  Traditional video action detectors typically adopt the two-stage pipeline,
where a person detector is first employed to generate actor boxes and then 3D
RoIAlign is used to extract actor-specific features for classification. This
detection paradigm requires multi-stage training and inference, and the feature
sampling is constrained inside the box, failing to effectively leverage richer
context information outside. Recently, a few query-based action detectors have
been proposed to predict action instances in an end-to-end manner. However,
they still lack adaptability in feature sampling and decoding, thus suffering
from the issues of inferior performance or slower convergence. In this paper,
we propose two core designs for a more flexible one-stage sparse action
detector. First, we present a query-based adaptive feature sampling module,
which endows the detector with the flexibility of mining a group of
discriminative features from the entire spatio-temporal domain. Second, we
devise a decoupled feature mixing module, which dynamically attends to and
mixes video features along the spatial and temporal dimensions respectively for
better feature decoding. Based on these designs, we instantiate two detection
pipelines, that is, STMixer-K for keyframe action detection and STMixer-T for
action tubelet detection. Without bells and whistles, our STMixer detectors
obtain state-of-the-art results on five challenging spatio-temporal action
detection benchmarks for keyframe action detection or action tube detection.
