2 code implementations • ECCV 2018 • Zehao Huang, Naiyan Wang
Deep convolutional neural networks have liberated its extraordinary power on various tasks.
1 code implementation • ICLR 2019 • Zehao Huang, Naiyan Wang
In this paper, we propose a novel knowledge transfer method by treating it as a distribution matching problem.
1 code implementation • 5 Nov 2018 • Xinbang Zhang, Zehao Huang, Naiyan Wang
Recently Neural Architecture Search (NAS) has aroused great interest in both academia and industry, however it remains challenging because of its huge and non-continuous search space.
1 code implementation • 14 Mar 2019 • Yuntao Chen, Chenxia Han, Yanghao Li, Zehao Huang, Yi Jiang, Naiyan Wang, Zhao-Xiang Zhang
A Simple and Versatile Framework for Object Detection and Instance Recognition
no code implementations • ICLR 2019 • Xinbang Zhang, Zehao Huang, Naiyan Wang
Recently Neural Architecture Search (NAS) has aroused great interest in both academia and industry, however it remains challenging because of its huge and non-continuous search space.
1 code implementation • 4 Aug 2020 • Zehao Huang, Zehui Chen, Qiaofei Li, Hongkai Zhang, Naiyan Wang
In this technical report, we present our solutions of Waymo Open Dataset (WOD) Challenge 2020 - 2D Object Track.
1 code implementation • CVPR 2022 • Chenhongyi Yang, Zehao Huang, Naiyan Wang
On the popular COCO dataset, the proposed method improves the detection mAP by 1. 0 and mAP-small by 2. 0, and the high-resolution inference speed is improved to 3. 0x on average.
1 code implementation • CVPR 2021 • JiaWei He, Zehao Huang, Naiyan Wang, Zhaoxiang Zhang
Then the association problem turns into a general graph matching between tracklet graph and detection graph.
1 code implementation • ICCV 2021 • Aoming Liu, Zehao Huang, Zhiwu Huang, Naiyan Wang
Data augmentation has been an indispensable tool to improve the performance of deep neural networks, however the augmentation can hardly transfer among different tasks and datasets.
no code implementations • 26 Oct 2022 • Liuchun Yuan, Zehao Huang, Naiyan Wang
In this paper, we present a general and effective framework for Neural Architecture Search (NAS), named PredNAS.
1 code implementation • CVPR 2023 • Shaofei Huang, Zhenwei Shen, Zehao Huang, Zi-han Ding, Jiao Dai, Jizhong Han, Naiyan Wang, Si Liu
An attempt has been made to get rid of BEV and predict 3D lanes from FV representations directly, while it still underperforms other BEV-based methods given its lack of structured representation for 3D lanes.
Ranked #3 on 3D Lane Detection on Apollo Synthetic 3D Lane
1 code implementation • ICCV 2023 • Zitian Wang, Zehao Huang, Jiahui Fu, Naiyan Wang, Si Liu
Existing methods mainly establish 3D representations from multi-view images and adopt a dense detection head for object detection, or employ object queries distributed in 3D space to localize objects.
1 code implementation • 27 Mar 2023 • JiaWei He, Zehao Huang, Naiyan Wang, Zhaoxiang Zhang
Data association is at the core of many computer vision tasks, e. g., multiple object tracking, image matching, and point cloud registration.
1 code implementation • 24 Apr 2023 • Yingyan Li, Lue Fan, Yang Liu, Zehao Huang, Yuntao Chen, Naiyan Wang, Zhaoxiang Zhang, Tieniu Tan
In this paper, we study how to effectively leverage image modality in the emerging fully sparse architecture.
no code implementations • 8 Jun 2023 • JiaWei He, Lue Fan, Yuqi Wang, Yuntao Chen, Zehao Huang, Naiyan Wang, Zhaoxiang Zhang
In this paper, we rethink the data association in 2D MOT and utilize the 3D object representation to separate each object in the feature space.
1 code implementation • 4 Sep 2023 • Yuheng Shi, Zehao Huang, Yan Yan, Naiyan Wang, Xiaojie Guo
Time-to-Contact (TTC) estimation is a critical task for assessing collision risk and is widely used in various driver assistance and autonomous driving systems.
no code implementations • 15 Mar 2024 • Yiheng Li, Hongyang Li, Zehao Huang, Hong Chang, Naiyan Wang
The versatility of SparseFusion is also validated in the temporal object detection task and 3D lane detection task.