Artikel mit Open-Access-Mandaten - Roozbeh MottaghiWeitere Informationen
Verfügbar: 20
OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge
K Marino, M Rastegari, A Farhadi, R Mottaghi
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
Mandate: US National Science Foundation, US Department of Defense
ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks
M Shridhar, J Thomason, D Gordon, Y Bisk, W Han, R Mottaghi, ...
arXiv preprint arXiv:1912.01734, 2019
Mandate: US National Science Foundation, US Department of Defense
ObjectNet3D: A Large Scale Database for 3D Object Recognition
Y Xiang, W Kim, W Chen, J Ji, C Choy, H Su, R Mottaghi, L Guibas, ...
European Conference on Computer Vision, 160-176, 2016
Mandate: US National Science Foundation
Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning
M Wortsman, K Ehsani, M Rastegari, A Farhadi, R Mottaghi
arXiv preprint arXiv:1812.00971, 2018
Mandate: US National Science Foundation
RoboTHOR: An Open Simulation-to-Real Embodied AI Platform
M Deitke, W Han, A Herrasti, A Kembhavi, E Kolve, R Mottaghi, J Salvador, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
Mandate: US National Science Foundation, US Department of Defense
SeGAN: Segmenting and Generating the Invisible
K Ehsani, R Mottaghi, A Farhadi
arXiv preprint arXiv:1703.10239, 2017
Mandate: US National Science Foundation, US Department of Defense
Visual Semantic Planning using Deep Successor Representations
Y Zhu, D Gordon, E Kolve, D Fox, L Fei-Fei, A Gupta, R Mottaghi, ...
arXiv preprint arXiv:1705.08080, 2017
Mandate: US National Science Foundation, US Department of Defense
Visualcomet: Reasoning about the dynamic context of a still image
JS Park, C Bhagavatula, R Mottaghi, A Farhadi, Y Choi
European Conference on Computer Vision, 508-524, 2020
Mandate: US National Science Foundation, US Department of Defense
" What happens if..." Learning to Predict the Effect of Forces in Images
R Mottaghi, M Rastegari, A Gupta, A Farhadi
arXiv preprint arXiv:1603.05600, 2016
Mandate: US National Science Foundation
Who Let The Dogs Out? Modeling Dog Behavior From Visual Data
K Ehsani, H Bagherinezhad, J Redmon, R Mottaghi, A Farhadi
arXiv preprint arXiv:1803.10827, 2018
Mandate: US National Science Foundation, US Department of Defense
See the Glass Half Full: Reasoning about Liquid Containers, their Volume and Content
R Mottaghi, C Schenck, D Fox, A Farhadi
arXiv preprint arXiv:1701.02718, 2017
Mandate: US National Science Foundation, US Department of Defense
Continuous Scene Representations for Embodied AI
SY Gadre, K Ehsani, S Song, R Mottaghi
arXiv preprint arXiv:2203.17251, 2022
Mandate: US National Science Foundation
RobustNav: Towards Benchmarking Robustness in Embodied Navigation
P Chattopadhyay, J Hoffman, R Mottaghi, A Kembhavi
arXiv preprint arXiv:2106.04531, 2021
Mandate: US National Aeronautics and Space Administration
Cora: Benchmarks, baselines, and metrics as a platform for continual reinforcement learning agents
S Powers, E Xing, E Kolve, R Mottaghi, A Gupta
Conference on Lifelong Learning Agents, 705-743, 2022
Mandate: US Department of Defense
Pushing it out of the Way: Interactive Visual Navigation
KH Zeng, L Weihs, A Farhadi, R Mottaghi
arXiv preprint arXiv:2104.14040, 2021
Mandate: US National Science Foundation, US Department of Defense
Human-Machine CRFs for Identifying Bottlenecks in Scene Understanding
R Mottaghi, S Fidler, A Yuille, R Urtasun, D Parikh
Pattern Analysis and Machine Intelligence, IEEE Transactions on 38 (1), 74-87, 2016
Mandate: US National Science Foundation
Navigating to Objects Specified by Images
J Krantz, T Gervet, K Yadav, A Wang, C Paxton, R Mottaghi, D Batra, ...
arXiv preprint arXiv:2304.01192, 2023
Mandate: US Department of Defense
Visual Reaction: Learning to Play Catch with Your Drone
KH Zeng, R Mottaghi, L Weihs, A Farhadi
arXiv preprint arXiv:1912.02155, 2019
Mandate: US National Science Foundation, US Department of Defense
Neural Priming for Sample-Efficient Adaptation
M Wallingford, V Ramanujan, A Fang, A Kusupati, R Mottaghi, ...
arXiv preprint arXiv:2306.10191, 2023
Mandate: US National Science Foundation, US Department of Defense
Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at 100k Steps-Per-Second
VP Berges, A Szot, DS Chaplot, A Gokaslan, R Mottaghi, D Batra, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
Mandate: US National Science Foundation, US Department of Defense
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