OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System

no code implementations • 1 Mar 2023 • Chao Xue, Wei Liu, Shuai Xie, Zhenfang Wang, Jiaxing Li, Xuyang Peng, Liang Ding, Shanshan Zhao, Qiong Cao, Yibo Yang, Fengxiang He, Bohua Cai, Rongcheng Bian, Yiyan Zhao, Heliang Zheng, Xiangyang Liu, Dongkai Liu, Daqing Liu, Li Shen, Chang Li, Shijin Zhang, Yukang Zhang, Guanpu Chen, Shixiang Chen, Yibing Zhan, Jing Zhang, Chaoyue Wang, DaCheng Tao

Automated machine learning (AutoML) seeks to build ML models with minimal human effort.

AutoML

Prior Knowledge about Attributes: Learning a More Effective Potential Space for Zero-Shot Recognition

no code implementations • 14 Sep 2020 • Chunlai Chai, Yukuan Lou, Shijin Zhang

Zero-shot learning (ZSL) aims to recognize unseen classes accurately by learning seen classes and known attributes, but correlations in attributes were ignored by previous study which lead to classification results confused.

Attribute Zero-Shot Learning

Cascade Network for Self-Supervised Monocular Depth Estimation

no code implementations • 14 Sep 2020 • Chunlai Chai, Yukuan Lou, Shijin Zhang

The third step is to use models with different parameters to generate depth maps of different depth parts in the target scene, and the fourth step is to fuse the depth map.

Monocular Depth Estimation Self-Supervised Learning