Jamal, Muhammad Abdullah, Brown, Matthew, Yang, Ming-Hsuan, Wang, Liqiang, and Gong, Boqing. Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition From a Domain Adaptation Perspective. Retrieved from https://par.nsf.gov/biblio/10204106. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Web. doi:10.1109/CVPR42600.2020.00763.

Jamal, Muhammad Abdullah, Brown, Matthew, Yang, Ming-Hsuan, Wang, Liqiang, & Gong, Boqing. Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition From a Domain Adaptation Perspective. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (). Retrieved from https://par.nsf.gov/biblio/10204106. https://doi.org/10.1109/CVPR42600.2020.00763

Jamal, Muhammad Abdullah, Brown, Matthew, Yang, Ming-Hsuan, Wang, Liqiang, and Gong, Boqing. "Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition From a Domain Adaptation Perspective". 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (). Country unknown/Code not available. https://doi.org/10.1109/CVPR42600.2020.00763. https://par.nsf.gov/biblio/10204106.

@article{osti_10204106,
place = {Country unknown/Code not available}, title = {Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition From a Domain Adaptation Perspective}, url = {https://par.nsf.gov/biblio/10204106}, DOI = {10.1109/CVPR42600.2020.00763}, abstractNote = {}, journal = {2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, author = {Jamal, Muhammad Abdullah and Brown, Matthew and Yang, Ming-Hsuan and Wang, Liqiang and Gong, Boqing}, }