285 citations · 429 across the 17 of their papers we have counts for
21 papers
HiTSR: A Hierarchical Transformer for Reference-based Super-Resolution
Masoomeh Aslahishahri, Jordan Ubbens, Ian Stavness
In this paper, we propose HiTSR, a hierarchical transformer model for reference-based image super-resolution, which enhances low-resolution input images by learning matching corres…
DARTS: Double Attention Reference-based Transformer for Super-resolution
Masoomeh Aslahishahri, Jordan Ubbens, Ian Stavness
We present DARTS, a transformer model for reference-based image super-resolution. DARTS learns joint representations of two image distributions to enhance the content of low-resolu…
Extending the WILDS Benchmark for Unsupervised Adaptation
Shiori Sagawa, Pang Wei Koh, Tony Lee +17
Machine learning systems deployed in the wild are often trained on a source distribution but deployed on a different target distribution. Unlabeled data can be a powerful point of…
Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods
Etienne David, Mario Serouart, Daniel Smith +32
The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4,700 RGB images acquired from various acquisition platforms…
Global Wheat Challenge 2020: Analysis of the competition design and winning models
Etienne David, Franklin Ogidi, Wei Guo +2
Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems. In plant phenotyping, data competitions…
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund +20
Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the…
Pruning Convolutional Filters using Batch Bridgeout
Najeeb Khan, Ian Stavness
State-of-the-art computer vision models are rapidly increasing in capacity, where the number of parameters far exceeds the number required to fit the training set. This results in…
Unsupervised Domain Adaptation For Plant Organ Counting
Tewodros Ayalew, Jordan Ubbens, Ian Stavness
Supervised learning is often used to count objects in images, but for counting small, densely located objects, the required image annotations are burdensome to collect. Counting pl…
AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images
Jordan Ubbens, Tewodros Ayalew, Steve Shirtliffe +3
Counting plant organs such as heads or tassels from outdoor imagery is a popular benchmark computer vision task in plant phenotyping, which has been previously investigated in the…
Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods
E. David, S. Madec, P. Sadeghi-Tehran +14
Detection of wheat heads is an important task allowing to estimate pertinent traits including head population density and head characteristics such as sanitary state, size, maturit…
Multi-Scale Weight Sharing Network for Image Recognition
Shubhra Aich, Ian Stavness, Yasuhiro Taniguchi +1
In this paper, we explore the idea of weight sharing over multiple scales in convolutional networks. Inspired by traditional computer vision approaches, we share the weights of con…
Crop Lodging Prediction from UAV-Acquired Images of Wheat and Canola using a DCNN Augmented with Handcrafted Texture Features
Sara Mardanisamani, Farhad Maleki, Sara Hosseinzadeh Kassani +12
Lodging, the permanent bending over of food crops, leads to poor plant growth and development. Consequently, lodging results in reduced crop quality, lowers crop yield, and makes h…
Sparseout: Controlling Sparsity in Deep Networks
Najeeb Khan, Ian Stavness
Dropout is commonly used to help reduce overfitting in deep neural networks. Sparsity is a potentially important property of neural networks, but is not explicitly controlled by Dr…
Semantic Binary Segmentation using Convolutional Networks without Decoders
Shubhra Aich, William van der Kamp, Ian Stavness
In this paper, we propose an efficient architecture for semantic image segmentation using the depth-to-space (D2S) operation. Our D2S model is comprised of a standard CNN encoder f…
Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images
Shubhra Aich, Ian Stavness
In this paper, we explore the problem of training one-look regression models for counting objects in datasets comprising a small number of high-resolution, variable-shaped images.…
Bridgeout: stochastic bridge regularization for deep neural networks
Najeeb Khan, Jawad Shah, Ian Stavness
A major challenge in training deep neural networks is overfitting, i.e. inferior performance on unseen test examples compared to performance on training examples. To reduce overfit…
Improving Object Counting with Heatmap Regulation
Shubhra Aich, Ian Stavness
In this paper, we propose a simple and effective way to improve one-look regression models for object counting from images. We use class activation map visualizations to illustrate…
DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning
Shubhra Aich, Anique Josuttes, Ilya Ovsyannikov +6
In this paper, we investigate estimating emergence and biomass traits from color images and elevation maps of wheat field plots. We employ a state-of-the-art deconvolutional networ…
Leaf Counting with Deep Convolutional and Deconvolutional Networks
Shubhra Aich, Ian Stavness
In this paper, we investigate the problem of counting rosette leaves from an RGB image, an important task in plant phenotyping. We propose a data-driven approach for this task gene…
Prediction of Muscle Activations for Reaching Movements using Deep Neural Networks
Najeeb Khan, Ian Stavness
The motor control problem involves determining the time-varying muscle activation trajectories required to accomplish a given movement. Muscle redundancy makes motor control a chal…
A biomechanical modeling study of the effects of the orbicularis oris muscle and jaw posture on lip shape
Ian Stavness, Mohammad Ali Nazari, Pascal Perrier +2
Purpose: The authors' general aim is to use biomechanical models of speech articulators to explore how possible variations in anatomical structure contribute to differences in arti…