With the rise and development of deep learning, computer vision has been tremendously
transformed and reshaped. As an important research area in computer vision, scene text …

Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

We demonstrate that it is possible to perform face-related computer vision in the wild using
synthetic data alone. The community has long enjoyed the benefits of synthesizing training …

We present a practical and robust deep learning solution for capturing and rendering novel
views of complex real world scenes for virtual exploration. Previous approaches either …

Human pose is typically represented by a coordinate vector of body joints or their heatmap
embeddings. While easy for data processing, unrealistic pose estimates are admitted due to …

Building home assistant robots has long been a goal for vision and robotics researchers. To
achieve this task, a simulated environment with physically realistic simulation, sufficient …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

In federated learning (FL), model performance typically suffers from client drift induced by
data heterogeneity, and mainstream works focus on correcting client drift. We propose a …

In this paper, we study the problem of semi-supervised image recognition, which is to learn
classifiers using both labeled and unlabeled images. We present Deep Co-Training, a deep …

Despite excellent performance on stationary test sets, deep neural networks (DNNs) can fail
to generalize to out-of-distribution (OoD) inputs, including natural, non-adversarial ones …