CVPR 2023 – STMixer: A One-Stage Sparse Action Detector

In this episode we discuss STMixer: A One-Stage Sparse Action Detector
by Tao Wu, Mengqi Cao, Ziteng Gao, Gangshan Wu, Limin Wang. The paper proposes a new one-stage sparse action detector called STMixer which is based on two core designs. The first design is a query-based adaptive feature sampling module that allows STMixer to mine discriminative features from the entire spatiotemporal domain. The second design is a dual-branch feature mixing module that permits STMixer to dynamically attend and mix video features along the spatial and temporal dimension respectively for better feature decoding. The proposed STMixer achieves state-of-the-art results on the AVA, UCF101-24, and JHMDB datasets.