captured by pre-trained models.<n>Our method achieves state-of-the-art performance on temporal action detection benchmarks.
• Score: 4.015022008487465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal action detection aims to locate and classify actions in untrimmed videos. While recent works focus on designing powerful feature processors for pre-trained representations, they often overlook the inherent noise and redundancy within these features. Large-scale pre-trained video encoders tend to introduce background clutter and irrelevant semantics, leading to context confusion and imprecise boundaries. To address this, we propose a frequency-aware decoupling network that improves action discriminability by filtering out noisy semantics captured by pre-trained models. Specifically, we introduce an adaptive temporal decoupling scheme that suppresses irrelevant information while preserving fine-grained atomic action details, yielding more task-specific representations. In addition, we enhance inter-frame modeling by capturing temporal variations to better distinguish actions from background redundancy. Furthermore, we present a long-short-term category-aware relation network that jointly models local transitions and long-range dependencies, improving localization precision. The refined atomic features and frequency-guided dynamics are fed into a standard detection head to produce accurate action predictions. Extensive experiments on THUMOS14, HACS, and ActivityNet-1.3 show that our method, powered by InternVideo2-6B features, achieves state-of-the-art performance on temporal action detection benchmarks.

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