Understanding Video Transformers for Segmentation: A Survey of
  Application and Interpretability
Rezaul Karim Richard P. Wildes
Video segmentation encompasses a wide range of categories of problem
formulation, e.g., object, scene, actor-action and multimodal video
segmentation, for delineating task-specific scene components with pixel-level
masks. Recently, approaches in this research area shifted from concentrating on
ConvNet-based to transformer-based models. In addition, various
interpretability approaches have appeared for transformer models and video
temporal dynamics, motivated by the growing interest in basic scientific
understanding, model diagnostics and societal implications of real-world
deployment. Previous surveys mainly focused on ConvNet models on a subset of
video segmentation tasks or transformers for classification tasks. Moreover,
component-wise discussion of transformer-based video segmentation models has
not yet received due focus. In addition, previous reviews of interpretability
methods focused on transformers for classification, while analysis of video
temporal dynamics modelling capabilities of video models received less
attention. In this survey, we address the above with a thorough discussion of
various categories of video segmentation, a component-wise discussion of the
state-of-the-art transformer-based models, and a review of related
interpretability methods. We first present an introduction to the different
video segmentation task categories, their objectives, specific challenges and
benchmark datasets. Next, we provide a component-wise review of recent
transformer-based models and document the state of the art on different video
segmentation tasks. Subsequently, we discuss post-hoc and ante-hoc
interpretability methods for transformer models and interpretability methods
for understanding the role of the temporal dimension in video models. Finally,
we conclude our discussion with future research directions.