Abstract Natural Language Processing (NLP) is one of the most captivating applications of
Deep Learning. In this survey, we consider how the Data Augmentation training strategy can …

Dropout is one of the most popular regularization methods in the scholarly domain for
preventing a neural network model from overfitting in the training phase. Develo** an …

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 …

Network architecture plays a key role in the deep learning-based computer vision system.
The widely-used convolutional neural network and transformer treat the image as a grid or …

Pre-training video transformers on extra large-scale datasets is generally required to
achieve premier performance on relatively small datasets. In this paper, we show that video …

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

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 …

In this work, we introduce Dual Attention Vision Transformers (DaViT), a simple yet effective
vision transformer architecture that is able to capture global context while maintaining …

Unsupervised large-scale vision-language pre-training has shown promising advances on
various downstream tasks. Existing methods often model the cross-modal interaction either …