196 citations · 569 across the 53 of their papers we have counts for
9 papers · 1 filter
SPIN: A High Speed, High Resolution Vision Dataset for Tracking and Action Recognition in Ping Pong
Steven Schwarcz, Peng Xu, David D'Ambrosio +4
We introduce a new high resolution, high frame rate stereo video dataset, which we call SPIN, for tracking and action recognition in the game of ping pong. The corpus consists of p…
KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects
Xingyu Liu, Rico Jonschkowski, Anelia Angelova +1
Estimating the 3D pose of desktop objects is crucial for applications such as robotic manipulation. Many existing approaches to this problem require a depth map of the object for b…
Tiny Video Networks
AJ Piergiovanni, Anelia Angelova, Michael S. Ryoo
Video understanding is a challenging problem with great impact on the abilities of autonomous agents working in the real-world. Yet, solutions so far have been computationally inte…
Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics
Vincent Casser, Soeren Pirk, Reza Mahjourian +1
We present an approach which takes advantage of both structure and semantics for unsupervised monocular learning of depth and ego-motion. More specifically, we model the motion of…
Evolving Losses for Unlabeled Video Representation Learning
AJ Piergiovanni, Anelia Angelova, Michael S. Ryoo
We present a new method to learn video representations from unlabeled data. Given large-scale unlabeled video data, the objective is to benefit from such data by learning a generic…
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures
Michael S. Ryoo, AJ Piergiovanni, Mingxing Tan +1
Learning to represent videos is a very challenging task both algorithmically and computationally. Standard video CNN architectures have been designed by directly extending architec…
Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras
Ariel Gordon, Hanhan Li, Rico Jonschkowski +1
We present a novel method for simultaneous learning of depth, egomotion, object motion, and camera intrinsics from monocular videos, using only consistency across neighboring video…
ShapeMask: Learning to Segment Novel Objects by Refining Shape Priors
Weicheng Kuo, Anelia Angelova, Jitendra Malik +1
Instance segmentation aims to detect and segment individual objects in a scene. Most existing methods rely on precise mask annotations of every category. However, it is difficult a…
Differentiable Grammars for Videos
AJ Piergiovanni, Anelia Angelova, Michael S. Ryoo
This paper proposes a novel algorithm which learns a formal regular grammar from real-world continuous data, such as videos. Learning latent terminals, non-terminals, and productio…