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pro vyhledávání: '"Just, John"'
The rapid proliferation of digital content and the ever-growing need for precise object recognition and segmentation have driven the advancement of cutting-edge techniques in the field of object classification and segmentation. This paper introduces
Externí odkaz:
http://arxiv.org/abs/2403.07231
Training image-based object detectors presents formidable challenges, as it entails not only the complexities of object detection but also the added intricacies of precisely localizing objects within potentially diverse and noisy environments. Howeve
Externí odkaz:
http://arxiv.org/abs/2402.13465
In precision agriculture, detecting productive crop fields is an essential practice that allows the farmer to evaluate operating performance separately and compare different seed varieties, pesticides, and fertilizers. However, manually identifying p
Externí odkaz:
http://arxiv.org/abs/2305.11990
Autor:
Herrera-Gerena, Jansel, Sundareswaran, Ramakrishnan, Just, John, Darr, Matthew, Jannesari, Ali
Learning effective visual representations without human supervision is a long-standing problem in computer vision. Recent advances in self-supervised learning algorithms have utilized contrastive learning, with methods such as SimCLR, which applies a
Externí odkaz:
http://arxiv.org/abs/2112.00847
Unsupervised disentangled representation learning is a long-standing problem in computer vision. This work proposes a novel framework for performing image clustering from deep embeddings by combining instance-level contrastive learning with a deep em
Externí odkaz:
http://arxiv.org/abs/2109.12714
Yield monitors on harvesters are a key component of precision agriculture. Mass flow estimation is the critical factor to measure, and having this allows for field productivity analysis, adjustments to machine efficiency, and cost minimization by ens
Externí odkaz:
http://arxiv.org/abs/2005.00907
Mass flow estimation is of great importance to several industries, and it can be quite challenging to obtain accurate estimates due to limitation in expense or general infeasibility. In the context of agricultural applications, yield monitoring is a
Externí odkaz:
http://arxiv.org/abs/2003.03192
Autor:
Just, John
An approach to utilize recent advances in deep generative models for anomaly detection in a granular (continuous) sense on a real-world image dataset with quality issues is detailed using recent normalizing flow models, with implications in many othe
Externí odkaz:
http://arxiv.org/abs/2001.04297
Autor:
Just, John, Ghosal, Sambuddha
Advances in deep generative and density models have shown impressive capacity to model complex probability density functions in lower-dimensional space. Also, applying such models to high-dimensional image data to model the PDF has shown poor general
Externí odkaz:
http://arxiv.org/abs/1911.04699
Supervised learning is the workhorse for regression and classification tasks, but the standard approach presumes ground truth for every measurement. In real world applications, limitations due to expense or general in-feasibility due to the specific
Externí odkaz:
http://arxiv.org/abs/1908.04387