  We propose TubeR: a simple solution for spatio-temporal video action
detection. Different from existing methods that depend on either an off-line
actor detector or hand-designed actor-positional hypotheses like proposals or
anchors, we propose to directly detect an action tubelet in a video by
simultaneously performing action localization and recognition from a single
representation. TubeR learns a set of tubelet-queries and utilizes a
tubelet-attention module to model the dynamic spatio-temporal nature of a video
clip, which effectively reinforces the model capacity compared to using
actor-positional hypotheses in the spatio-temporal space. For videos containing
transitional states or scene changes, we propose a context aware classification
head to utilize short-term and long-term context to strengthen action
classification, and an action switch regression head for detecting the precise
temporal action extent. TubeR directly produces action tubelets with variable
lengths and even maintains good results for long video clips. TubeR outperforms
the previous state-of-the-art on commonly used action detection datasets AVA,
UCF101-24 and JHMDB51-21.
