Nowadays, Human Activity Recognition (HAR) is being widely used in a variety of domains,
and vision and sensor-based data enable cutting-edge technologies to detect, recognize …

Understanding human behavior and activity facilitates advancement of numerous real-world
applications, and is critical for video analysis. Despite the progress of action recognition …

Scale is the primary factor for building a powerful foundation model that could well
generalize to a variety of downstream tasks. However, it is still challenging to train video …

This paper studies a conceptually simple extension of Masked Autoencoders (MAE) to
spatiotemporal representation learning from videos. We randomly mask out spacetime …

The foundation models have recently shown excellent performance on a variety of
downstream tasks in computer vision. However, most existing vision foundation models …

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 …

Capitalizing on large pre-trained models for various downstream tasks of interest have
recently emerged with promising performance. Due to the ever-growing model size, the …

While today's video recognition systems parse snapshots or short clips accurately, they
cannot connect the dots and reason across a longer range of time yet. Most existing video …

Existing visual question answering methods often suffer from cross-modal spurious
correlations and oversimplified event-level reasoning processes that fail to capture event …