no code implementations • 9 Apr 2024 • Kaylee Burns, Ajinkya Jain, Keegan Go, Fei Xia, Michael Stark, Stefan Schaal, Karol Hausman
Large Language Models (LLMs) have been successful at generating robot policy code, but so far these results have been limited to high-level tasks that do not require precise movement.
no code implementations • CVPR 2017 • Austin Stone, Huayan Wang, Michael Stark, Yi Liu, D. Scott Phoenix, Dileep George
Convolutional neural networks (CNNs) have shown great success in computer vision, approaching human-level performance when trained for specific tasks via application-specific loss functions.
no code implementations • CVPR 2015 • Justin Johnson, Ranjay Krishna, Michael Stark, Li-Jia Li, David Shamma, Michael Bernstein, Li Fei-Fei
We introduce a novel dataset of 5, 000 human-generated scene graphs grounded to images and use this dataset to evaluate our method for image retrieval.
no code implementations • CVPR 2015 • Christopher Bongsoo Choy, Michael Stark, Sam Corbett-Davies, Silvio Savarese
We propose an efficient method for synthesizing templates from 3D models that runs on the fly -- that is, it quickly produces detectors for an arbitrary viewpoint of a 3D model without expensive dataset-dependent training or template storage.
no code implementations • 17 Mar 2015 • Bojan Pepik, Michael Stark, Peter Gehler, Tobias Ritschel, Bernt Schiele
Object class detection has been a synonym for 2D bounding box localization for the longest time, fueled by the success of powerful statistical learning techniques, combined with robust image representations.
no code implementations • 18 Nov 2014 • M. Zeeshan Zia, Michael Stark, Konrad Schindler
An object class - in our case cars - is modeled as a deformable 3D wireframe, which enables fine-grained modeling at the level of individual vertices and faces.
no code implementations • CVPR 2014 • Muhammad Zeeshan Zia, Michael Stark, Konrad Schindler
Current systems for scene understanding typically represent objects as 2D or 3D bounding boxes.
no code implementations • 20 Dec 2013 • Bojan Pepik, Michael Stark, Peter Gehler, Bernt Schiele
While the majority of today's object class models provide only 2D bounding boxes, far richer output hypotheses are desirable including viewpoint, fine-grained category, and 3D geometry estimate.
no code implementations • CVPR 2013 • M. Zeeshan Zia, Michael Stark, Konrad Schindler
In this paper, we tackle the challenge of modeling occlusion in the context of a 3D geometric object class model that is capable of fine-grained, part-level 3D object reconstruction.
no code implementations • CVPR 2013 • Bojan Pepikj, Michael Stark, Peter Gehler, Bernt Schiele
Despite the success of recent object class recognition systems, the long-standing problem of partial occlusion remains a major challenge, and a principled solution is yet to be found.