Search Results for author: Fenggang Liu

Found 8 papers, 2 papers with code

UniG3D: A Unified 3D Object Generation Dataset

no code implementations19 Jun 2023 Qinghong Sun, Yangguang Li, Zexiang Liu, Xiaoshui Huang, Fenggang Liu, Xihui Liu, Wanli Ouyang, Jing Shao

However, the quality and diversity of existing 3D object generation methods are constrained by the inadequacies of existing 3D object datasets, including issues related to text quality, the incompleteness of multi-modal data representation encompassing 2D rendered images and 3D assets, as well as the size of the dataset.

Autonomous Driving Object

Mask Hierarchical Features For Self-Supervised Learning

no code implementations1 Apr 2023 Fenggang Liu, Yangguang Li, Feng Liang, Jilan Xu, Bin Huang, Jing Shao

We mask part of patches in the representation space and then utilize sparse visible patches to reconstruct high semantic image representation.

object-detection Object Detection +1

Fast-BEV: A Fast and Strong Bird's-Eye View Perception Baseline

1 code implementation29 Jan 2023 Yangguang Li, Bin Huang, Zeren Chen, Yufeng Cui, Feng Liang, Mingzhu Shen, Fenggang Liu, Enze Xie, Lu Sheng, Wanli Ouyang, Jing Shao

Our Fast-BEV consists of five parts, We novelly propose (1) a lightweight deployment-friendly view transformation which fast transfers 2D image feature to 3D voxel space, (2) an multi-scale image encoder which leverages multi-scale information for better performance, (3) an efficient BEV encoder which is particularly designed to speed up on-vehicle inference.

Data Augmentation

Fast-BEV: Towards Real-time On-vehicle Bird's-Eye View Perception

1 code implementation19 Jan 2023 Bin Huang, Yangguang Li, Enze Xie, Feng Liang, Luya Wang, Mingzhu Shen, Fenggang Liu, Tianqi Wang, Ping Luo, Jing Shao

Recently, the pure camera-based Bird's-Eye-View (BEV) perception removes expensive Lidar sensors, making it a feasible solution for economical autonomous driving.

Autonomous Driving Data Augmentation

Neighbor Regularized Bayesian Optimization for Hyperparameter Optimization

no code implementations7 Oct 2022 Lei Cui, Yangguang Li, Xin Lu, Dong An, Fenggang Liu

Bayesian Optimization (BO) is a common solution to search optimal hyperparameters based on sample observations of a machine learning model.

Bayesian Optimization Hyperparameter Optimization

INTERN: A New Learning Paradigm Towards General Vision

no code implementations16 Nov 2021 Jing Shao, Siyu Chen, Yangguang Li, Kun Wang, Zhenfei Yin, Yinan He, Jianing Teng, Qinghong Sun, Mengya Gao, Jihao Liu, Gengshi Huang, Guanglu Song, Yichao Wu, Yuming Huang, Fenggang Liu, Huan Peng, Shuo Qin, Chengyu Wang, Yujie Wang, Conghui He, Ding Liang, Yu Liu, Fengwei Yu, Junjie Yan, Dahua Lin, Xiaogang Wang, Yu Qiao

Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society.

ModuleNet: Knowledge-inherited Neural Architecture Search

no code implementations10 Apr 2020 Yaran Chen, Ruiyuan Gao, Fenggang Liu, Dongbin Zhao

Unlike previous search algorithms, and benefiting from inherited knowledge, our method is able to directly search for architectures in the macro space by NSGA-II algorithm without tuning parameters in these \textit{module}s. Experiments show that our strategy can efficiently evaluate the performance of new architecture even without tuning weights in convolutional layers.

Neural Architecture Search

Cannot find the paper you are looking for? You can Submit a new open access paper.