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

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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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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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This paper cites Towards Deep Neural Network Architectures Robust to Adversarial Examples.

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

Reference 31

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

Reference 32

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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

Reference 34

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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

Reference 42

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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

Reference 44

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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

Reference 45

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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

Reference 46

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Benchmarking the Robustness of Semantic Segmentation Models Joshi, R

Reference 47

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Benchmarking the Robustness of Semantic Segmentation Models Kamann, S

Reference 48

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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

Reference 50

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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

Reference 52

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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

Reference 54

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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

Reference 56

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This paper cites Lukas, J.

Benchmarking the Robustness of Semantic Segmentation Models Lukas, J

Reference 57

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Observation f2c81d59-7a25-47b2-92ae-0f8a55dac216 · outbound

This paper cites On Detecting Adversarial Perturbations.

Benchmarking the Robustness of Semantic Segmentation Models On Detecting Adversarial Perturbations

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:46.645871Z

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This paper cites Michaelis, B.

Benchmarking the Robustness of Semantic Segmentation Models Michaelis, B

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:46.607415Z

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Observation 4f750659-d3b8-49f4-8b7f-65727611ea34 · outbound

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

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:46.571580Z

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Observation e48e9205-9a7b-4ca3-b6eb-0b0654dacf0a · outbound

This paper cites Exploring Generaliza- tion in Deep Learning.

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

Reference 61

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Observation 09aadb36-4f27-49f2-a8d7-1f985f1aa5b0 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:46.463286Z

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Observation d717e212-91d8-45c1-a254-f1d164ae19df · outbound

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

Reference 63

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Observation b6146a75-9dd4-4367-9eaf-96af591c9263 · outbound

This paper cites Automatic Dif- ferentiation in PyTorch.

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

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:46.363678Z

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Observation abc826a0-ec0f-4cb5-8b50-e0ba4bfce7bb · outbound

This paper cites Efﬁcient neural architecture search via parameter sharing.

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

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:46.289971Z

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Observation 7adf764a-7a02-441e-80f2-a20478f87b51 · outbound

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

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:46.220629Z

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Observation b9faa0af-6333-4430-b08c-0aba9f321163 · outbound

This paper cites Girshick, and Ali Farhadi.

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

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:46.142484Z

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Observation b7cea476-2c3d-4785-9960-08e2af12440b · outbound

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

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:45.991770Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:45.938870Z

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Observation 8a72e5ad-6476-4983-bf2a-7e5bd0b6bef1 · outbound

This paper cites MobileNetV2: Inverted Residuals and Linear Bottlenecks.

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

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:45.893717Z

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Observation 13d86d64-9cdc-438a-8e83-ec24b7d0f04a · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:45.867169Z

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Observation 3b6a92a4-d73e-43eb-8ba9-09eead8cc18e · outbound

This paper cites an unresolved cited work.

Benchmarking the Robustness of Semantic Segmentation Models Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:30:45.814462Z

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Observation 3dadf628-9217-4d8e-9004-49c8ca22aa4a · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:45.775645Z

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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

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:45.752675Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:45.724653Z

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This paper cites Going deeper with convolutions.

Benchmarking the Robustness of Semantic Segmentation Models Going deeper with convolutions

Reference 76

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Observation a8519f15-2b0a-471a-bbfe-294d90757e7f · outbound

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

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:45.668524Z

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Observation 05e89412-bbda-449b-a313-56853c8b24d5 · outbound

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

Reference 78

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Observation 05bed703-aedf-462e-abed-5a37b0ea3ea6 · outbound

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

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:45.639594Z

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Observation 1d730401-0b41-4246-8ef0-4212055a1b42 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:45.606552Z

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Observation b7fceead-39f4-4a88-aaa8-dc40743f3a04 · outbound

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

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:45.569727Z

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Observation 8378e857-052a-4b6f-b1bd-005844aa0a14 · outbound

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

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:45.535194Z

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Observation 84f3483f-eb15-4056-9801-4b5a2fdf5ec1 · outbound

This paper cites Delft University of Technology Delft, 1998.

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

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:45.422492Z

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Observation 94d003bc-a62b-4184-850d-e86f67a5b6e4 · outbound

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

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:45.374675Z

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Observation e59426f0-1415-49ac-ace5-fb7bec533e86 · outbound

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

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:45.324646Z

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Observation 20d29e71-3e24-4f1f-b343-40509eefe32d · outbound

This paper cites Pyramid Scene Parsing Network.

Benchmarking the Robustness of Semantic Segmentation Models Pyramid Scene Parsing Network

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:45.254147Z

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Observation 7ee4d142-a84c-40cb-82a5-389d2720fa7a · outbound

This paper cites Good- fellow.

Benchmarking the Robustness of Semantic Segmentation Models Good- fellow

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:45.215620Z

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Observation 1b0d039e-e3df-4b18-bab9-f036c6be575b · outbound

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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Observation 4e3e5915-03c6-434a-8c7f-f13cd0cfd4da · outbound

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

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:45.137594Z

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Observation a80a1464-c2de-4cac-bf24-7aaff0c425ee · outbound

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

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:45.083126Z

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Observation e542ae6c-80ca-4768-9365-f72720dfc742 · outbound

This paper cites an unresolved cited work.

Benchmarking the Robustness of Semantic Segmentation Models Unresolved cited work

Reference 91

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:30:45.023660Z

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Observation 5f3e4c1a-bc64-469f-9939-6befe9ddea11 · outbound

This paper cites jpeg compression.

Benchmarking the Robustness of Semantic Segmentation Models jpeg compression

Reference 92

Resolution: malformed identifier; raw_fallback, observed 2026-08-14T13:30:44.976212Z

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Observation cdddfd3b-8067-43eb-bea0-acd0b4600750 · outbound

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

Resolution: malformed identifier; no resolver link, observed 2026-08-14T13:30:44.112469Z

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