9 citations · 16 across the 8 of their papers we have counts for
8 papers
Productive Crop Field Detection: A New Dataset and Deep Learning Benchmark Results
Eduardo Nascimento, John Just, Jurandy Almeida +1
In precision agriculture, detecting productive crop fields is an essential practice that allows the farmer to evaluate operating performance separately and compare different seed v…
CLAWS: Contrastive Learning with hard Attention and Weak Supervision
Jansel Herrera-Gerena, Ramakrishnan Sundareswaran, John Just +2
Learning effective visual representations without human supervision is a long-standing problem in computer vision. Recent advances in self-supervised learning algorithms have utili…
Cluster Analysis with Deep Embeddings and Contrastive Learning
Ramakrishnan Sundareswaran, Jansel Herrera-Gerena, John Just +1
Unsupervised disentangled representation learning is a long-standing problem in computer vision. This work proposes a novel framework for performing image clustering from deep embe…
Volumetric based mass flow estimation on sugarcane harvesters
Muhammad K. A. Hamdan, Diane T. Rover, Matthew J. Darr +1
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 ana…
Generalizable semi-supervised learning method to estimate mass from sparsely annotated images
Muhammad K. A. Hamdan, Diane T. Rover, Matthew J. Darr +1
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 infeasibilit…
Granular Learning with Deep Generative Models using Highly Contaminated Data
John Just
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 detaile…