30 citations · 62 across the 11 of their papers we have counts for
10 papers · 1 filter
The Label Imitation Game: Turing Test Network for Zero-Shot Pseudo-Label Pruning
Brent A. Griffin, Jason J. Corso
Foundation model pseudo-labeling - labeling data strictly via zero-shot inference - enables massive scale, but performance is undermined by hallucinations that evade standard thres…
Auto-Labeling Data for Object Detection
Brent A. Griffin, Manushree Gangwar, Jacob Sela +1
Great labels make great models. However, traditional labeling approaches for tasks like object detection have substantial costs at scale. Furthermore, alternatives to fully-supervi…
Zero-Shot Coreset Selection via Iterative Subspace Sampling
Brent A. Griffin, Jacob Marks, Jason J. Corso
Deep learning increasingly relies on massive data with substantial storage, annotation, and training costs. To reduce costs, coreset selection finds a representative subset of data…
Mobile Robot Manipulation using Pure Object Detection
Brent Griffin
This paper addresses the problem of mobile robot manipulation using object detection. Our approach uses detection and control as complimentary functions that learn from real-world…
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…
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…
Tukey-Inspired Video Object Segmentation
Brent A. Griffin, Jason J. Corso
We investigate the problem of strictly unsupervised video object segmentation, i.e., the separation of a primary object from background in video without a user-provided object mask…
Learning Kinematic Descriptions using SPARE: Simulated and Physical ARticulated Extendable dataset
Abhishek Venkataraman, Brent Griffin, Jason J. Corso
Next generation robots will need to understand intricate and articulated objects as they cooperate in human environments. To do so, these robots will need to move beyond their curr…
Video Object Segmentation using Supervoxel-Based Gerrymandering
Brent A. Griffin, Jason J. Corso
Pixels operate locally. Superpixels have some potential to collect information across many pixels; supervoxels have more potential by implicitly operating across time. In this pape…