Abstract We present Masked Feature Prediction (MaskFeat) for self-supervised pre-training
of video models. Our approach first randomly masks out a portion of the input sequence and …

In this paper, we study Multiscale Vision Transformers (MViTv2) as a unified architecture for
image and video classification, as well as object detection. We present an improved version …

Abstract We present Multiscale Vision Transformers (MViT) for video and image recognition,
by connecting the seminal idea of multiscale feature hierarchies with transformer models …

Existing video understanding approaches, such as 3D convolutional neural networks and
Transformer-Based methods, usually process the videos in a clip-wise manner. Hence huge …

A large proportion of construction accidents are caused by unintentional and unsafe actions
and behaviors. It is of significant difficulties and ineffectiveness to monitor unsafe behaviors …

Micro-videos have recently gained immense popularity, sparking critical research in micro-
video recommendation with significant implications for the entertainment, advertising, and e …

We introduce AugLy, a data augmentation library with a focus on adversarial robustness.
AugLy provides a wide array of augmentations for multiple modalities (audio, image, text, & …

We introduce the task of spotting temporally precise, fine-grained events in video (detecting
the precise moment in time events occur). Precise spotting requires models to reason …

Machine learning models often fail to generalize well under distributional shifts.
Understanding and overcoming these failures have led to a research field of Out-of …

In this paper we study multi-label atomic activity recognition. Despite the notable progress in
action recognition it is still challenging to recognize atomic activities due to a deficiency in …