(CVPR 2016) Traffic-Sign Detection and Classification in the Wild

Paper: https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper.pdf

Page: https://cg.cs.tsinghua.edu.cn/traffic-sign/

Code: http://cg.cs.tsinghua.edu.cn/traffic-sign/data_model_code/code.zip

(ICML 2018) Efficient Neural Architecture Search via Parameter Sharing

Paper: https://arxiv.org/abs/1802.03268

Code: https://github.com/melodyguan/enas

(ICLR 2017) Neural Architecture Search with Reinforcement Learning

Paper: https://arxiv.org/abs/1611.01578

Page: https://ai.googleblog.com/2017/05/using-machine-learning-to-explore.html

(CVPR 2018) Learning Transferable Architectures for Scalable Image Recognition

Paper: https://arxiv.org/abs/1707.07012

Page: https://ai.googleblog.com/2017/11/automl-for-large-scale-image.html

Code: https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet

DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications

Paper: https://arxiv.org/abs/1711.05073

Page: http://ai.baidu.com/broad/subordinate?dataset=dureader

Code: https://github.com/baidu/DuReader/

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

Paper: https://arxiv.org/abs/1802.02611

Blog: https://research.googleblog.com/2018/03/semantic-image-segmentation-with.html

Code: https://github.com/tensorflow/models/tree/master/research/deeplab

Rethinking Atrous Convolution for Semantic Image Segmentation
Paper: https://arxiv.org/abs/1706.05587

ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
Paper: https://arxiv.org/abs/1606.02147
Code: https://github.com/e-lab/ENet-training

ICNet for Real-Time Semantic Segmentation on High-Resolution Images
Paper: https://arxiv.org/abs/1704.08545
Code: https://github.com/hszhao/ICNet

(CVPR 2017) Simple Does It: Weakly Supervised Instance and Semantic Segmentation
Paper: https://arxiv.org/abs/1603.07485
Project Page: https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/weakly-supervised-learning/simple-does-it-weakly-supervised-instance-and-semantic-segmentation/