Song, Yang
UNSW Sydney
6 papers on CoChatORCID 0000-0003-1283-1672
Most cited papers
- Verified
Class-Balanced Loss Based on Effective Number of Samples
Cui, Y., Jia, M., Lin, T.-Y., Song, Y., & Belongie, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Advanced Neural Network ApplicationsImbalanced Data Classification Techniques
Cited by 3,094
- Verified
Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors
Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning
Cited by 2,743
- Verified
The iNaturalist Species Classification and Detection Dataset
Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., & Belongie, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning
Cited by 1,451
- Verified
Feature-Based Image Patch Approximation for Lung Tissue Classification
Song, Y., Cai, W., Zhou, Y., & Feng, D. D. (2013) · IEEE Transactions on Medical Imaging
Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques
Cited by 177
- Verified
Large Margin Local Estimate With Applications to Medical Image Classification
Song, Y., Cai, W., Huang, H., Zhou, Y., Feng, D. D., Wang, Y., Fulham, M. J., & Chen, M. (2015) · IEEE Transactions on Medical Imaging
Image Retrieval and Classification TechniquesRadiomics and Machine Learning in Medical Imaging
Cited by 87
- Verified
Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation
Chen, Y., Zhang, C., Song, Y., Makris, N., Rathi, Y., Cai, W., Zhang, F., & O’Donnell, L. J. (2021) · Lecture Notes in Computer Science
Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity Studies
Cited by 28
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