285 citations · 429 across the 17 of their papers we have counts for
6 papers · 1 filter
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…