1 code implementation • 21 May 2024 • Andres Hernandez, Zhongqi Miao, Luisa Vargas, Rahul Dodhia, Juan Lavista
The Opossum model achieves 98% accuracy, and the Amazon model has 92% recognition accuracy for 36 animals in 90% of the data.
no code implementations • 2 Nov 2023 • Zalan Fabian, Zhongqi Miao, Chunyuan Li, Yuanhan Zhang, Ziwei Liu, Andrés Hernández, Andrés Montes-Rojas, Rafael Escucha, Laura Siabatto, Andrés Link, Pablo Arbeláez, Rahul Dodhia, Juan Lavista Ferres
In particular, we instruction tune vision-language models to generate detailed visual descriptions of camera trap images using similar terminology to experts.
no code implementations • 17 Aug 2022 • Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, Stella X. Yu
A practical recognition system must balance between majority (head) and minority (tail) classes, generalize across the distribution, and acknowledge novelty upon the instances of unseen classes (open classes).
1 code implementation • 5 May 2021 • Zhongqi Miao, Ziwei Liu, Kaitlyn M. Gaynor, Meredith S. Palmer, Stella X. Yu, Wayne M. Getz
Camera trapping is increasingly used to monitor wildlife, but this technology typically requires extensive data annotation.
2 code implementations • ICLR 2021 • Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, Stella X. Yu
We take a dynamic view of the training data and provide a principled model bias and variance analysis as the training data fluctuates: Existing long-tail classifiers invariably increase the model variance and the head-tail model bias gap remains large, due to more and larger confusion with hard negatives for the tail.
Ranked #24 on Long-tail Learning on iNaturalist 2018
no code implementations • CVPR 2020 • Ziwei Liu, Zhongqi Miao, Xingang Pan, Xiaohang Zhan, Dahua Lin, Stella X. Yu, Boqing Gong
A typical domain adaptation approach is to adapt models trained on the annotated data in a source domain (e. g., sunny weather) for achieving high performance on the test data in a target domain (e. g., rainy weather).
2 code implementations • CVPR 2019 • Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, Stella X. Yu
We define Open Long-Tailed Recognition (OLTR) as learning from such naturally distributed data and optimizing the classification accuracy over a balanced test set which include head, tail, and open classes.