A Generative Victim Model for Segmentation | Paper Citation Record

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

A Generative Victim Model for Segmentation Segpgd: An effective and efficient adversarial attack for evaluating and boosting segmentation robustness,

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This paper cites Explaining and harnessing adversarial examples,.

A Generative Victim Model for Segmentation Explaining and harnessing adversarial examples,

Reference 2

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Observation 8e40a2bd-0dd6-4f12-a08d-d9c53cddab23 · outbound

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

A Generative Victim Model for Segmentation On the robustness of semantic segmentation models to adversarial attacks,

Reference 3

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

A Generative Victim Model for Segmentation Towards deep learning models resistant to adversarial attacks,

Reference 4

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This paper cites Practical black-box attacks against machine learning,.

A Generative Victim Model for Segmentation Practical black-box attacks against machine learning,

Reference 5

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This paper cites Dast: Data-free substitute training for adversarial attacks,.

A Generative Victim Model for Segmentation Dast: Data-free substitute training for adversarial attacks,

Reference 6

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This paper cites Square attack: a query-efficient black-box adversarial attack via random search,.

A Generative Victim Model for Segmentation Square attack: a query-efficient black-box adversarial attack via random search,

Reference 7

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This paper cites Simple black-box adversarial attacks,.

A Generative Victim Model for Segmentation Simple black-box adversarial attacks,

Reference 8

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This paper cites Sign bits are all you need for black- box attacks,.

A Generative Victim Model for Segmentation Sign bits are all you need for black- box attacks,

Reference 9

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This paper cites Decision-based adversarial attacks: Reliable attacks against black-box machine learning models,.

A Generative Victim Model for Segmentation Decision-based adversarial attacks: Reliable attacks against black-box machine learning models,

Reference 10

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Observation 7fd2032e-a8a9-45a3-92c9-53c72846246b · outbound

This paper cites Black-box adversarial at- tacks with limited queries and information,.

A Generative Victim Model for Segmentation Black-box adversarial at- tacks with limited queries and information,

Reference 11

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This paper cites Adversarial risk and the dangers of evaluating against weak attacks,.

A Generative Victim Model for Segmentation Adversarial risk and the dangers of evaluating against weak attacks,

Reference 12

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Observation c5f87bfa-37d8-4e5a-8eb3-b4ab8f42d1b8 · outbound

This paper cites Adversarial examples for semantic segmentation and object detection,.

A Generative Victim Model for Segmentation Adversarial examples for semantic segmentation and object detection,

Reference 13

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Observation 09d7feb2-4545-4169-af43-b3c02556af94 · outbound

This paper cites Skip connec- tions matter: On the transferability of adversarial examples generated with resnets,.

A Generative Victim Model for Segmentation Skip connec- tions matter: On the transferability of adversarial examples generated with resnets,

Reference 14

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Observation 0b09827b-b3be-42bd-8573-2bc0898abc7a · outbound

This paper cites Gen- erating transferable adversarial examples against vision transformers,.

A Generative Victim Model for Segmentation Gen- erating transferable adversarial examples against vision transformers,

Reference 15

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Observation 17f6becb-d5c2-4d70-86c3-30317080ff40 · outbound

This paper cites Diverse generative perturbations on attention space for transferable adversarial attacks,.

A Generative Victim Model for Segmentation Diverse generative perturbations on attention space for transferable adversarial attacks,

Reference 16

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Observation a77e96a1-3ae1-4f65-a8b2-f51a7ed84eaf · outbound

This paper cites Transferable adversarial attacks on vision transformers with token gradient regularization,.

A Generative Victim Model for Segmentation Transferable adversarial attacks on vision transformers with token gradient regularization,

Reference 17

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This paper cites Transferable adversarial attack for both vision transformers and convolutional networks via momentum integrated gradients,.

A Generative Victim Model for Segmentation Transferable adversarial attack for both vision transformers and convolutional networks via momentum integrated gradients,

Reference 18

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Observation 05c9f60c-39f0-43ea-8eb5-1122d8529c7b · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics,.

A Generative Victim Model for Segmentation Deep unsupervised learning using nonequilibrium thermodynamics,

Reference 19

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This paper cites Denoising diffusion probabilistic models,.

A Generative Victim Model for Segmentation Denoising diffusion probabilistic models,

Reference 20

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This paper cites Generative modeling by estimating gradients of the data distribution,.

A Generative Victim Model for Segmentation Generative modeling by estimating gradients of the data distribution,

Reference 21

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This paper cites Rethinking adversarial transferability from a data distribution perspective,.

A Generative Victim Model for Segmentation Rethinking adversarial transferability from a data distribution perspective,

Reference 22

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This paper cites Advdiffuser: Natural adversarial example synthesis with diffusion models,.

A Generative Victim Model for Segmentation Advdiffuser: Natural adversarial example synthesis with diffusion models,

Reference 23

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This paper cites Revisiting graph adversarial attack and defense from a data distribution perspective,.

A Generative Victim Model for Segmentation Revisiting graph adversarial attack and defense from a data distribution perspective,

Reference 24

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Observation d99a08e7-d0ac-4c09-b3c3-08b9539482b1 · outbound

This paper cites Robust evaluation of diffusion-based adversarial purification,.

A Generative Victim Model for Segmentation Robust evaluation of diffusion-based adversarial purification,

Reference 25

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Observation ffd7a169-c0e3-4279-8340-61f9b24209da · outbound

This paper cites Diffusion models for adversarial purification,.

A Generative Victim Model for Segmentation Diffusion models for adversarial purification,

Reference 26

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Observation 85180841-b7cd-4a4c-83c8-8b6c7def1f34 · outbound

This paper cites Guided Diffusion Model for Adversarial Purification.

A Generative Victim Model for Segmentation Guided Diffusion Model for Adversarial Purification

Reference 27

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This paper cites Grad-cam: Visual explanations from deep networks via 11 gradient-based localization,.

A Generative Victim Model for Segmentation Grad-cam: Visual explanations from deep networks via 11 gradient-based localization,

Reference 28

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This paper cites Intriguing properties of neural networks,.

A Generative Victim Model for Segmentation Intriguing properties of neural networks,

Reference 29

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This paper cites Towards evaluating the robustness of neural networks,.

A Generative Victim Model for Segmentation Towards evaluating the robustness of neural networks,

Reference 30

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Observation fb38a8c6-ad04-40d4-8334-d2be1978ddcf · outbound

This paper cites Impact of adversarial examples on deep learning models for biomedical image segmentation,.

A Generative Victim Model for Segmentation Impact of adversarial examples on deep learning models for biomedical image segmentation,

Reference 31

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This paper cites Fashion-guided adversarial attack on person segmentation,.

A Generative Victim Model for Segmentation Fashion-guided adversarial attack on person segmentation,

Reference 32

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This paper cites Universal adversarial perturbations against semantic image segmenta- tion,.

A Generative Victim Model for Segmentation Universal adversarial perturbations against semantic image segmenta- tion,

Reference 33

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Observation 7d981086-e283-4be4-8dfb-98cda3d52bfd · outbound

This paper cites Data-free universal adversarial perturbation and black-box attack,.

A Generative Victim Model for Segmentation Data-free universal adversarial perturbation and black-box attack,

Reference 34

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Observation caad3714-23b0-4cd3-911d-5fd1052712f9 · outbound

This paper cites Query-based black-box attack against medical image segmentation model,.

A Generative Victim Model for Segmentation Query-based black-box attack against medical image segmentation model,

Reference 35

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This paper cites Surfree: a fast surrogate- free black-box attack,.

A Generative Victim Model for Segmentation Surfree: a fast surrogate- free black-box attack,

Reference 36

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This paper cites Simulating unknown target models for query-efficient black-box attacks,.

A Generative Victim Model for Segmentation Simulating unknown target models for query-efficient black-box attacks,

Reference 37

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This paper cites Diversity can be transferred: Output diversification for white-and black-box attacks,.

A Generative Victim Model for Segmentation Diversity can be transferred: Output diversification for white-and black-box attacks,

Reference 38

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Observation f129f5d5-8f42-4460-a153-9bdf19183adc · outbound

This paper cites Geoda: a geometric framework for black-box adversarial attacks,.

A Generative Victim Model for Segmentation Geoda: a geometric framework for black-box adversarial attacks,

Reference 39

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Observation 10671a85-89d2-4488-86d6-aa2b92621920 · outbound

This paper cites Boosting black-box attack with partially transferred conditional adversarial distribution,.

A Generative Victim Model for Segmentation Boosting black-box attack with partially transferred conditional adversarial distribution,

Reference 40

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Observation 047bd209-0de2-4945-96df-48869570969a · outbound

This paper cites Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models,.

A Generative Victim Model for Segmentation Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models,

Reference 41

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Observation e92837dd-6aec-4dc4-bf20-3ac48e32977a · outbound

This paper cites Parallel rectangle flip attack: A query-based black-box attack against object detection,.

A Generative Victim Model for Segmentation Parallel rectangle flip attack: A query-based black-box attack against object detection,

Reference 42

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Observation e74ecff1-95d8-47e9-b4b5-bc4710b7b680 · outbound

This paper cites Boost- ing the transferability of adversarial attacks with reverse adversarial perturbation,.

A Generative Victim Model for Segmentation Boost- ing the transferability of adversarial attacks with reverse adversarial perturbation,

Reference 43

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This paper cites Frequency domain model augmentation for adversarial attack,.

A Generative Victim Model for Segmentation Frequency domain model augmentation for adversarial attack,

Reference 44

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Observation dc6a18d6-07d9-4c3c-932a-383d1f777853 · outbound

This paper cites Transferable adversarial perturbations,.

A Generative Victim Model for Segmentation Transferable adversarial perturbations,

Reference 45

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Observation c3be5989-285c-4703-b162-57b90c0f80b2 · outbound

This paper cites Fda: Feature disruptive attack,.

A Generative Victim Model for Segmentation Fda: Feature disruptive attack,

Reference 46

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Observation 3b747e99-b03f-4841-9ccb-ac918779e6f5 · outbound

This paper cites Feature importance-aware transferable adversarial attacks,.

A Generative Victim Model for Segmentation Feature importance-aware transferable adversarial attacks,

Reference 47

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Observation ded3ae18-6e85-4339-b7ad-45ecdc52a780 · outbound

This paper cites Boosting the transferability of adversarial samples via attention,.

A Generative Victim Model for Segmentation Boosting the transferability of adversarial samples via attention,

Reference 48

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Observation c8d23e42-bd5b-4527-b45c-cb05f05063ea · outbound

This paper cites Nesterov accelerated gradient and scale invariance for adversarial attacks,.

A Generative Victim Model for Segmentation Nesterov accelerated gradient and scale invariance for adversarial attacks,

Reference 49

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This paper cites Enhancing the transferability of adversarial attacks through variance tuning,.

A Generative Victim Model for Segmentation Enhancing the transferability of adversarial attacks through variance tuning,

Reference 50

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Observation a2d88488-4c3e-4e66-b8e1-66c0eb65bc7e · outbound

This paper cites Stochastic variance reduced ensemble adversarial attack for boosting the adver- sarial transferability,.

A Generative Victim Model for Segmentation Stochastic variance reduced ensemble adversarial attack for boosting the adver- sarial transferability,

Reference 51

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ba8df23d-4934-44ca-85d2-febe4f2042c4 · outbound

This paper cites Toward understanding and boosting adversarial trans- ferability from a distribution perspective,.

A Generative Victim Model for Segmentation Toward understanding and boosting adversarial trans- ferability from a distribution perspective,

Reference 52

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9f719f52-03db-4f96-b9a2-20f6b38e268e · outbound

This paper cites Admix: Enhancing the transfer- ability of adversarial attacks,.

A Generative Victim Model for Segmentation Admix: Enhancing the transfer- ability of adversarial attacks,

Reference 53

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Source-reported events for the cited work

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Observation 3c986c88-bacd-482f-94e7-a5160125da98 · outbound

This paper cites Evading defenses to transferable adversarial examples by translation-invariant attacks,.

A Generative Victim Model for Segmentation Evading defenses to transferable adversarial examples by translation-invariant attacks,

Reference 54

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Observation 8c146337-8534-4da4-99c4-ea05d12ed033 · outbound

This paper cites Improving the transferability of adversarial samples with adversarial transformations,.

A Generative Victim Model for Segmentation Improving the transferability of adversarial samples with adversarial transformations,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.583517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0625bc95-e130-497e-9970-6bcf0029324f · outbound

This paper cites Diffusion-based adversarial sample generation for improved stealthiness and controllability,.

A Generative Victim Model for Segmentation Diffusion-based adversarial sample generation for improved stealthiness and controllability,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.567519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b8ce2616-bd93-44ee-9af6-93252da9db0f · outbound

This paper cites Diffusion Models for Imperceptible and Transferable Adversarial Attack.

A Generative Victim Model for Segmentation Diffusion Models for Imperceptible and Transferable Adversarial Attack

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T19:01:26.837294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b5057aa-ebcf-42ac-b8c0-3d182b00920f · outbound

This paper cites Classifier-free diffusion guidance,.

A Generative Victim Model for Segmentation Classifier-free diffusion guidance,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.550615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 38c2adc0-6ce1-4a80-8ccb-31808fa819f7 · outbound

This paper cites Auto-encoding variational bayes,.

A Generative Victim Model for Segmentation Auto-encoding variational bayes,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.534194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 273176bf-d445-414e-b3c1-beff570f974a · outbound

This paper cites Variational inference with normalizing flows,.

A Generative Victim Model for Segmentation Variational inference with normalizing flows,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.519257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0cd274dc-5873-4298-9ebb-daf8247f38a3 · outbound

This paper cites Denoising Diffusion Implicit Models.

A Generative Victim Model for Segmentation Denoising Diffusion Implicit Models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T19:01:26.856514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eef3de05-6dc8-437b-94c0-2ca25a555a30 · outbound

This paper cites Camouflaged object detection,.

A Generative Victim Model for Segmentation Camouflaged object detection,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.503280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cd065586-5e4f-4387-aca1-78790a7694ce · outbound

This paper cites Simultaneously localize, segment and rank the camouflaged objects,.

A Generative Victim Model for Segmentation Simultaneously localize, segment and rank the camouflaged objects,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.486392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cc0cc517-1de6-4877-af7a-bff5fae4a526 · outbound

This paper cites The pascal visual object classes (voc) challenge,.

A Generative Victim Model for Segmentation The pascal visual object classes (voc) challenge,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-11T19:01:26.870802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d6bf4b8-376c-446c-a051-7b3376dbd999 · outbound

This paper cites Vision transformers for dense prediction,.

A Generative Victim Model for Segmentation Vision transformers for dense prediction,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.457208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f3151ea2-9bac-43ab-a0b0-d3ecc3ad7ae2 · outbound

This paper cites Pvt v2: Improved baselines with pyramid vision transformer,.

A Generative Victim Model for Segmentation Pvt v2: Improved baselines with pyramid vision transformer,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-11T19:01:26.879945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc6ea28f-8763-4279-98fd-c9ea64253200 · outbound

This paper cites Deep residual learning for image recognition,.

A Generative Victim Model for Segmentation Deep residual learning for image recognition,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.427972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2b5c4ff1-1abd-4e76-b853-500f355789d0 · outbound

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

A Generative Victim Model for Segmentation Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.412176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 72837b38-c585-48b1-9a59-36d2a604b75f · outbound

This paper cites Very deep convolutional networks for large-scale image recognition,.

A Generative Victim Model for Segmentation Very deep convolutional networks for large-scale image recognition,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.396532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 82202fd0-3aa8-4320-a140-eb95f5efeb57 · outbound

This paper cites Generative Transformer for Accurate and Reliable Salient Object Detection.

A Generative Victim Model for Segmentation Generative Transformer for Accurate and Reliable Salient Object Detection

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-08-11T19:01:27.075672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3c7f8533-a193-447f-8412-91d65c81e96f · outbound

This paper cites Encoder- decoder with atrous separable convolution for semantic image segmen- tation,.

A Generative Victim Model for Segmentation Encoder- decoder with atrous separable convolution for semantic image segmen- tation,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.378338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7e19158a-2e79-4438-8b22-651b33cc6ebd · outbound

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

A Generative Victim Model for Segmentation MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-11T19:01:26.907749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa73ccc1-9eea-4d94-af7f-4434ae1de5c0 · outbound

This paper cites Pyramid scene parsing network,.

A Generative Victim Model for Segmentation Pyramid scene parsing network,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.362058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dc56803e-c63d-4780-8ca4-564ea6a027fa · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

A Generative Victim Model for Segmentation Fully convolutional networks for semantic segmentation,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.346225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6d409f21-726e-45c3-9ae5-45bdac09558d · outbound

This paper cites Boosting adversarial attacks with momentum,.

A Generative Victim Model for Segmentation Boosting adversarial attacks with momentum,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.330342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 86d86bce-c03c-470c-8400-3ae9004f0980 · outbound

This paper cites Improving transferability of adversarial examples with input diversity,.

A Generative Victim Model for Segmentation Improving transferability of adversarial examples with input diversity,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.315430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1336eeea-86bb-4f89-9ee1-3e7d0510e873 · outbound

This paper cites Enhancing adversarial example transferability with an intermediate level attack,.

A Generative Victim Model for Segmentation Enhancing adversarial example transferability with an intermediate level attack,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.299552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7dbfc57b-cb71-430c-ab5e-9e3d09de1ff8 · outbound

This paper cites Improving adversarial transferability via neuron attribution-based attacks,.

A Generative Victim Model for Segmentation Improving adversarial transferability via neuron attribution-based attacks,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.283161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 91662e94-c0e0-419b-834d-01ff50999721 · outbound

This paper cites Enhanced-alignment measure for binary foreground map evaluation,.

A Generative Victim Model for Segmentation Enhanced-alignment measure for binary foreground map evaluation,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.267365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7cc15fa6-1433-4c3f-8b06-f144c911e604 · outbound

This paper cites Structure-measure: A new way to evaluate foreground maps,.

A Generative Victim Model for Segmentation Structure-measure: A new way to evaluate foreground maps,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.252226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 72a71ebe-0441-43b4-8008-ad8add7dc3a5 · outbound

This paper cites Adversarial Examples on Segmentation Models Can be Easy to Transfer.

A Generative Victim Model for Segmentation Adversarial Examples on Segmentation Models Can be Easy to Transfer

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-11T19:01:26.947135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a0e08ad-1d7f-4c7c-8bab-e32820e69eba · outbound

This paper cites Prior convictions: Black-box adversarial attacks with bandits and priors,.

A Generative Victim Model for Segmentation Prior convictions: Black-box adversarial attacks with bandits and priors,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.237092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 452cdd27-0bcf-44ca-b493-4cff1687058a · outbound

This paper cites Ensemble Adversarial Training: Attacks and Defenses.

A Generative Victim Model for Segmentation Ensemble Adversarial Training: Attacks and Defenses

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-11T19:01:26.956059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5188ed90-d0fa-42fe-853a-5b8a81c63a2e · outbound

This paper cites Learning to detect salient objects with image-level supervision,.

A Generative Victim Model for Segmentation Learning to detect salient objects with image-level supervision,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.218139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a3b2c955-f6f7-4ee4-939b-0ce4ea602c2e · outbound

This paper cites Design and perceptual validation of per- formance measures for salient object segmentation,.

A Generative Victim Model for Segmentation Design and perceptual validation of per- formance measures for salient object segmentation,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.200471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c1c0e4cc-2fd7-49fd-9a8c-18ab34918721 · outbound

This paper cites Saliency detection via graph-based manifold ranking,.

A Generative Victim Model for Segmentation Saliency detection via graph-based manifold ranking,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.183729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3bd380fe-9fb2-4024-b431-db41b13d8243 · outbound

This paper cites Hierarchical saliency detection,.

A Generative Victim Model for Segmentation Hierarchical saliency detection,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.166796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 947e175a-286d-495d-a1aa-9f8a6224ba6e · outbound

This paper cites The secrets of salient object segmentation,.

A Generative Victim Model for Segmentation The secrets of salient object segmentation,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.151487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c5047211-997f-4d0e-bf2a-eb0edca870f1 · outbound

This paper cites Salient objects in clutter: Bringing salient object detection to the foreground,.

A Generative Victim Model for Segmentation Salient objects in clutter: Bringing salient object detection to the foreground,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:27.136335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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