Search Results for author: Runjian Chen

Found 7 papers, 4 papers with code

CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer

no code implementations17 Jun 2022 Yao Mu, Shoufa Chen, Mingyu Ding, Jianyu Chen, Runjian Chen, Ping Luo

In visual control, learning transferable state representation that can transfer between different control tasks is important to reduce the training sample size.

Transfer Learning

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

1 code implementation8 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

CycleMLP: A MLP-like Architecture for Dense Prediction

8 code implementations ICLR 2022 Shoufa Chen, Enze Xie, Chongjian Ge, Runjian Chen, Ding Liang, Ping Luo

We build a family of models which surpass existing MLPs and even state-of-the-art Transformer-based models, e. g., Swin Transformer, while using fewer parameters and FLOPs.

Image Classification Instance Segmentation +3

RaLL: End-to-end Radar Localization on Lidar Map Using Differentiable Measurement Model

1 code implementation15 Sep 2020 Huan Yin, Runjian Chen, Yue Wang, Rong Xiong

In this paper, we propose an end-to-end deep learning framework for Radar Localization on Lidar Map (RaLL) to bridge the gap, which not only achieves the robust radar localization but also exploits the mature lidar mapping technique, thus reducing the cost of radar mapping.

CoSimGNN: Towards Large-scale Graph Similarity Computation

no code implementations14 May 2020 Haoyan Xu, Runjian Chen, Yueyang Wang, Ziheng Duan, Jie Feng

In this paper, we focus on similarity computation for large-scale graphs and propose the "embedding-coarsening-matching" framework CoSimGNN, which first embeds and coarsens large graphs with adaptive pooling operation and then deploys fine-grained interactions on the coarsened graphs for final similarity scores.

3D Action Recognition Graph Similarity

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