1 code implementation • CVPR 2023 • Mohamed El Banani, Karan Desai, Justin Johnson
Our approach diverges from image-based contrastive learning by sampling view pairs using language similarity instead of hand-crafted augmentations or learned clusters.
1 code implementation • 6 Dec 2022 • Mohamed El Banani, Ignacio Rocco, David Novotny, Andrea Vedaldi, Natalia Neverova, Justin Johnson, Benjamin Graham
To address this, we propose a self-supervised approach for correspondence estimation that learns from multiview consistency in short RGB-D video sequences.
no code implementations • ICCV 2021 • Mohamed El Banani, Justin Johnson
Our approach combines classic ideas from point cloud registration with more recent representation learning approaches.
1 code implementation • CVPR 2021 • Mohamed El Banani, Luya Gao, Justin Johnson
Aligning partial views of a scene into a single whole is essential to understanding one's environment and is a key component of numerous robotics tasks such as SLAM and SfM.
1 code implementation • CVPR 2020 • Mohamed El Banani, Jason J. Corso, David F. Fouhey
Our key insight is that although we do not have an explicit 3D model or a predefined canonical pose, we can still learn to estimate the object's shape in the viewer's frame and then use an image to provide our reference model or canonical pose.
1 code implementation • 5 Feb 2018 • Mohamed El Banani, Jason J. Corso
We address this question by formulating it as an Adviser Problem: can we learn a mapping from the input to a specific question to ask the human to maximize the expected positive impact to the overall task?