Search Results for author: Chunxiao Liu

Found 16 papers, 13 papers with code

Safe Driving via Expert Guided Policy Optimization

1 code implementation13 Oct 2021 Zhenghao Peng, Quanyi Li, Chunxiao Liu, Bolei Zhou

Offline RL technique is further used to learn from the partial demonstration generated by the expert.

Offline RL reinforcement-learning +1

Network Pruning via Resource Reallocation

1 code implementation2 Mar 2021 Yuenan Hou, Zheng Ma, Chunxiao Liu, Zhe Wang, Chen Change Loy

Channel pruning is broadly recognized as an effective approach to obtain a small compact model through eliminating unimportant channels from a large cumbersome network.

Network Pruning

Improving the Generalization of End-to-End Driving through Procedural Generation

2 code implementations26 Dec 2020 Quanyi Li, Zhenghao Peng, Qihang Zhang, Chunxiao Liu, Bolei Zhou

We validate that training with the increasing number of procedurally generated scenes significantly improves the generalization of the agent across scenarios of different traffic densities and road networks.

Autonomous Driving

Channel-wise Alignment for Adaptive Object Detection

no code implementations7 Sep 2020 Hang Yang, Shan Jiang, Xinge Zhu, Mingyang Huang, Zhiqiang Shen, Chunxiao Liu, Jianping Shi

Existing methods on this task usually draw attention on the high-level alignment based on the whole image or object of interest, which naturally, cannot fully utilize the fine-grained channel information.

Instance Segmentation object-detection +2

Understanding the wiring evolution in differentiable neural architecture search

1 code implementation2 Sep 2020 Sirui Xie, Shoukang Hu, Xinjiang Wang, Chunxiao Liu, Jianping Shi, Xunying Liu, Dahua Lin

To this end, we pose questions that future differentiable methods for neural wiring discovery need to confront, hoping to evoke a discussion and rethinking on how much bias has been enforced implicitly in existing NAS methods.

Neural Architecture Search

Inter-Region Affinity Distillation for Road Marking Segmentation

1 code implementation CVPR 2020 Yuenan Hou, Zheng Ma, Chunxiao Liu, Tak-Wai Hui, Chen Change Loy

We study the problem of distilling knowledge from a large deep teacher network to a much smaller student network for the task of road marking segmentation.

Knowledge Distillation Lane Detection +1

Graph Structured Network for Image-Text Matching

1 code implementation CVPR 2020 Chunxiao Liu, Zhendong Mao, Tianzhu Zhang, Hongtao Xie, Bin Wang, Yongdong Zhang

The GSMN explicitly models object, relation and attribute as a structured phrase, which not only allows to learn correspondence of object, relation and attribute separately, but also benefits to learn fine-grained correspondence of structured phrase.

Image-text matching Text Matching

DSNAS: Direct Neural Architecture Search without Parameter Retraining

1 code implementation CVPR 2020 Shoukang Hu, Sirui Xie, Hehui Zheng, Chunxiao Liu, Jianping Shi, Xunying Liu, Dahua Lin

We argue that given a computer vision task for which a NAS method is expected, this definition can reduce the vaguely-defined NAS evaluation to i) accuracy of this task and ii) the total computation consumed to finally obtain a model with satisfying accuracy.

Neural Architecture Search

Learning a Decision Module by Imitating Driver's Control Behaviors

no code implementations30 Nov 2019 Junning Huang, Sirui Xie, Jiankai Sun, Qiurui Ma, Chunxiao Liu, Jianping Shi, Dahua Lin, Bolei Zhou

In this work, we propose a hybrid framework to learn neural decisions in the classical modular pipeline through end-to-end imitation learning.

Autonomous Driving Imitation Learning

Learning Lightweight Lane Detection CNNs by Self Attention Distillation

2 code implementations ICCV 2019 Yuenan Hou, Zheng Ma, Chunxiao Liu, Chen Change Loy

Training deep models for lane detection is challenging due to the very subtle and sparse supervisory signals inherent in lane annotations.

Knowledge Distillation Lane Detection +1

SNAS: Stochastic Neural Architecture Search

2 code implementations ICLR 2019 Sirui Xie, Hehui Zheng, Chunxiao Liu, Liang Lin

In experiments on CIFAR-10, SNAS takes less epochs to find a cell architecture with state-of-the-art accuracy than non-differentiable evolution-based and reinforcement-learning-based NAS, which is also transferable to ImageNet.

Neural Architecture Search reinforcement-learning +1

Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks

2 code implementations7 Nov 2018 Yuenan Hou, Zheng Ma, Chunxiao Liu, Chen Change Loy

In this paper, we considerably improve the accuracy and robustness of predictions through heterogeneous auxiliary networks feature mimicking, a new and effective training method that provides us with much richer contextual signals apart from steering direction.

Image Segmentation Multi-Task Learning +3

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