4 citations · 4 across the 1 of their papers we have counts for
9 papers
Depth from Camera Motion and Object Detection
Brent A. Griffin, Jason J. Corso
This paper addresses the problem of learning to estimate the depth of detected objects given some measurement of camera motion (e.g., from robot kinematics or vehicle odometry). We…
Learning Object Depth from Camera Motion and Video Object Segmentation
Brent A. Griffin, Jason J. Corso
Video object segmentation, i.e., the separation of a target object from background in video, has made significant progress on real and challenging videos in recent years. To levera…
Robot-Supervised Learning for Object Segmentation
Victoria Florence, Jason J. Corso, Brent Griffin
To be effective in unstructured and changing environments, robots must learn to recognize new objects. Deep learning has enabled rapid progress for object detection and segmentatio…
BubbleNets: Learning to Select the Guidance Frame in Video Object Segmentation by Deep Sorting Frames
Brent A. Griffin, Jason J. Corso
Semi-supervised video object segmentation has made significant progress on real and challenging videos in recent years. The current paradigm for segmentation methods and benchmark…
Video Object Segmentation-based Visual Servo Control and Object Depth Estimation on a Mobile Robot
Brent A. Griffin, Victoria Florence, Jason J. Corso
To be useful in everyday environments, robots must be able to identify and locate real-world objects. In recent years, video object segmentation has made significant progress on de…
Kinematically-Informed Interactive Perception: Robot-Generated 3D Models for Classification
Abhishek Venkataraman, Brent Griffin, Jason J. Corso
To be useful in everyday environments, robots must be able to observe and learn about objects. Recent datasets enable progress for classifying data into known object categories; ho…