Graphs naturally appear in numerous application domains, ranging from social analysis,
bioinformatics to computer vision. The unique capability of graphs enables capturing the …

Scene graph is a structured representation of a scene that can clearly express the objects,
attributes, and relationships between objects in the scene. As computer vision technology …

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 …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

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

The canonical approach to video action recognition dictates a neural model to do a classic
and standard 1-of-N majority vote task. They are trained to predict a fixed set of predefined …

Temporal modeling still remains challenging for action recognition in videos. To mitigate this
issue, this paper presents a new video architecture, termed as Temporal Difference Network …

The main challenge in the field of unsupervised machine translation (UMT) is to associate
source-target sentences in the latent space. As people who speak different languages share …

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 …

Spatial-temporal graphs have been widely used by skeleton-based action recognition
algorithms to model human action dynamics. To capture robust movement patterns from …