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Texas A&M Univeristy Machine Learning Challenge: Cross Domain Person Re-Identification
Artificial Intelligence course challenge sponsored by Walmart Inc., Texas A&M Univeristy, 2018
CVPR Workshop and Prize Challenge: Bridging the Gap between Computational Photography and Visual Recognition
UG2+ Prize Challenge, CVPR, 2019
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publications
Genome Sequence of a Proteus mirabilis Strain Isolated from the Salivary Glands of Larval Lucilia sericata
Published on Genome Announc., 2016
Recommended citation: Ye Yuan et al. (2016). "Genome Sequence of a Proteus mirabilis Strain Isolated from the Salivary Glands of Larval Lucilia sericata" Genome Announc.. e00672-16. [link]
Genome Sequence of a Providencia stuartii Strain Isolated from Lucilia sericata Salivary Glands
Published on Genome Announc., 2017
Recommended citation: Ye Yuan et al. (2017). "Genome Sequence of a Providencia stuartii Strain Isolated from Lucilia sericata Salivary Glands." Genome Announc.. e00250-17. [link]
Feasibility of Quantifying Amyloid Burden Using Volumetric MRI Data: Preliminary Findings Based on the Deep Learning 3D Convolutional Neural Network Approach
Published on Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 2018
Recommended citation: Ye Yuan et al. (2018). "Feasibility of Quantifying Amyloid Burden Using Volumetric MRI Data: Preliminary Findings Based on the Deep Learning 3D Convolutional Neural Network Approach." Alzheimer's & Dementia: The Journal of the Alzheimer's Association. Volume 14, Issue 7, P30 - P31. [link]
Quantification of Amyloid Burden from Florbetapir PET Images without Using Target and Reference Regions: Preliminary Findings Based on the Deep Learning 3D Convolutional Neural Network Approach
Published on Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 2018
Recommended citation: Ye Yuan et al. (2018). "Quantification of Amyloid Burden from Florbetapir PET Images without Using Target and Reference Regions: Preliminary Findings Based on the Deep Learning 3D Convolutional Neural Network Approach." Alzheimer's & Dementia: The Journal of the Alzheimer's Association. Volume 14, Issue 7, P31. [link]
Bridging the Gap Between Computational Photography and Visual Recognition
Published on Arxiv Preprint, 2019
In this paper, we introduced the UG2 dataset as a large-scale benchmark composed of video imagery captured under challenging conditions, and two enhancement tasks designed to test algorithmic impact on visual quality and automatic object recognition. Furthermore, we proposed a set of metrics to evaluate the joint improvement of such tasks as well as individual algorithmic advances, including a novel psychophysics-based evaluation regime for human assessment and a realistic set of quantitative measures for object recognition performance. We introduced six new algorithms for image restoration or enhancement, which were created as part of the IARPA sponsored UG2 Challenge workshop held at CVPR 2018.
Recommended citation: Rosaura G. VidalMata et al. (2019). "Bridging the Gap Between Computational Photography and Visual Recognition" Arxiv Preprint. 1901.09482. [link]
UG2+ Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments
Published on Arxiv Preprint, 2019
A summary paper on datasets, fact sheets and baseline results in UG2+ Challenge (Track 2). More materials are provided in http://www.ug2challenge.org.
Recommended citation: Ye Yuan et al. (2019). "UG2+ Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments" Arxiv Preprint. 1904.04474. [link]
ABD-Net: Attentive but Diverse Person Re-Identification
Published on ICCV, 2019
We advocate the complementary powers of attention and diversity for Re-ID, by proposing an Attentive but Diverse Network (ABD-Net). ABD-Net seamlessly integrates attention modules and diversity regularizations throughout the entire network to learn features that are representative, robust, and more discriminative
Recommended citation: Tianlong Chen et al. (2019). "ABD-Net: Attentive but Diverse Person Re-Identification." ICCV 2019. ... [link]
Calibrated Domain-Invariant Learning for Highly Generalizable Large Scale Re-Identification
Published on WACV, 2020
In seek for a highly generalizable large-scale ReID method, we present an adversarialdomain-invariant feature learning framework (ADIN) that explicitly learns to separate identity-related features from challenging variations, where for the first time “free” annotations in ReID data such as video timestamp and camera index are utilized.
Recommended citation: Ye Yuan et al. (2020). "Calibrated Domain-Invariant Learning for Highly Generalizable Large Scale Re-Identification." WACV 2020. ... [link]