Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

Denoising diffusion probabilistic models are a promising new class of generative models
that mark a milestone in high-quality image generation. This paper showcases their ability to …

We present an approach to modeling an image-space prior on scene motion. Our prior is
learned from a collection of motion trajectories extracted from real video sequences …

Denoising diffusion models are a powerful type of generative models used to capture
complex distributions of real-world signals. However, their applicability is limited to …

Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial
prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel …

Object-centric representations are a promising path toward more systematic generalization
by providing flexible abstractions upon which compositional world models can be built …

Abstract Machine learning, particularly deep learning has boosted medical image analysis
over the past years. Training a good model based on deep learning requires large amount …

Over the last decade, supervised deep learning on manually annotated big data has been
progressing significantly on computer vision tasks. But, the application of deep learning in …

Predicting and anticipating future outcomes or reasoning about missing information in a
sequence are critical skills for agents to be able to make intelligent decisions. This requires …

From an image of a person in action, we can easily guess the 3D motion of the person in the
immediate past and future. This is because we have a mental model of 3D human dynamics …