Zobrazeno 1 - 10
of 93
pro vyhledávání: '"Kar, Amlan"'
Autor:
Zhang, Dongsu, Williams, Francis, Gojcic, Zan, Kreis, Karsten, Fidler, Sanja, Kim, Young Min, Kar, Amlan
We aim to generate fine-grained 3D geometry from large-scale sparse LiDAR scans, abundantly captured by autonomous vehicles (AV). Contrary to prior work on AV scene completion, we aim to extrapolate fine geometry from unlabeled and beyond spatial lim
Externí odkaz:
http://arxiv.org/abs/2406.08292
Autor:
Li, Daiqing, Ling, Huan, Kar, Amlan, Acuna, David, Kim, Seung Wook, Kreis, Karsten, Torralba, Antonio, Fidler, Sanja
In this work, we introduce a self-supervised feature representation learning framework DreamTeacher that utilizes generative networks for pre-training downstream image backbones. We propose to distill knowledge from a trained generative model into st
Externí odkaz:
http://arxiv.org/abs/2307.07487
Autor:
Darkhalil, Ahmad, Shan, Dandan, Zhu, Bin, Ma, Jian, Kar, Amlan, Higgins, Richard, Fidler, Sanja, Fouhey, David, Damen, Dima
We introduce VISOR, a new dataset of pixel annotations and a benchmark suite for segmenting hands and active objects in egocentric video. VISOR annotates videos from EPIC-KITCHENS, which comes with a new set of challenges not encountered in current v
Externí odkaz:
http://arxiv.org/abs/2209.13064
Autor:
Resnick, Cinjon, Litany, Or, Kar, Amlan, Kreis, Karsten, Lucas, James, Cho, Kyunghyun, Fidler, Sanja
Modern computer vision applications rely on learning-based perception modules parameterized with neural networks for tasks like object detection. These modules frequently have low expected error overall but high error on atypical groups of data due t
Externí odkaz:
http://arxiv.org/abs/2202.03651
Autor:
Paschalidou, Despoina, Kar, Amlan, Shugrina, Maria, Kreis, Karsten, Geiger, Andreas, Fidler, Sanja
The ability to synthesize realistic and diverse indoor furniture layouts automatically or based on partial input, unlocks many applications, from better interactive 3D tools to data synthesis for training and simulation. In this paper, we present ATI
Externí odkaz:
http://arxiv.org/abs/2110.03675
Data is the engine of modern computer vision, which necessitates collecting large-scale datasets. This is expensive, and guaranteeing the quality of the labels is a major challenge. In this paper, we investigate efficient annotation strategies for co
Externí odkaz:
http://arxiv.org/abs/2104.12690
Labelling data is expensive and time consuming especially for domains such as medical imaging that contain volumetric imaging data and require expert knowledge. Exploiting a larger pool of labeled data available across multiple centers, such as in fe
Externí odkaz:
http://arxiv.org/abs/2009.00668
Inferring detailed 3D geometry of the scene is crucial for robotics applications, simulation, and 3D content creation. However, such information is hard to obtain, and thus very few datasets support it. In this paper, we propose an interactive framew
Externí odkaz:
http://arxiv.org/abs/2008.10719
Procedural models are being widely used to synthesize scenes for graphics, gaming, and to create (labeled) synthetic datasets for ML. In order to produce realistic and diverse scenes, a number of parameters governing the procedural models have to be
Externí odkaz:
http://arxiv.org/abs/2008.09092