TubeLite: Lightweight Multi-Actor Spatio-Temporal Action Detection

• URL: http://arxiv.org/abs/2607.04684v1
• Date: Mon, 06 Jul 2026 05:12:30 GMT
• Title: TubeLite: Lightweight Multi-Actor Spatio-Temporal Action Detection
• Abstract summary: TubeLite is a lightweight framework for temporal-temporal action detection in videos.<n>It focuses on stable tube construction and boundary-aware temporal modeling.<n>It achieves strong video-level localization performance despite its compact design.
• Score: 74.20082571588867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatio-temporal action detection in videos requires jointly localizing actors in space and identifying action boundaries over time. A common challenge is constructing temporally stable action tubes, as frame-level detectors often suffer from jitter, fragmentation, and imprecise temporal localization. Many recent approaches address this by introducing heavy spatio-temporal transformers or optical-flow-based pipelines, leading to high computational cost and limited scalability. We propose TubeLite, a lightweight framework for spatio-temporal action detection that focuses on stable tube construction and boundary-aware temporal modeling. TubeLite represents each actor as a tube, defined as a sequence of bounding boxes associated with a single actor over time, and explicitly enforces temporal consistency at both the spatial and semantic levels. The method combines low-jitter actor detection, Gaussian-weighted actor feature extraction, efficient short-term temporal propagation, and a boundary-focused temporal prediction head, while avoiding optical flow and large-scale temporal attention. Despite its compact design, TubeLite achieves strong video-level localization performance. It improves Video-mAP@0.5 by 4.5 and 7.1 percentage points over the best compared method on the MultiSports and UCF101-24 datasets, respectively, with substantially fewer parameters and floating-point operations than transformer-based alternatives, demonstrating that effective spatio-temporal action detection can be obtained through principled, lightweight temporal modeling.

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