In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …

Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

Human motion modeling is important for many modern graphics applications, which typically
require professional skills. In order to remove the skill barriers for laymen, recent motion …

Despite the remarkable progress in deep generative models, synthesizing high-resolution
and temporally coherent videos still remains a challenge due to their high-dimensionality …

Light-weight convolutional neural networks (CNNs) are specially designed for applications
on mobile devices with faster inference speed. The convolutional operation can only capture …

Large-scale pretrained transformers have created milestones in text (GPT-3) and text-to-
image (DALL-E and CogView) generation. Its application to video generation is still facing …

Contrastive language-image pretraining has shown great success in learning visual-textual
joint representation from web-scale data, demonstrating remarkable “zero-shot” …

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 …

Video understanding requires reasoning at multiple spatiotemporal resolutions--from short
fine-grained motions to events taking place over longer durations. Although transformer …

Visual data such as images and videos are typically modeled as discretizations of inherently
continuous, multidimensional signals. Existing continuous-signal models attempt to exploit …