Search Results for author: Chenhan Jiang

Found 10 papers, 5 papers with code

CO^3: Cooperative Unsupervised 3D Representation Learning for Autonomous Driving

no code implementations8 Jun 2022 Runjian Chen, Yao Mu, Runsen Xu, Wenqi Shao, Chenhan Jiang, Hang Xu, Zhenguo Li, Ping Luo

In this paper, we propose CO^3, namely Cooperative Contrastive Learning and Contextual Shape Prediction, to learn 3D representation for outdoor-scene point clouds in an unsupervised manner.

Autonomous Driving Contrastive Learning +1

One Million Scenes for Autonomous Driving: ONCE Dataset

1 code implementation21 Jun 2021 Jiageng Mao, Minzhe Niu, Chenhan Jiang, Hanxue Liang, Jingheng Chen, Xiaodan Liang, Yamin Li, Chaoqiang Ye, Wei zhang, Zhenguo Li, Jie Yu, Hang Xu, Chunjing Xu

To facilitate future research on exploiting unlabeled data for 3D detection, we additionally provide a benchmark in which we reproduce and evaluate a variety of self-supervised and semi-supervised methods on the ONCE dataset.

3D Object Detection Autonomous Driving +1

Exploring Geometry-Aware Contrast and Clustering Harmonization for Self-Supervised 3D Object Detection

no code implementations ICCV 2021 Hanxue Liang, Chenhan Jiang, Dapeng Feng, Xin Chen, Hang Xu, Xiaodan Liang, Wei zhang, Zhenguo Li, Luc van Gool

Here we present a novel self-supervised 3D Object detection framework that seamlessly integrates the geometry-aware contrast and clustering harmonization to lift the unsupervised 3D representation learning, named GCC-3D.

3D Object Detection object-detection +2

ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection

no code implementations3 Mar 2020 Chenhan Jiang, Shaoju Wang, Hang Xu, Xiaodan Liang, Nong Xiao

Is a hand-crafted detection network tailored for natural image undoubtedly good enough over a discrepant medical lesion domain?

Lesion Detection

Layout-Graph Reasoning for Fashion Landmark Detection

no code implementations CVPR 2019 Weijiang Yu, Xiaodan Liang, Ke Gong, Chenhan Jiang, Nong Xiao, Liang Lin

Each Layout-Graph Reasoning(LGR) layer aims to map feature representations into structural graph nodes via a Map-to-Node module, performs reasoning over structural graph nodes to achieve global layout coherency via a layout-graph reasoning module, and then maps graph nodes back to enhance feature representations via a Node-to-Map module.

Graph Clustering

3D Human Pose Machines with Self-supervised Learning

2 code implementations arXiv.org 2019 Keze Wang, Liang Lin, Chenhan Jiang, Chen Qian, Pengxu Wei

Driven by recent computer vision and robotic applications, recovering 3D human poses has become increasingly important and attracted growing interests.

3D Human Pose Estimation Computer Vision +1

Hybrid Knowledge Routed Modules for Large-scale Object Detection

1 code implementation NeurIPS 2018 Chenhan Jiang, Hang Xu, Xiangdan Liang, Liang Lin

The dominant object detection approaches treat the recognition of each region separately and overlook crucial semantic correlations between objects in one scene.

object-detection Object Detection

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