Z
Zhongqi Miao
Researcher at University of California, Berkeley
Publications - 17
Citations - 1309
Zhongqi Miao is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 6, co-authored 10 publications receiving 519 citations. Previous affiliations of Zhongqi Miao include International Computer Science Institute & The Chinese University of Hong Kong.
Papers
More filters
Proceedings ArticleDOI
Large-Scale Long-Tailed Recognition in an Open World
TL;DR: An integrated OLTR algorithm is developed that maps an image to a feature space such that visual concepts can easily relate to each other based on a learned metric that respects the closed-world classification while acknowledging the novelty of the open world.
Posted Content
Large-Scale Long-Tailed Recognition in an Open World
TL;DR: Open Long-Tailed Recognition (OLTR) as mentioned in this paper maps an image to a feature space such that visual concepts can easily relate to each other based on a learned metric that respects the closed-world classification while acknowledging the novelty of the open world.
Posted Content
Long-tailed Recognition by Routing Diverse Distribution-Aware Experts
TL;DR: RIDE aims to reduce both the bias and the variance of a long-tailed classifier by RoutIng Diverse Experts (RIDE), which significantly outperforms the state-of-the-art methods by 5% to 7% on all the benchmarks including CIFAR100-LT, ImageNet-LT and iNaturalist.
Journal ArticleDOI
Insights and approaches using deep learning to classify wildlife.
Zhongqi Miao,Kaitlyn M. Gaynor,Jiayun Wang,Ziwei Liu,Oliver Muellerklein,Oliver Muellerklein,Mohammad Sadegh Norouzzadeh,Alex McInturff,Rauri C. K. Bowie,Ran Nathan,Stella X. Yu,Stella X. Yu,Wayne M. Getz +12 more
TL;DR: Light is shed on the methods themselves and types of features these methods extract to make efficient identifications and reliable classifications of wildlife species from camera-trap data, and presents dataset biases that were revealed by these extracted features.
Proceedings ArticleDOI
Open Compound Domain Adaptation
TL;DR: This work proposes a new approach based on two technical insights into OCDA: a curriculum domain adaptation strategy to bootstrap generalization across domains in a data-driven self-organizing fashion and a memory module to increase the model's agility towards novel domains.