John Just
YOU?
Author Swipe
View article: Learn and Search: An Elegant Technique for Object Lookup using Contrastive Learning
Learn and Search: An Elegant Technique for Object Lookup using Contrastive Learning Open
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 …
View article: Unsupervised learning based object detection using Contrastive Learning
Unsupervised learning based object detection using Contrastive Learning Open
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 environme…
View article: Productive Crop Field Detection: A New Dataset and Deep Learning Benchmark Results
Productive Crop Field Detection: A New Dataset and Deep Learning Benchmark Results Open
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 id…
View article: CLAWS: Contrastive Learning with hard Attention and Weak Supervision
CLAWS: Contrastive Learning with hard Attention and Weak Supervision Open
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, whic…
View article: Cluster Analysis with Deep Embeddings and Contrastive Learning
Cluster Analysis with Deep Embeddings and Contrastive Learning Open
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 wit…
View article: Volumetric based mass flow estimation on sugarcane harvesters
Volumetric based mass flow estimation on sugarcane harvesters Open
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 minimiza…
View article: Generalizable semi-supervised learning method to estimate mass from\n sparsely annotated images
Generalizable semi-supervised learning method to estimate mass from\n sparsely annotated images Open
Mass flow estimation is of great importance to several industries, and it can\nbe quite challenging to obtain accurate estimates due to limitation in expense\nor general infeasibility. In the context of agricultural applications, yield\nmo…
View article: Granular Learning with Deep Generative Models using Highly Contaminated\n Data
Granular Learning with Deep Generative Models using Highly Contaminated\n Data Open
An approach to utilize recent advances in deep generative models for anomaly\ndetection in a granular (continuous) sense on a real-world image dataset with\nquality issues is detailed using recent normalizing flow models, with\nimplication…
View article: Granular Learning with Deep Generative Models using Highly Contaminated Data
Granular Learning with Deep Generative Models using Highly Contaminated Data Open
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 i…
View article: Mass Estimation from Images using Deep Neural Network and Sparse Ground Truth
Mass Estimation from Images using Deep Neural Network and Sparse Ground Truth Open
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 th…
View article: Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data
Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data Open
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 p…
View article: Mass Estimation from Images using Deep Neural Network and Sparse Ground\n Truth
Mass Estimation from Images using Deep Neural Network and Sparse Ground\n Truth Open
Supervised learning is the workhorse for regression and classification tasks,\nbut the standard approach presumes ground truth for every measurement. In real\nworld applications, limitations due to expense or general in-feasibility due to\…