Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Uzkent, Burak"'
Grounding-based vision and language models have been successfully applied to low-level vision tasks, aiming to precisely locate objects referred in captions. The effectiveness of grounding representation learning heavily relies on the scale of the tr
Externí odkaz:
http://arxiv.org/abs/2311.02536
The recently proposed Vision transformers (ViTs) have shown very impressive empirical performance in various computer vision tasks, and they are viewed as an important type of foundation model. However, ViTs are typically constructed with large-scale
Externí odkaz:
http://arxiv.org/abs/2301.05345
Data augmentation is often used to enlarge datasets with synthetic samples generated in accordance with the underlying data distribution. To enable a wider range of augmentations, we explore negative data augmentation strategies (NDA)that intentional
Externí odkaz:
http://arxiv.org/abs/2102.05113
Autor:
Chakraborty, Shuvam, Uzkent, Burak, Ayush, Kumar, Tanmay, Kumar, Sheehan, Evan, Ermon, Stefano
Almost all the state-of-the-art neural networks for computer vision tasks are trained by (1) pre-training on a large-scale dataset and (2) finetuning on the target dataset. This strategy helps reduce dependence on the target dataset and improves conv
Externí odkaz:
http://arxiv.org/abs/2011.10231
Autor:
Ayush, Kumar, Uzkent, Burak, Meng, Chenlin, Tanmay, Kumar, Burke, Marshall, Lobell, David, Ermon, Stefano
Contrastive learning methods have significantly narrowed the gap between supervised and unsupervised learning on computer vision tasks. In this paper, we explore their application to geo-located datasets, e.g. remote sensing, where unlabeled data is
Externí odkaz:
http://arxiv.org/abs/2011.09980
Autor:
Lee, Jihyeon, Grosz, Dylan, Uzkent, Burak, Zeng, Sicheng, Burke, Marshall, Lobell, David, Ermon, Stefano
Major decisions from governments and other large organizations rely on measurements of the populace's well-being, but making such measurements at a broad scale is expensive and thus infrequent in much of the developing world. We propose an inexpensiv
Externí odkaz:
http://arxiv.org/abs/2006.08661
The combination of high-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks, including poverty prediction, infrastructure measurement, and forest monitoring. However, the accuracy afforded by high
Externí odkaz:
http://arxiv.org/abs/2006.04224
Farm parcel delineation provides cadastral data that is important in developing and managing climate change policies. Specifically, farm parcel delineation informs applications in downstream governmental policies of land allocation, irrigation, ferti
Externí odkaz:
http://arxiv.org/abs/2004.05471
Autor:
Uzkent, Burak, Ermon, Stefano
While high resolution images contain semantically more useful information than their lower resolution counterparts, processing them is computationally more expensive, and in some applications, e.g. remote sensing, they can be much more expensive to a
Externí odkaz:
http://arxiv.org/abs/2003.00425
Accurate local-level poverty measurement is an essential task for governments and humanitarian organizations to track the progress towards improving livelihoods and distribute scarce resources. Recent computer vision advances in using satellite image
Externí odkaz:
http://arxiv.org/abs/2002.01612