  Detecting visual content on language expression has become an emerging topic
in the community. However, in the video domain, the existing setting, i.e.,
spatial-temporal video grounding (STVG), is formulated to only detect one
pre-existing object in each frame, ignoring the fact that language descriptions
can involve none or multiple entities within a video. In this work, we advance
the STVG to a more practical setting called described spatial-temporal video
detection (DSTVD) by overcoming the above limitation. To facilitate the
exploration of DSTVD, we first introduce a new benchmark, namely DVD-ST.
Notably, DVD-ST supports grounding from none to many objects onto the video in
response to queries and encompasses a diverse range of over 150 entities,
including appearance, actions, locations, and interactions. The extensive
breadth and diversity of the DVD-ST dataset make it an exemplary testbed for
the investigation of DSTVD. In addition to the new benchmark, we further
present two baseline methods for our proposed DSTVD task by extending two
representative STVG models, i.e., TubeDETR, and STCAT. These extended models
capitalize on tubelet queries to localize and track referred objects across the
video sequence. Besides, we adjust the training objectives of these models to
optimize spatial and temporal localization accuracy and multi-class
classification capabilities. Furthermore, we benchmark the baselines on the
introduced DVD-ST dataset and conduct extensive experimental analysis to guide
future investigation. Our code and benchmark will be publicly available.
