Device-free activity recognition plays a crucial role in smart building, security, and human–
computer interaction, which shows its strength in its convenience and cost-efficiency …

Brain imaging techniques play an important role in determining the causes of brain cell
injury. Therefore, earlier diagnosis of these diseases can be led to give rise to bring huge …

This paper studies the BERT pretraining of video transformers. It is a straightforward but
worth-studying extension given the recent success from BERT pretraining of image …

This paper presents X3D, a family of efficient video networks that progressively expand a
tiny 2D image classification architecture along multiple network axes, in space, time, width …

Abstract We introduce Video Transformer (VidTr) with separable-attention for video
classification. Comparing with commonly used 3D networks, VidTr is able to aggregate …

Abstract Convolutional Neural Networks (CNNs) use pooling to decrease the size of
activation maps. This process is crucial to increase the receptive fields and to reduce …

Group convolution has been shown to offer great computational savings in various 2D
convolutional architectures for image classification. It is natural to ask: 1) if group convolution …

In natural images, information is conveyed at different frequencies where higher frequencies
are usually encoded with fine details and lower frequencies are usually encoded with global …

With the burgeoning growth of online video platforms and the escalating volume of video
content, the demand for proficient video understanding tools has intensified markedly. Given …

Learning to capture long-range relations is fundamental to image/video recognition. Existing
CNN models generally rely on increasing depth to model such relations which is highly …