13 citations · 31 across the 5 of their papers we have counts for
5 papers · 1 filter
Kubric: A scalable dataset generator
Klaus Greff, Francois Belletti, Lucas Beyer +32
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and trainin…
SMURF: Self-Teaching Multi-Frame Unsupervised RAFT with Full-Image Warping
Austin Stone, Daniel Maurer, Alper Ayvaci +2
We present SMURF, a method for unsupervised learning of optical flow that improves state of the art on all benchmarks by to (over the prior best method UFlow) and eve…
What Matters in Unsupervised Optical Flow
Rico Jonschkowski, Austin Stone, Jonathan T. Barron +3
We systematically compare and analyze a set of key components in unsupervised optical flow to identify which photometric loss, occlusion handling, and smoothness regularization is…
Towards Object Detection from Motion
Rico Jonschkowski, Austin Stone
We present a novel approach to weakly supervised object detection. Instead of annotated images, our method only requires two short videos to learn to detect a new object: 1) a vide…
Teaching Compositionality to CNNs
Austin Stone, Huayan Wang, Michael Stark +3
Convolutional neural networks (CNNs) have shown great success in computer vision, approaching human-level performance when trained for specific tasks via application-specific loss…