We present SlowFast networks for video recognition. Our model involves (i) a Slow pathway,
operating at low frame rate, to capture spatial semantics, and (ii) a Fast pathway, operating …

Abstract We introduce the Action Transformer model for recognizing and localizing human
actions in video clips. We repurpose a Transformer-style architecture to aggregate features …

Action recognition has typically treated actions and activities as monolithic events that occur
in videos. However, there is evidence from Cognitive Science and Neuroscience that people …

To understand the world, we humans constantly need to relate the present to the past, and
put events in context. In this paper, we enable existing video models to do the same. We …

Many believe that the successes of deep learning on image understanding problems can be
replicated in the realm of video understanding. However, due to the scale and temporal …

We propose an end-to-end pipeline, named Watch Once Only (WOO), for video action
detection. Current methods either decouple video action detection task into separated …

Traditional video action detectors typically adopt the two-stage pipeline, where a person
detector is first employed to yield actor boxes and then 3D RoIAlign is used to extract actor …

Box-supervised instance segmentation has gained much attention as it requires only simple
box annotations instead of costly mask or polygon annotations. However, existing box …

Spatio-temporal action detection (STAD) aims to classify the actions present in a video and
localize them in space and time. It has become a particularly active area of research in …

Despite the advances in Human Activity Recognition, the ability to exploit the dynamics of
human body motion in videos has yet to be achieved. In numerous recent works …