no code implementations • 7 Dec 2023 • Ethan Weber, Aleksander Hołyński, Varun Jampani, Saurabh Saxena, Noah Snavely, Abhishek Kar, Angjoo Kanazawa
In contrast to related works, we focus on completing scenes rather than deleting foreground objects, and our approach does not require tight 2D object masks or text.
1 code implementation • ICCV 2023 • Frederik Warburg, Ethan Weber, Matthew Tancik, Aleksander Holynski, Angjoo Kanazawa
Casually captured Neural Radiance Fields (NeRFs) suffer from artifacts such as floaters or flawed geometry when rendered outside the camera trajectory.
2 code implementations • 8 Feb 2023 • Matthew Tancik, Ethan Weber, Evonne Ng, RuiLong Li, Brent Yi, Justin Kerr, Terrance Wang, Alexander Kristoffersen, Jake Austin, Kamyar Salahi, Abhik Ahuja, David McAllister, Angjoo Kanazawa
Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging applications in computer vision, graphics, robotics, and more.
no code implementations • 6 Sep 2022 • Vongani H. Maluleke, Neerja Thakkar, Tim Brooks, Ethan Weber, Trevor Darrell, Alexei A. Efros, Angjoo Kanazawa, Devin Guillory
In this work, we study how the performance and evaluation of generative image models are impacted by the racial composition of their training datasets.
no code implementations • 28 Jul 2022 • Georgios Pavlakos, Ethan Weber, Matthew Tancik, Angjoo Kanazawa
TV shows depict a wide variety of human behaviors and have been studied extensively for their potential to be a rich source of data for many applications.
1 code implementation • 11 Jan 2022 • Ethan Weber, Dim P. Papadopoulos, Agata Lapedriza, Ferda Ofli, Muhammad Imran, Antonio Torralba
In this work, we present the Incidents1M Dataset, a large-scale multi-label dataset which contains 977, 088 images, with 43 incident and 49 place categories.
no code implementations • ICCV 2021 • Dim P. Papadopoulos, Ethan Weber, Antonio Torralba
Through a large-scale experiment to populate 1M unlabeled images with object segmentation masks for 80 object classes, we show that (1) we obtain 1M object segmentation masks with an total annotation time of only 290 hours; (2) we reduce annotation time by 76x compared to manual annotation; (3) the segmentation quality of our masks is on par with those from manually annotated datasets.
1 code implementation • ECCV 2020 • Ethan Weber, Nuria Marzo, Dim P. Papadopoulos, Aritro Biswas, Agata Lapedriza, Ferda Ofli, Muhammad Imran, Antonio Torralba
While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes.
1 code implementation • 12 Apr 2020 • Ethan Weber, Hassan Kané
Automatic change detection and disaster damage assessment are currently procedures requiring a huge amount of labor and manual work by satellite imagery analysts.
Ranked #4 on 2D Semantic Segmentation on xBD