Benchmarking the Robustness of Semantic Segmentation Models | Paper Citation Record

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This paper cites Mur- ray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete War- den, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng.

Benchmarking the Robustness of Semantic Segmentation Models Mur- ray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete War- den, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng

Reference 1

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Reference 2

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This paper cites Why do deep convolutional networks generalize so poorly to small image transformations?.

Benchmarking the Robustness of Semantic Segmentation Models Why do deep convolutional networks generalize so poorly to small image transformations?

Reference 3

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This paper cites SegNet: A Deep Convolutional Encoder-Decoder Architec- ture for Image Segmentation.

Benchmarking the Robustness of Semantic Segmentation Models SegNet: A Deep Convolutional Encoder-Decoder Architec- ture for Image Segmentation

Reference 4

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This paper cites Learning to Remove Rain in Traf- ﬁc Surveillance by Using Synthetic Data.

Benchmarking the Robustness of Semantic Segmentation Models Learning to Remove Rain in Traf- ﬁc Surveillance by Using Synthetic Data

Reference 5

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This paper cites CNN-Cert: An Efﬁcient Framework for Certifying Robustness of Convolutional Neural Networks.

Benchmarking the Robustness of Semantic Segmentation Models CNN-Cert: An Efﬁcient Framework for Certifying Robustness of Convolutional Neural Networks

Reference 6

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This paper cites DeepCorrect: Correcting DNN models against Image Distortions.

Benchmarking the Robustness of Semantic Segmentation Models DeepCorrect: Correcting DNN models against Image Distortions

Reference 7

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This paper cites Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods.

Benchmarking the Robustness of Semantic Segmentation Models Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods

Reference 8

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Reference 9

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This paper cites Collins, Yukun Zhu, George Papandreou, Barret Zoph, Florian Schroff, Hartwig Adam, and Jonathon Shlens.

Benchmarking the Robustness of Semantic Segmentation Models Collins, Yukun Zhu, George Papandreou, Barret Zoph, Florian Schroff, Hartwig Adam, and Jonathon Shlens

Reference 10

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This paper cites Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs.

Benchmarking the Robustness of Semantic Segmentation Models Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Reference 11

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Reference 12

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This paper cites Rethinking Atrous Convolution for Seman- tic Image Segmentation, 2017.

Benchmarking the Robustness of Semantic Segmentation Models Rethinking Atrous Convolution for Seman- tic Image Segmentation, 2017

Reference 13

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This paper cites Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.

Benchmarking the Robustness of Semantic Segmentation Models Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

Reference 14

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This paper cites Domain adaptive faster r-cnn for object de- tection in the wild.

Benchmarking the Robustness of Semantic Segmentation Models Domain adaptive faster r-cnn for object de- tection in the wild

Reference 15

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This paper cites Xception: Deep Learning with Depthwise Separable Convolutions.

Benchmarking the Robustness of Semantic Segmentation Models Xception: Deep Learning with Depthwise Separable Convolutions

Reference 16

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This paper cites Parseval Networks: Improv- ing Robustness to Adversarial Examples.

Benchmarking the Robustness of Semantic Segmentation Models Parseval Networks: Improv- ing Robustness to Adversarial Examples

Reference 17

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This paper cites The Cityscapes Dataset for Semantic Urban Scene Understanding.

Benchmarking the Robustness of Semantic Segmentation Models The Cityscapes Dataset for Semantic Urban Scene Understanding

Reference 18

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This paper cites Le.Intriguing Properties of Adversarial Exam- ples.

Benchmarking the Robustness of Semantic Segmentation Models Le.Intriguing Properties of Adversarial Exam- ples

Reference 19

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This paper cites Dark model adaptation: Semantic image segmentation from daytime to nighttime.

Benchmarking the Robustness of Semantic Segmentation Models Dark model adaptation: Semantic image segmentation from daytime to nighttime

Reference 20

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Reference 21

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This paper cites A study and comparison of human and deep learning recognition performance under visual distortions.

Benchmarking the Robustness of Semantic Segmentation Models A study and comparison of human and deep learning recognition performance under visual distortions

Reference 22

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Benchmarking the Robustness of Semantic Segmentation Models Dodge and Lina J

Reference 23

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Reference 24

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Reference 25

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This paper cites Geirhos, P.

Benchmarking the Robustness of Semantic Segmentation Models Geirhos, P

Reference 26

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This paper cites Generalisation in humans and deep neural networks.

Benchmarking the Robustness of Semantic Segmentation Models Generalisation in humans and deep neural networks

Reference 27

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This paper cites Adversarial Examples Are a Natural Consequence of Test Error in Noise.

Benchmarking the Robustness of Semantic Segmentation Models Adversarial Examples Are a Natural Consequence of Test Error in Noise

Reference 28

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This paper cites Deep Learning.

Benchmarking the Robustness of Semantic Segmentation Models Deep Learning

Reference 29

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Benchmarking the Robustness of Semantic Segmentation Models Grauman and T

Reference 30

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Benchmarking the Robustness of Semantic Segmentation Models Towards Deep Neural Network Architectures Robust to Adversarial Examples

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Benchmarking the Robustness of Semantic Segmentation Models Hypercolumns for object segmentation and ﬁne- grained localization

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Benchmarking the Robustness of Semantic Segmentation Models Multiple view ge- ometry in computer vision

Reference 33

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Benchmarking the Robustness of Semantic Segmentation Models Hasirlioglu, A

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This paper cites Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.

Benchmarking the Robustness of Semantic Segmentation Models Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

Reference 35

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This paper cites Delving Deep into Rectiﬁers: Surpassing Human-Level Per- formance on ImageNet Classiﬁcation.

Benchmarking the Robustness of Semantic Segmentation Models Delving Deep into Rectiﬁers: Surpassing Human-Level Per- formance on ImageNet Classiﬁcation

Reference 36

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This paper cites Deep Residual Learning for Image Recognition.

Benchmarking the Robustness of Semantic Segmentation Models Deep Residual Learning for Image Recognition

Reference 37

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This paper cites Radiometric CCD camera calibration and noise estimation.

Benchmarking the Robustness of Semantic Segmentation Models Radiometric CCD camera calibration and noise estimation

Reference 38

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This paper cites Benchmarking Neu- ral Network Robustness to Common Corruptions and Per- turbations.

Benchmarking the Robustness of Semantic Segmentation Models Benchmarking Neu- ral Network Robustness to Common Corruptions and Per- turbations

Reference 39

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Benchmarking the Robustness of Semantic Segmentation Models Henriques and Andrea Vedaldi

Reference 40

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Benchmarking the Robustness of Semantic Segmentation Models Holschneider, R

Reference 41

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This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Benchmarking the Robustness of Semantic Segmentation Models MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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Benchmarking the Robustness of Semantic Segmentation Models Weinberger

Reference 43

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This paper cites Kwiatkowska, Sen Wang, and Min Wu.

Benchmarking the Robustness of Semantic Segmentation Models Kwiatkowska, Sen Wang, and Min Wu

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This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Benchmarking the Robustness of Semantic Segmentation Models Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

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This paper cites Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art.

Benchmarking the Robustness of Semantic Segmentation Models Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art

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Benchmarking the Robustness of Semantic Segmentation Models Joshi, R

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Benchmarking the Robustness of Semantic Segmentation Models Kamann, S

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Benchmarking the Robustness of Semantic Segmentation Models Unresolved cited work

Reference 49

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This paper cites Imagenet classiﬁcation with deep convolutional neural net- works.

Benchmarking the Robustness of Semantic Segmentation Models Imagenet classiﬁcation with deep convolutional neural net- works

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This paper cites Be- yond Bags of Features: Spatial Pyramid Matching for Rec- ognizing Natural Scene Categories.

Benchmarking the Robustness of Semantic Segmentation Models Be- yond Bags of Features: Spatial Pyramid Matching for Rec- ognizing Natural Scene Categories

Reference 51

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Benchmarking the Robustness of Semantic Segmentation Models Unresolved cited work

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This paper cites Gradient-based learning applied to document recog- nition.

Benchmarking the Robustness of Semantic Segmentation Models Gradient-based learning applied to document recog- nition

Reference 53

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Benchmarking the Robustness of Semantic Segmentation Models Network in net- work

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Reference 55

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This paper cites Fully Convolutional Networks for Semantic Segmentation.

Benchmarking the Robustness of Semantic Segmentation Models Fully Convolutional Networks for Semantic Segmentation

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This paper cites Lukas, J.

Benchmarking the Robustness of Semantic Segmentation Models Lukas, J

Reference 57

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This paper cites On Detecting Adversarial Perturbations.

Benchmarking the Robustness of Semantic Segmentation Models On Detecting Adversarial Perturbations

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This paper cites Michaelis, B.

Benchmarking the Robustness of Semantic Segmentation Models Michaelis, B

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This paper cites Visual Quality Enhancement Of Images Under Adverse Weather Conditions.

Benchmarking the Robustness of Semantic Segmentation Models Visual Quality Enhancement Of Images Under Adverse Weather Conditions

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This paper cites Exploring Generaliza- tion in Deep Learning.

Benchmarking the Robustness of Semantic Segmentation Models Exploring Generaliza- tion in Deep Learning

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This paper cites Modeling local and global deformations in Deep Learning: Epitomic convolution, Multiple Instance Learn- ing, and sliding window detection.

Benchmarking the Robustness of Semantic Segmentation Models Modeling local and global deformations in Deep Learning: Epitomic convolution, Multiple Instance Learn- ing, and sliding window detection

Reference 62

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This paper cites ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.

Benchmarking the Robustness of Semantic Segmentation Models ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

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This paper cites Automatic Dif- ferentiation in PyTorch.

Benchmarking the Robustness of Semantic Segmentation Models Automatic Dif- ferentiation in PyTorch

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This paper cites Efﬁcient neural architecture search via parameter sharing.

Benchmarking the Robustness of Semantic Segmentation Models Efﬁcient neural architecture search via parameter sharing

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This paper cites Deformable convolutional net- workscoco detection and segmentation challenge 2017 entry.

Benchmarking the Robustness of Semantic Segmentation Models Deformable convolutional net- workscoco detection and segmentation challenge 2017 entry

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This paper cites Girshick, and Ali Farhadi.

Benchmarking the Robustness of Semantic Segmentation Models Girshick, and Ali Farhadi

Reference 67

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This paper cites Se- mantic foggy scene understanding with synthetic data.IJCV, 126(9):973–992, 2018.

Benchmarking the Robustness of Semantic Segmentation Models Se- mantic foggy scene understanding with synthetic data.IJCV, 126(9):973–992, 2018

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This paper cites Guided Curriculum Model Adaptation and Uncertainty-Aware Eval- uation for Semantic Nighttime Image Segmentation.

Benchmarking the Robustness of Semantic Segmentation Models Guided Curriculum Model Adaptation and Uncertainty-Aware Eval- uation for Semantic Nighttime Image Segmentation

Reference 69

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This paper cites MobileNetV2: Inverted Residuals and Linear Bottlenecks.

Benchmarking the Robustness of Semantic Segmentation Models MobileNetV2: Inverted Residuals and Linear Bottlenecks

Reference 70

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This paper cites Overfeat: Integrated recognition, localization and detection using convolutional networks.

Benchmarking the Robustness of Semantic Segmentation Models Overfeat: Integrated recognition, localization and detection using convolutional networks

Reference 71

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Benchmarking the Robustness of Semantic Segmentation Models Unresolved cited work

Reference 72

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This paper cites Intrinsic parameter cali- bration procedure for a (high-distortion) ﬁsh-eye lens cam- era with distortion model and accuracy estimation.

Benchmarking the Robustness of Semantic Segmentation Models Intrinsic parameter cali- bration procedure for a (high-distortion) ﬁsh-eye lens cam- era with distortion model and accuracy estimation

Reference 73

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This paper cites Very Deep Con- volutional Networks for Large-Scale Image Recognition.

Benchmarking the Robustness of Semantic Segmentation Models Very Deep Con- volutional Networks for Large-Scale Image Recognition

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This paper cites Feature Quantization for Defending Against Dis- tortion of Images.

Benchmarking the Robustness of Semantic Segmentation Models Feature Quantization for Defending Against Dis- tortion of Images

Reference 75

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This paper cites Going deeper with convolutions.

Benchmarking the Robustness of Semantic Segmentation Models Going deeper with convolutions

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This paper cites Gated-SCNN: Gated Shape CNNs for Semantic Seg- mentation.

Benchmarking the Robustness of Semantic Segmentation Models Gated-SCNN: Gated Shape CNNs for Semantic Seg- mentation

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This paper cites Examining the Impact of Blur on Recognition by Convolutional Networks.

Benchmarking the Robustness of Semantic Segmentation Models Examining the Impact of Blur on Recognition by Convolutional Networks

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This paper cites Towards Robust CNN- Based Object Detection through Augmentation with Syn- thetic Rain Variations.

Benchmarking the Robustness of Semantic Segmentation Models Towards Robust CNN- Based Object Detection through Augmentation with Syn- thetic Rain Variations

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This paper cites Modeling and calibration of automated zoom lenses.

Benchmarking the Robustness of Semantic Segmentation Models Modeling and calibration of automated zoom lenses

Reference 80

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This paper cites Wider or deeper: Revisiting the resnet model for visual recognition.

Benchmarking the Robustness of Semantic Segmentation Models Wider or deeper: Revisiting the resnet model for visual recognition

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This paper cites Enhancing the Perfor- mance of Convolutional Neural Networks on Quality De- graded Datasets.

Benchmarking the Robustness of Semantic Segmentation Models Enhancing the Perfor- mance of Convolutional Neural Networks on Quality De- graded Datasets

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This paper cites Delft University of Technology Delft, 1998.

Benchmarking the Robustness of Semantic Segmentation Models Delft University of Technology Delft, 1998

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This paper cites Multi-Scale Context Aggre- gation by Dilated Convolutions.

Benchmarking the Robustness of Semantic Segmentation Models Multi-Scale Context Aggre- gation by Dilated Convolutions

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This paper cites ICNet for Real-Time Semantic Segmen- tation on High-Resolution Images.

Benchmarking the Robustness of Semantic Segmentation Models ICNet for Real-Time Semantic Segmen- tation on High-Resolution Images

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This paper cites Pyramid Scene Parsing Network.

Benchmarking the Robustness of Semantic Segmentation Models Pyramid Scene Parsing Network

Reference 86

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This paper cites Good- fellow.

Benchmarking the Robustness of Semantic Segmentation Models Good- fellow

Reference 87

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This paper cites Scene parsing through ade20k dataset.

Benchmarking the Robustness of Semantic Segmentation Models Scene parsing through ade20k dataset

Reference 88

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This paper cites Semantic under- standing of scenes through the ade20k dataset.

Benchmarking the Robustness of Semantic Segmentation Models Semantic under- standing of scenes through the ade20k dataset

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This paper cites On Classiﬁ- cation of Distorted Images with Deep Convolutional Neural Networks.

Benchmarking the Robustness of Semantic Segmentation Models On Classiﬁ- cation of Distorted Images with Deep Convolutional Neural Networks

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Benchmarking the Robustness of Semantic Segmentation Models Unresolved cited work

Reference 91

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This paper cites jpeg compression.

Benchmarking the Robustness of Semantic Segmentation Models jpeg compression

Reference 92

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This paper cites On ADE20K, the mIoU de- creases between 1.2 % (Xception-65) and 7.7 % (ResNet- 50).

Benchmarking the Robustness of Semantic Segmentation Models On ADE20K, the mIoU de- creases between 1.2 % (Xception-65) and 7.7 % (ResNet- 50)

Reference 93

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