no code implementations • ICCV 2023 • Klemen Kotar, Aaron Walsman, Roozbeh Mottaghi
ENTL's generic architecture enables sharing of the spatio-temporal sequence encoder for multiple challenging embodied tasks.
no code implementations • 2 Jun 2023 • Daniel M. Bear, Kevin Feigelis, Honglin Chen, Wanhee Lee, Rahul Venkatesh, Klemen Kotar, Alex Durango, Daniel L. K. Yamins
Leading approaches in machine vision employ different architectures for different tasks, trained on costly task-specific labeled datasets.
no code implementations • 5 Dec 2023 • Lukas Wolf, Greta Tuckute, Klemen Kotar, Eghbal Hosseini, Tamar Regev, Ethan Wilcox, Alex Warstadt
Training on multiple modalities of input can augment the capabilities of a language model.
no code implementations • 11 Dec 2023 • Rahul Venkatesh, Honglin Chen, Kevin Feigelis, Daniel M. Bear, Khaled Jedoui, Klemen Kotar, Felix Binder, Wanhee Lee, Sherry Liu, Kevin A. Smith, Judith E. Fan, Daniel L. K. Yamins
Third, the counterfactual modeling capability enables the design of counterfactual queries to extract vision structures similar to keypoints, optical flows, and segmentations, which are useful for dynamics understanding.
1 code implementation • ICCV 2021 • Klemen Kotar, Gabriel Ilharco, Ludwig Schmidt, Kiana Ehsani, Roozbeh Mottaghi
In the past few years, we have witnessed remarkable breakthroughs in self-supervised representation learning.
1 code implementation • CVPR 2022 • Klemen Kotar, Roozbeh Mottaghi
Our adaptive object detection model provides a 7. 2 point improvement in AP (and 12. 7 points in AP50) over DETR, a recent, high-performance object detector.
2 code implementations • 27 Jul 2022 • Aaron Walsman, Muru Zhang, Klemen Kotar, Karthik Desingh, Ali Farhadi, Dieter Fox
We pair this simulator with a new dataset of fan-made LEGO creations that have been uploaded to the internet in order to provide complex scenes containing over a thousand unique brick shapes.
1 code implementation • 28 Aug 2020 • Luca Weihs, Jordi Salvador, Klemen Kotar, Unnat Jain, Kuo-Hao Zeng, Roozbeh Mottaghi, Aniruddha Kembhavi
The domain of Embodied AI, in which agents learn to complete tasks through interaction with their environment from egocentric observations, has experienced substantial growth with the advent of deep reinforcement learning and increased interest from the computer vision, NLP, and robotics communities.