SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge | Paper Citation Record

Observation 379a51de-8651-4d66-b802-996e20cfcbbf · outbound

This paper cites MoSFormer: Augmenting Temporal Context with Memory of Surgery for Surgical Phase Recognition.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge MoSFormer: Augmenting Temporal Context with Memory of Surgery for Surgical Phase Recognition

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:45:50.773106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ad934906-4e63-4e51-9e80-70f67be378ab · outbound

This paper cites Operating Room Workflow Analysis via Reasoning Segmentation over Digital Twins.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Operating Room Workflow Analysis via Reasoning Segmentation over Digital Twins

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:45:50.814285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3a8a5f29-b529-426e-953f-5c2293592d20 · outbound

This paper cites Neural Finite-State Machines for Surgical Phase Recognition.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Neural Finite-State Machines for Surgical Phase Recognition

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:45:50.849441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d5fe0bb1-3ca2-47ae-ace8-b3d0daa9c998 · outbound

This paper cites Towards Robust Algorithms for Surgical Phase Recognition via Digital Twin Representation.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Towards Robust Algorithms for Surgical Phase Recognition via Digital Twin Representation

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:45:50.819742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9665260c-d167-4a94-8d59-fffb580f1b90 · outbound

This paper cites In: 2020 25th International Conference on Pattern Recogni- tion (ICPR), pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: 2020 25th International Conference on Pattern Recogni- tion (ICPR), pp

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.456919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation acf7a6f8-55b0-41f3-be86-b92c519fee39 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.452924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 80c83560-b9d4-48e2-a249-bd5bc88e78ae · outbound

This paper cites Scientific reports11(1), 5197 (2021).

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Scientific reports11(1), 5197 (2021)

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.443697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8bc8c2e3-05d8-49a0-86fe-bbc0f688eacd · outbound

This paper cites Healthcare Technology Letters12(1), 12119 (2025).

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Healthcare Technology Letters12(1), 12119 (2025)

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.467281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e0d23d2b-33af-467f-af7e-9ff3d19f27d1 · outbound

This paper cites International journal of computer assisted radiology and surgery19(6), 1213–1222 (2024).

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge International journal of computer assisted radiology and surgery19(6), 1213–1222 (2024)

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.536139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 01ef3a2e-e552-4dcf-b074-0e806ff7ef18 · outbound

This paper cites Healthcare Technology Letters 11(6), 355–364 (2024).

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Healthcare Technology Letters 11(6), 355–364 (2024)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.493413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 42fe5d0b-d2fa-4a28-951c-10afca2b3c46 · outbound

This paper cites International Journal of Computer Assisted Radiology and Surgery 19(7), 1301–1312 (2024).

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge International Journal of Computer Assisted Radiology and Surgery 19(7), 1301–1312 (2024)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.555261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 36ee2b14-6d26-4035-8246-2616721a984f · outbound

This paper cites Inter- national Journal of Computer Assisted Radiology and Surgery18(7), 1235–1243 (2023).

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Inter- national Journal of Computer Assisted Radiology and Surgery18(7), 1235–1243 (2023)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.559256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 43f01d44-5a2e-4de5-bd4e-f9f3d8c082b0 · outbound

This paper cites Towards Robust Surgical Automation via Digital Twin Representations from Foundation Models.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Towards Robust Surgical Automation via Digital Twin Representations from Foundation Models

Reference 13

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T22:45:50.783609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8319d3ec-f5a1-485b-a70d-8b7cbe06ceaf · outbound

This paper cites Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-05-23T22:45:50.865763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 576649be-f89c-4077-ba4f-7b6a011e10e9 · outbound

This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.436393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bd2e3003-7323-4bf1-94e6-6653c88e28da · outbound

This paper cites In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.609051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ae85234f-e407-4c56-b280-f89af78257cf · outbound

This paper cites In: Pro- ceedings of the European Conference on Computer Vision (ECCV), pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: Pro- ceedings of the European Conference on Computer Vision (ECCV), pp

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.500744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ab4661b0-338b-4050-88aa-5fdc79bfc6cc · outbound

This paper cites IEEE Robotics and Automation Letters 7(2), 3858–3865 (2022).

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge IEEE Robotics and Automation Letters 7(2), 3858–3865 (2022)

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.482507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e4c50786-003a-4716-b75a-3e7af588666c · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.448576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 60d66422-fddf-455d-ba2e-f90401c10d52 · outbound

This paper cites Advances in Neural Information Processing Systems34, 12077–12090 (2021).

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Advances in Neural Information Processing Systems34, 12077–12090 (2021)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.486608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5c2b9b9a-f577-47af-91a1-d29fe8635929 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.504936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ec1320c6-709b-458f-a398-98e3ef338f37 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.508859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5c30a986-7a29-4572-80a2-74788cffbbe7 · outbound

This paper cites 2018 Robotic Scene Segmentation Challenge.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge 2018 Robotic Scene Segmentation Challenge

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:45:50.788411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c1529776-b030-48a5-874e-2f991d539a11 · outbound

This paper cites 2017 Robotic Instrument Segmentation Challenge.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge 2017 Robotic Instrument Segmentation Challenge

Reference 25

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T22:45:50.870783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6bf3fa92-4ecf-459e-b793-e0d614397fac · outbound

This paper cites International journal of computer vision 88, 303–338 (2010) 27.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge International journal of computer vision 88, 303–338 (2010) 27

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.623805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5cb08043-5795-4189-a67e-0c58767377e3 · outbound

This paper cites International Journal of Computer Vision127(3), 302–321 (2019).

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge International Journal of Computer Vision127(3), 302–321 (2019)

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.612753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 79b15bf4-0e7d-4a6e-9806-d52a6f6aff0d · outbound

This paper cites In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.616620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c24ac485-d681-4290-8a3e-3b47dcea4466 · outbound

This paper cites A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:45:50.834657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 79a31a6a-c41f-40ce-ba39-ca5fc5692f9d · outbound

This paper cites Dual Invariance Self-training for Reliable Semi-supervised Surgical Phase Recognition.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Dual Invariance Self-training for Reliable Semi-supervised Surgical Phase Recognition

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:45:50.824897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 95297d59-a406-4fb9-b06e-6f771f130bb3 · outbound

This paper cites Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Reference 31

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T22:45:50.830202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 71981665-04a6-44ae-a04c-d2b4063aa1ea · outbound

This paper cites Causality-Driven Audits of Model Robustness.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Causality-Driven Audits of Model Robustness

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:45:50.882985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 30ac60a8-3578-4f14-923b-a7aabd561460 · outbound

This paper cites Detecting Dataset Bias in Medical AI: A Generalized and Modality-Agnostic Auditing Framework.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Detecting Dataset Bias in Medical AI: A Generalized and Modality-Agnostic Auditing Framework

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:45:50.804137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 34ad07d4-fbbd-4b47-b793-c0400e6daff9 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.601044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 32b995ce-5770-47e1-9948-b6584b70355f · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge SAM 2: Segment Anything in Images and Videos

Reference 35

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T22:45:50.839505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9552bf6d-ffd0-4df9-a194-6e4118fa44a4 · outbound

This paper cites Performance and Non-adversarial Robustness of the Segment Anything Model 2 in Surgical Video Segmentation.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Performance and Non-adversarial Robustness of the Segment Anything Model 2 in Surgical Video Segmentation

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:45:50.799162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 520945db-bcc1-460d-bf71-1233b53450c4 · outbound

This paper cites Biomimetics 7(2), 68 (2022).

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Biomimetics 7(2), 68 (2022)

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.596885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ed970934-884b-4de6-b07c-97c5508fc041 · outbound

This paper cites International Journal of Computer Assisted Radiology and Surgery18(5), 939–944 (2023) 28.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge International Journal of Computer Assisted Radiology and Surgery18(5), 939–944 (2023) 28

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.605220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0950edcf-2caf-4e03-9040-587c053154cd · outbound

This paper cites In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.620251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5a0d90cb-e2f9-423d-b26e-73dd9a7d936a · outbound

This paper cites In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.568125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 537e3812-2a94-420a-9dd4-03043a47395c · outbound

This paper cites International Journal of Computer Assisted Radiology and Surgery18(6), 1009–1016 (2023).

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge International Journal of Computer Assisted Radiology and Surgery18(6), 1009–1016 (2023)

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.581129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f169e2b8-5c81-4fbe-ac2d-60900961f162 · outbound

This paper cites Online Reasoning Video Segmentation with Just-in-Time Digital Twins.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Online Reasoning Video Segmentation with Just-in-Time Digital Twins

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:45:50.860205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e4cef380-3310-41f2-8398-a65cf7615acb · outbound

This paper cites In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.563635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6b4a7c79-13a4-43d3-885e-13efc95b8af4 · outbound

This paper cites Segment Anything.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Segment Anything

Reference 44

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T22:45:50.843927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 35c53731-4a73-4ec3-8d8a-abe09c561ca0 · outbound

This paper cites Video-based surveillance systems: Computer vision and distributed processing, 135–144 (2002).

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Video-based surveillance systems: Computer vision and distributed processing, 135–144 (2002)

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.543866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ac87b76b-d8c3-4233-846c-74160c6614ac · outbound

This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.551519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9b2b4dfc-18a2-46b3-9ddc-8988d095d6ef · outbound

This paper cites Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:45:50.877561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bb40b631-3476-4b03-a2a6-9c3e0dde5334 · outbound

This paper cites DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:45:50.762146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b2568b3f-5378-4913-87bb-3ef438596b0a · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.539722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2719a731-17f8-492c-8170-d6b6f9ca98fe · outbound

This paper cites In: 2021 International Conference on 3D Vision (3DV), pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: 2021 International Conference on 3D Vision (3DV), pp

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.547569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0c5b6f11-f9b6-4a4f-978f-18717c8bd5f5 · outbound

This paper cites 18963–18974 (2022).

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge 18963–18974 (2022)

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.576460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 26363200-b743-4a40-8a0d-ace485e43727 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 52

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T22:45:50.808965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e8a6f4a3-17d0-4528-8ae2-bd899a0b8dd6 · outbound

This paper cites an unresolved cited work.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-05-23T22:45:51.431889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b3599be3-368a-45e7-abd1-44fda2baa5ce · outbound

This paper cites U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 54

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T22:45:50.854608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 168b6eaa-f924-4128-b498-c9ab4843b51f · outbound

This paper cites SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:45:50.778823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 801cbed9-b5c6-4bfd-9953-202c097bc1fc · outbound

This paper cites VMamba: Visual State Space Model.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge VMamba: Visual State Space Model

Reference 56

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T22:45:50.793670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8724a7f4-59c7-4a1e-a0ef-b0380131899c · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.524263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b5da5f0c-7af8-4cb0-b744-5c845a02d58e · outbound

This paper cites IEEE transactions on medical imaging40(5), 1450–1460 (2021).

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge IEEE transactions on medical imaging40(5), 1450–1460 (2021)

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.532367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0df9a124-cd71-4e15-9539-8cab7d3d41ef · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.516336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f732089e-231f-4b02-b400-7a2479ea5cf7 · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence45(5), 5436–5447 (2022).

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge IEEE Transactions on Pattern Analysis and Machine Intelligence45(5), 5436–5447 (2022)

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.512749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 172a2393-072b-46d5-86b1-e261273fbf36 · outbound

This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.519900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation df492a92-60d8-4d7b-8321-e1b11f343a3a · outbound

This paper cites In: Proceedings of the European Conference on Computer Vision 30 (ECCV), pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: Proceedings of the European Conference on Computer Vision 30 (ECCV), pp

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.528104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8f47db31-e482-4b72-b96d-20fa1d31e29d · outbound

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

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 63

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T22:45:50.767099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3f30b32a-340e-4218-abbf-9324f75bb725 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.497057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9869cc04-4c4b-48d7-82c4-5362b08f5cee · outbound

This paper cites Informa- tion 11(2), 125 (2020).

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Informa- tion 11(2), 125 (2020)

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.490069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5efad931-7a35-4f06-99d6-a69f43b71fb9 · outbound

This paper cites Swin transformer v2: Scaling up capacity and resolution.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Swin transformer v2: Scaling up capacity and resolution

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.471549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d4884c5b-b179-48b4-9e6c-00bb1abd05e7 · outbound

This paper cites In: Computer Vision (ICCV), 2017 IEEE International Conference On (2017).

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: Computer Vision (ICCV), 2017 IEEE International Conference On (2017)

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.475560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bc90738d-9783-4750-9c00-d84893108731 · outbound

This paper cites nnU-Net: A Self-Configuring Method for Deep Learning-Based Biomedical Image Segmentation.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge nnU-Net: A Self-Configuring Method for Deep Learning-Based Biomedical Image Segmentation

Reference 68

Resolution: verified exact; doi, observed 2026-05-23T22:45:50.476107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0932412f-0d9c-41c2-9c9c-f7f0f7b017a7 · outbound

This paper cites In: Neural Infor- mation Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part III 28, pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: Neural Infor- mation Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part III 28, pp

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.572602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation eddce138-9ec4-4e27-8cc7-5b82e9cb8efe · outbound

This paper cites an unresolved cited work.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-05-23T22:45:51.592807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2d54bdd7-1432-4b8f-b801-575176b9020f · outbound

This paper cites International journal of computer assisted radiol- ogy and surgery, 1–9 (2024).

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge International journal of computer assisted radiol- ogy and surgery, 1–9 (2024)

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.585252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 180cb947-f601-45bf-9db3-7443af92bc6c · outbound

This paper cites In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:45:51.589122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers