  Detecting and segmenting individual objects, regardless of their category, is
crucial for many applications such as action detection or robotic interaction.
While this problem has been well-studied under the classic formulation of
spatio-temporal grouping, state-of-the-art approaches do not make use of
learning-based methods. To bridge this gap, we propose a simple learning-based
approach for spatio-temporal grouping. Our approach leverages motion cues from
optical flow as a bottom-up signal for separating objects from each other.
Motion cues are then combined with appearance cues that provide a generic
objectness prior for capturing the full extent of objects. We show that our
approach outperforms all prior work on the benchmark FBMS dataset. One
potential worry with learning-based methods is that they might overfit to the
particular type of objects that they have been trained on. To address this
concern, we propose two new benchmarks for generic, moving object detection,
and show that our model matches top-down methods on common categories, while
significantly out-performing both top-down and bottom-up methods on
never-before-seen categories.
