SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification | Paper Citation Record

Observation fd739339-c48b-42e6-8888-fd79c1018e5a · outbound

This paper cites Semantic understanding of scenes through the ade20k dataset.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Semantic understanding of scenes through the ade20k dataset

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This paper cites Microsoft coco: Common objects in context.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Microsoft coco: Common objects in context

Reference 2

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This paper cites Benchmarking neural network robustness to common corruptions and perturbations.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Benchmarking neural network robustness to common corruptions and perturbations

Reference 3

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This paper cites 3d common corruptions and data augmentation.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification 3d common corruptions and data augmentation

Reference 4

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Observation b3572c33-a571-4546-aa11-107e1c90fd6d · outbound

This paper cites What Do Compressed Deep Neural Networks Forget?.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification What Do Compressed Deep Neural Networks Forget?

Reference 5

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This paper cites Goodfellow, and Samy Bengio.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Goodfellow, and Samy Bengio

Reference 6

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Observation 5b9456ea-6afc-4131-9070-cbe8704a19ce · outbound

This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 7

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Observation fe49a54c-db10-4ad9-b5f0-5fefae01f83c · outbound

This paper cites Attacking motion estimation with adver- sarial snow.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Attacking motion estimation with adver- sarial snow

Reference 8

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Observation ba667ed3-cce8-4b98-a734-be71db17fcda · outbound

This paper cites Medyolo: A medical image object detection framework.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Medyolo: A medical image object detection framework

Reference 9

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Observation 4b1d9940-60ef-4db7-af70-6c24e98c3e12 · outbound

This paper cites Surgical instrument detection algorithm based on improved yolov7x.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Surgical instrument detection algorithm based on improved yolov7x

Reference 10

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Observation d9c7db39-b564-44d9-b42a-2063c055c986 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification U-net: Convolutional networks for biomedical image segmentation

Reference 11

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Observation 88b2dc35-5ccb-4102-91aa-1d424912f017 · outbound

This paper cites Using duck-net for polyp image segmentation.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Using duck-net for polyp image segmentation

Reference 12

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Observation df2c2351-0121-4c88-ad56-0d8228135e14 · outbound

This paper cites Object Detection in Autonomous Vehicles: Status and Open Challenges.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Object Detection in Autonomous Vehicles: Status and Open Challenges

Reference 13

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Observation d53b9e39-d371-4871-a301-1d0566a14077 · outbound

This paper cites Enhancing object detection in self-driving cars using a hybrid approach.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Enhancing object detection in self-driving cars using a hybrid approach

Reference 14

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Observation de8bd3f1-4a88-4a5f-ac5f-f4f74a5f4eda · outbound

This paper cites Object scene flow for autonomous vehicles.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Object scene flow for autonomous vehicles

Reference 15

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Observation 9dd3894f-5922-42d8-8524-105661ef8760 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification The cityscapes dataset for semantic urban scene understanding

Reference 16

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Observation af851625-9894-4c98-b6c1-ec53701f2d6e · outbound

This paper cites Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness

Reference 17

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Observation 22d28ea7-f5e5-4c2d-998c-7ce28b6cc1d1 · outbound

This paper cites Towards Robust and Resilient Machine Learning.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Towards Robust and Resilient Machine Learning

Reference 18

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This paper cites Wichmann.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Wichmann

Reference 19

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Observation 0958e3d6-9f27-437d-af0f-4acde4cccb86 · outbound

This paper cites RobustBench: a standardized adversarial robustness benchmark.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification RobustBench: a standardized adversarial robustness benchmark

Reference 20

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This paper cites Neural architecture design and robustness: A dataset.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Neural architecture design and robustness: A dataset

Reference 21

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Observation 7e9623c2-deb8-426a-942a-8756c33661f1 · outbound

This paper cites Towards understanding adversarial robustness of optical flow networks.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Towards understanding adversarial robustness of optical flow networks

Reference 22

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This paper cites Improving feature stability during upsampling–spectral artifacts and the importance of spatial context.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Improving feature stability during upsampling–spectral artifacts and the importance of spatial context

Reference 23

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Observation 2be273cc-f67c-4253-96f2-8cdf9e3eefbb · outbound

This paper cites Differentiable Sensor Layouts for End-to-End Learning of Task-Specific Camera Parameters.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Differentiable Sensor Layouts for End-to-End Learning of Task-Specific Camera Parameters

Reference 24

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Observation bdd5ea9e-fc67-4fe2-8c34-2565fc6d067c · outbound

This paper cites Roll the dice: Monte carlo downsampling as a low-cost adversarial defence, 2024.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Roll the dice: Monte carlo downsampling as a low-cost adversarial defence, 2024

Reference 25

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Observation 6e127f55-3f1e-4eb9-b21a-ca0f42e8fb4a · outbound

This paper cites Improving native CNN robustness with filter frequency regularization.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Improving native CNN robustness with filter frequency regularization

Reference 26

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Observation 634a027d-4dd7-4d0c-9b90-2c68fabb50cc · outbound

This paper cites Frequencylowcut pooling- plug and play against catastrophic overfitting.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Frequencylowcut pooling- plug and play against catastrophic overfitting

Reference 27

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This paper cites How do training methods influence the utilization of vision models? InNeurIPS 2024 workshop on Interpretable AI: Past, Present and Future, 2024.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification How do training methods influence the utilization of vision models? InNeurIPS 2024 workshop on Interpretable AI: Past, Present and Future, 2024

Reference 28

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This paper cites MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark

Reference 29

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This paper cites MMDetection: Open MMLab Detection Toolbox and Benchmark.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification MMDetection: Open MMLab Detection Toolbox and Benchmark

Reference 30

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Observation b686cec1-b4fb-4df9-8165-fbf5f4edc568 · outbound

This paper cites Benchmarking the robustness of semantic segmen- tation models.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Benchmarking the robustness of semantic segmen- tation models

Reference 31

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This paper cites Robust object detection in challenging weather conditions.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Robust object detection in challenging weather conditions

Reference 32

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This paper cites Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

Reference 33

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This paper cites Robust object detection in extreme construction conditions.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Robust object detection in extreme construction conditions

Reference 34

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Observation f3f511e7-0a34-47ad-9ba0-fc8bd4bc48d0 · outbound

This paper cites On the robustness of semantic segmentation models to adversarial attacks.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification On the robustness of semantic segmentation models to adversarial attacks

Reference 35

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This paper cites Towards reliable evaluation and fast training of robust semantic segmentation models.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Towards reliable evaluation and fast training of robust semantic segmentation models

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This paper cites Frod: Robust object detection for free.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Frod: Robust object detection for free

Reference 37

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Observation 24d836bd-9fa1-44fc-a4c8-4f6fc4a34d3d · outbound

This paper cites Adversarially-aware robust object detector.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Adversarially-aware robust object detector

Reference 38

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This paper cites Adver- sarial examples for semantic segmentation and object detection.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Adver- sarial examples for semantic segmentation and object detection

Reference 39

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This paper cites Detection defenses: An empty promise against adversarial patch attacks on optical flow.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Detection defenses: An empty promise against adversarial patch attacks on optical flow

Reference 40

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This paper cites A perturbation-constrained adversarial attack for evaluating the robustness of optical flow.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification A perturbation-constrained adversarial attack for evaluating the robustness of optical flow

Reference 41

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This paper cites Distracting downpour: Adversarial weather attacks for motion estimation.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Distracting downpour: Adversarial weather attacks for motion estimation

Reference 42

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This paper cites Towards Class-wise Robustness Analysis.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Towards Class-wise Robustness Analysis

Reference 43

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This paper cites Explaining and harnessing adver- sarial examples.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Explaining and harnessing adver- sarial examples

Reference 44

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Observation 590680d2-e5f0-406e-ba5d-8bb9f0a663bf · outbound

This paper cites Adversarial examples in the physical world.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Adversarial examples in the physical world

Reference 45

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This paper cites CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks

Reference 46

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This paper cites Segpgd: An effective and efficient adversarial attack for evaluating and boosting segmentation robustness.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Segpgd: An effective and efficient adversarial attack for evaluating and boosting segmentation robustness

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This paper cites Transferable adversarial attacks for image and video object detection.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Transferable adversarial attacks for image and video object detection

Reference 48

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This paper cites Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshmi- narayanan.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshmi- narayanan

Reference 49

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This paper cites Towards improving robustness of compressed cnns.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Towards improving robustness of compressed cnns

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This paper cites RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo

Reference 51

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This paper cites ACDC: The adverse conditions dataset with correspondences for semantic driving scene understanding.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification ACDC: The adverse conditions dataset with correspondences for semantic driving scene understanding

Reference 52

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This paper cites RobustART: Benchmarking Robustness on Architecture Design and Training Techniques.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification RobustART: Benchmarking Robustness on Architecture Design and Training Techniques

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Observation d7db7d74-ba07-479d-8ea6-96ff0af513fa · outbound

This paper cites Intra-& extra-source exemplar- based style synthesis for improved domain generalization.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Intra-& extra-source exemplar- based style synthesis for improved domain generalization

Reference 54

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This paper cites Implicit representations for image segmentation.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Implicit representations for image segmentation

Reference 55

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This paper cites Implicit representations for constrained image segmentation.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Implicit representations for constrained image segmentation

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Observation 5a82fab6-3a69-454c-a0d2-63e7c47e2c90 · outbound

This paper cites Torchattacks: A PyTorch Repository for Adversarial Attacks.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Torchattacks: A PyTorch Repository for Adversarial Attacks

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Observation b1aa5f6d-603d-4645-96eb-1661c74f2755 · outbound

This paper cites Foolbox native: Fast adversarial attacks to benchmark the robustness of machine learning models in pytorch, tensorflow, and jax.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Foolbox native: Fast adversarial attacks to benchmark the robustness of machine learning models in pytorch, tensorflow, and jax

Reference 58

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Observation d0fad3f5-a8b1-4610-abb8-333aece3a310 · outbound

This paper cites Segmentmeifyoucan: A benchmark for anomaly segmentation.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Segmentmeifyoucan: A benchmark for anomaly segmentation

Reference 59

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Observation e457b9e5-c4e9-4189-823a-131e8e0b5297 · outbound

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SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification DispBench

Reference 60

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SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Pyramid scene parsing network

Reference 61

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This paper cites Improving 2d feature representations by 3d-aware fine-tuning.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Improving 2d feature representations by 3d-aware fine-tuning

Reference 62

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Observation 6e3132c3-f3d6-4126-9de7-be34948e32ba · outbound

This paper cites Mta-clip: Language-guided semantic segmentation with mask-text alignment.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Mta-clip: Language-guided semantic segmentation with mask-text alignment

Reference 63

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Observation 9a0432d2-93ef-43ef-a97a-894aaa1d7a06 · outbound

This paper cites Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Reference 64

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This paper cites Focal Loss for Dense Object Detection.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Focal Loss for Dense Object Detection

Reference 65

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Observation d5a243ba-f18a-43b0-90bb-99f585d0d32a · outbound

This paper cites Mask r-cnn.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Mask r-cnn

Reference 66

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This paper cites Cascade r-cnn: High quality object detection and instance segmentation.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Cascade r-cnn: High quality object detection and instance segmentation

Reference 67

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Observation 9e4a87d8-cdff-44b6-9bbf-2ccfebc29cda · outbound

This paper cites Dn-detr: Accelerate detr training by introducing query denoising.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Dn-detr: Accelerate detr training by introducing query denoising

Reference 68

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This paper cites Fooling the eyes of autonomous vehicles: Robust physical adversarial examples against traffic sign recognition systems.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Fooling the eyes of autonomous vehicles: Robust physical adversarial examples against traffic sign recognition systems

Reference 69

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Observation c1e3ae68-713b-4920-ae43-0c0078071a78 · outbound

This paper cites Context-aware transfer attacks for object detection.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Context-aware transfer attacks for object detection

Reference 70

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Observation acdfe487-6bf4-4ae3-94c7-022ffe9c5aa3 · outbound

This paper cites T-sea: Transfer- based self-ensemble attack on object detection.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification T-sea: Transfer- based self-ensemble attack on object detection

Reference 71

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Observation e9083f94-53be-427f-aa92-634c907417a9 · outbound

This paper cites Towards deep learning models resistant to adversarial attacks, 2017.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Towards deep learning models resistant to adversarial attacks, 2017

Reference 72

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This paper cites Fast is better than free: Revisiting adversarial training.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Fast is better than free: Revisiting adversarial training

Reference 73

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This paper cites On the unreasonable vulnerability of transformers for image restoration-and an easy fix.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification On the unreasonable vulnerability of transformers for image restoration-and an easy fix

Reference 74

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Observation e4c5eabe-d421-4ff6-ae5a-7d978525fcbb · outbound

This paper cites Sp2 net for generalized zero-label semantic segmentation.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Sp2 net for generalized zero-label semantic segmentation

Reference 75

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Observation 163c9f05-b3e2-48bb-9092-0f807224b73f · outbound

This paper cites Dynamic divide-and-conquer adversarial train- ing for robust semantic segmentation.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Dynamic divide-and-conquer adversarial train- ing for robust semantic segmentation

Reference 76

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This paper cites Rethinking atrous convolution for semantic image segmentation, 2017.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Rethinking atrous convolution for semantic image segmentation, 2017

Reference 77

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This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers.Advances in neural information processing systems, 34:12077–12090, 2021.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Segformer: Simple and efficient design for semantic segmentation with transformers.Advances in neural information processing systems, 34:12077–12090, 2021

Reference 78

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This paper cites Internimage: Exploring large-scale vision foundation models with deformable convolutions.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Internimage: Exploring large-scale vision foundation models with deformable convolutions

Reference 79

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This paper cites Deep residual learning for image recognition.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Deep residual learning for image recognition

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:41:34.804442Z

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Observation d4c700c3-723b-490e-bb3f-fef2a4e373d8 · outbound

This paper cites A convnet for the 2020s.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification A convnet for the 2020s

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:41:34.618743Z

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This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Swin transformer: Hierarchical vision transformer using shifted windows

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:41:34.406542Z

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This paper cites Vision transformers are robust learners.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Vision transformers are robust learners

Reference 83

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Observation a3ac68a5-f58c-4001-bcd2-718001ab2aa0 · outbound

This paper cites Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation

Reference 84

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This paper cites DETRs with collaborative hybrid assignments training.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification DETRs with collaborative hybrid assignments training

Reference 85

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Observation 466a74c5-2d82-4148-b4c7-54d6cbf15fa1 · outbound

This paper cites Dense distinct query for end-to-end object detection.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Dense distinct query for end-to-end object detection

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:41:33.327341Z

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Observation 3bf51264-451b-45f5-8f33-0afe7e594b1f · outbound

This paper cites Grounded language-image pre-training.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Grounded language-image pre-training

Reference 87

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This paper cites Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:41:33.048145Z

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Observation 4b86998f-e0b9-4839-8036-bb0f69105d49 · outbound

This paper cites An Open and Comprehensive Pipeline for Unified Object Grounding and Detection.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification An Open and Comprehensive Pipeline for Unified Object Grounding and Detection

Reference 89

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Observation bc9f2947-54d4-45aa-9e0b-b6fcaee9c75f · outbound

This paper cites Emerging properties in self-supervised vision transformers.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Emerging properties in self-supervised vision transformers

Reference 90

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This paper cites RTMDet: An Empirical Study of Designing Real-Time Object Detectors.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification RTMDet: An Empirical Study of Designing Real-Time Object Detectors

Reference 91

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Observation c6e70428-66ff-468b-a1d6-12ccbfef8c26 · outbound

This paper cites Aggregated residual transformations for deep neural networks.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Aggregated residual transformations for deep neural networks

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:41:32.728530Z

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Observation 331f518b-459e-42a2-a4d1-c76c3def1bdc · outbound

This paper cites Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks

Reference 93

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This paper cites Classification robustness to common optical aberrations.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Classification robustness to common optical aberrations

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:41:32.415074Z

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Observation f58d3074-e272-4aa4-9309-ea35c6b225f6 · outbound

This paper cites Robustifying token attention for vision transformers.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Robustifying token attention for vision transformers

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:41:32.124551Z

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Observation 0bcd98c5-c602-4772-8cfd-b1e79f2fbc90 · outbound

This paper cites Are Synthetic Corruptions A Reliable Proxy For Real-World Corruptions? In CVPR Workshop On Synthetic Data for Computer Vision, 2025.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Are Synthetic Corruptions A Reliable Proxy For Real-World Corruptions? In CVPR Workshop On Synthetic Data for Computer Vision, 2025

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:41:31.811424Z

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Observation 77a525e2-659e-4aa2-b7c2-f6b220477f46 · outbound

This paper cites Urban scene semantic segmentation with low-cost coarse annotation.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Urban scene semantic segmentation with low-cost coarse annotation

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:41:31.474819Z

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Observation 3bf4ba35-5eb4-4535-a36e-0147528b1caf · outbound

This paper cites Everingham, L.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Everingham, L

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:41:31.201761Z

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Observation e529ea68-e020-4802-b1b1-ad458eb1c30f · outbound

This paper cites Center- net: Keypoint triplets for object detection.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Center- net: Keypoint triplets for object detection

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:41:30.964631Z

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Observation c7ef2f32-db90-42ed-8b3a-1848a9fe8d21 · outbound

This paper cites Tood: Task- aligned one-stage object detection.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Tood: Task- aligned one-stage object detection

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:41:30.673862Z

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