10 citations · 29 across the 11 of their papers we have counts for
7 papers · 1 filter
Identifying Terms and Conditions Important to Consumers using Crowdsourcing
Xingyu Liu, Annabel Sun, Jason I. Hong
Terms and conditions (T&Cs) are pervasive on the web and often contain important information for consumers, but are rarely read. Previous research has explored methods to surface a…
V-MAO: Generative Modeling for Multi-Arm Manipulation of Articulated Objects
Xingyu Liu, Kris M. Kitani
Manipulating articulated objects requires multiple robot arms in general. It is challenging to enable multiple robot arms to collaboratively complete manipulation tasks on articula…
Sequential Voting with Relational Box Fields for Active Object Detection
Qichen Fu, Xingyu Liu, Kris M. Kitani
A key component of understanding hand-object interactions is the ability to identify the active object -- the object that is being manipulated by the human hand. In order to accura…
Ego4D: Around the World in 3,000 Hours of Egocentric Video
Kristen Grauman, Andrew Westbury, Eugene Byrne +82
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household, outd…
KDFNet: Learning Keypoint Distance Field for 6D Object Pose Estimation
Xingyu Liu, Shun Iwase, Kris M. Kitani
We present KDFNet, a novel method for 6D object pose estimation from RGB images. To handle occlusion, many recent works have proposed to localize 2D keypoints through pixel-wise vo…
StereOBJ-1M: Large-scale Stereo Image Dataset for 6D Object Pose Estimation
Xingyu Liu, Shun Iwase, Kris M. Kitani
We present a large-scale stereo RGB image object pose estimation dataset named the dataset. The dataset is designed to address challenging cases such as obje…
RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering
Shun Iwase, Xingyu Liu, Rawal Khirodkar +2
We present RePOSE, a fast iterative refinement method for 6D object pose estimation. Prior methods perform refinement by feeding zoomed-in input and rendered RGB images into a CNN…