1 code implementation • 17 Jul 2023 • Masoomeh Aslahishahri, Jordan Ubbens, Ian Stavness
Our work demonstrates how the attention mechanism can be adapted for the particular requirements of reference-based image super-resolution, significantly simplifying the architecture and training pipeline.
1 code implementation • ICLR 2022 • Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang
Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can often be obtained from distributions beyond the source distribution as well.
no code implementations • 17 May 2021 • Etienne David, Mario Serouart, Daniel Smith, Simon Madec, Kaaviya Velumani, Shouyang Liu, Xu Wang, Francisco Pinto Espinosa, Shahameh Shafiee, Izzat S. A. Tahir, Hisashi Tsujimoto, Shuhei Nasuda, Bangyou Zheng, Norbert Kichgessner, Helge Aasen, Andreas Hund, Pouria Sadhegi-Tehran, Koichi Nagasawa, Goro Ishikawa, Sébastien Dandrifosse, Alexis Carlier, Benoit Mercatoris, Ken Kuroki, Haozhou Wang, Masanori Ishii, Minhajul A. Badhon, Curtis Pozniak, David Shaner LeBauer, Morten Lilimo, Jesse Poland, Scott Chapman, Benoit de Solan, Frédéric Baret, Ian Stavness, Wei Guo
We now release a new version of the Global Wheat Head Detection (GWHD) dataset in 2021, which is bigger, more diverse, and less noisy than the 2020 version.
no code implementations • 13 May 2021 • Etienne David, Franklin Ogidi, Wei Guo, Frederic Baret, Ian Stavness
Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems.
6 code implementations • 14 Dec 2020 • Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton A. Earnshaw, Imran S. Haque, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, Percy Liang
Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild.
no code implementations • 23 Sep 2020 • Najeeb Khan, Ian Stavness
However, the huge size of contemporary models results in large inference costs and limits their use on resource-limited devices.
2 code implementations • 2 Sep 2020 • 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.
no code implementations • 17 Jul 2020 • Jordan Ubbens, Tewodros Ayalew, Steve Shirtliffe, Anique Josuttes, Curtis Pozniak, Ian Stavness
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 literature using state-of-the-art supervised deep learning techniques.
no code implementations • 9 Jan 2020 • Shubhra Aich, Ian Stavness, Yasuhiro Taniguchi, Masaki Yamazaki
In this paper, we explore the idea of weight sharing over multiple scales in convolutional networks.
1 code implementation • 18 Jun 2019 • Sara Mardanisamani, Farhad Maleki, Sara Hosseinzadeh Kassani, Sajith Rajapaksa, Hema Duddu, Menglu Wang, Steve Shirtliffe, Seungbum Ryu, Anique Josuttes, Ti Zhang, Sally Vail, Curtis Pozniak, Isobel Parkin, Ian Stavness, Mark Eramian
In this paper, we propose a deep convolutional neural network (DCNN) architecture for lodging classification using five spectral channel orthomosaic images from canola and wheat breeding trials.
1 code implementation • 17 Apr 2019 • Najeeb Khan, Ian Stavness
We found that sparsity of the activations is favorable for language modelling performance while image classification benefits from denser activations.
no code implementations • 28 May 2018 • Shubhra Aich, Ian Stavness
This generalization capability allows GSP to avoid both patchwise cancellation and overfitting by training on small patches and inference on full-resolution images as a whole.
1 code implementation • 1 May 2018 • 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.
no code implementations • 21 Apr 2018 • Najeeb Khan, Jawad Shah, Ian Stavness
Stochastic methods such as Dropout and Shakeout, in expectation, are equivalent to imposing a ridge and elastic-net penalty on the model parameters, respectively.
2 code implementations • 14 Mar 2018 • Shubhra Aich, Ian Stavness
Adding HR to a simple VGG front-end improves performance on all these benchmarks compared to a simple one-look baseline model and results in state-of-the-art performance for car counting.
1 code implementation • 30 Sep 2017 • Shubhra Aich, Anique Josuttes, Ilya Ovsyannikov, Keegan Strueby, Imran Ahmed, Hema Sudhakar Duddu, Curtis Pozniak, Steve Shirtliffe, Ian Stavness
In this paper, we investigate estimating emergence and biomass traits from color images and elevation maps of wheat field plots.
1 code implementation • 24 Aug 2017 • 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.
no code implementations • 13 Jun 2017 • Najeeb Khan, Ian Stavness
In this work, we investigate deep autoencoders for the prediction of muscle activation trajectories for point-to-point reaching movements.