Fetching the paper…
Reading the bibliography…
Modern deep learning architectures produce highly accurate results on many challenging semantic segmentation datasets.
The Laplacian Pyramid as a Compact Image Code
Burt, P.J., Adelson, E.H.: · 1987
Earlier work this paper cites.
Semantic object classes in video: A high-definition ground truth database
Brostow, G.J., Fauqueur, J., Cipolla, R.: · 2009
Earlier work this paper cites.
Lecture 6.5-RMSProp, Coursera: neural networks for machine learning
Tieleman, T., Hinton, G.: · 2012
Earlier work this paper cites.
Object scene flow for autonomous vehicles
Menze, M., Geiger, A.: · 2015
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
Earlier work this paper cites.
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., Brox, T.: · 2015
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems (2015)
Abadi, M., et. al.: · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A., Fei-Fei, L.: · 2015
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: · 2016
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., Darrell, T.: · 2016
Earlier work this paper cites.
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: · 2016
Cited alongside, same era.
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
Paszke, A., Chaurasia, A., Kim, S., Culurciello, E.: · 2016
Cited alongside, same era.
Binarized Neural Networks
Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y.: · 2016
Cited alongside, same era.
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: · 2016
Cited alongside, same era.
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F.: · 2016
Cited alongside, same era.
Pyramid Scene Parsing Network
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: · 2017
Later among the works it cites.
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
Zhao, H., Qi, X., Shen, X., Shi, J., Jia, J.: · 2017
Later among the works it cites.
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: · 2017
Later among the works it cites.
Pruning Filters for Efficient ConvNets
Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: · 2017
Later among the works it cites.
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
Lin, G., Milan, A., Shen, C., Reid, I.: · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
Han, S., Mao, H., Dally, W.J.: · 2016
Cited alongside, same era.
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: · 2016
Cited alongside, same era.
Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation
Ghiasi, G., Fowlkes, C.C.: · 2016
Cited alongside, same era.
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., Cipolla, R.: · 2017
Cited alongside, same era.
ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation
Romera, E., Álvarez, J.M., Bergasa, L.M., Arroyo, R.: · 2018
Closest in time.
Training and Inference with Integers in Deep Neural Networks
Wu, S., Li, G., Chen, F., Shi, L.: · 2018
Closest in time.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: · 2018
Closest in time.
The lottery ticket hypothesis: Training pruned neural networks
Frankle, J., Carbin, M.: · 2018
Closest in time.