Open-World Panoptic Segmentation | Paper Citation Record

Observation c6e3741a-2665-496f-81c1-1b4de83d0414 · outbound

This paper cites Maskomaly: Zero-shot mask anomaly segmentation,.

Open-World Panoptic Segmentation Maskomaly: Zero-shot mask anomaly segmentation,

Reference 1

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Observation 67262841-da4a-4dd1-9c84-5d9232ec2c16 · outbound

This paper cites A General and Adaptive Robust Loss Function,.

Open-World Panoptic Segmentation A General and Adaptive Robust Loss Function,

Reference 2

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Observation 1b680131-1d37-4e90-8257-21a9918352ce · outbound

This paper cites SemanticKITTI: A Dataset for Semantic Scene Understand- ing of LiDAR Sequences,.

Open-World Panoptic Segmentation SemanticKITTI: A Dataset for Semantic Scene Understand- ing of LiDAR Sequences,

Reference 3

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Observation 084620ff-43e5-41ad-8661-ed5c7d84fa6d · outbound

This paper cites Towards open set deep networks,.

Open-World Panoptic Segmentation Towards open set deep networks,

Reference 4

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Observation fdd872d5-85c9-4e78-bd82-534f9ee53af1 · outbound

This paper cites MVTec AD–A comprehensive real-world dataset for unsupervised anomaly detection,.

Open-World Panoptic Segmentation MVTec AD–A comprehensive real-world dataset for unsupervised anomaly detection,

Reference 5

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Observation 877399e0-c860-487b-86cc-1e34d76f7355 · outbound

This paper cites Fishyscapes: A benchmark for safe semantic segmentation in autonomous driving,.

Open-World Panoptic Segmentation Fishyscapes: A benchmark for safe semantic segmentation in autonomous driving,

Reference 6

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Observation 782fe011-13b4-4735-b5d0-2bee85af20c9 · outbound

This paper cites The Fishyscapes Benchmark: Measuring blind spots in semantic segmenta- tion,.

Open-World Panoptic Segmentation The Fishyscapes Benchmark: Measuring blind spots in semantic segmenta- tion,

Reference 7

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Observation b1c28726-7c7a-4bde-a1c9-291c5d5764c9 · outbound

This paper cites Inverseform: A loss function for structured boundary-aware segmentation,.

Open-World Panoptic Segmentation Inverseform: A loss function for structured boundary-aware segmentation,

Reference 8

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Observation c5603a07-40e4-45c6-81c1-0f556476499f · outbound

This paper cites Weightless neural networks for open set recognition,.

Open-World Panoptic Segmentation Weightless neural networks for open set recognition,

Reference 9

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Observation bbe66e91-8a68-4ee6-8e8c-4379218223b1 · outbound

This paper cites SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation,.

Open-World Panoptic Segmentation SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation,

Reference 10

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Observation 68c50660-7f98-43cc-b525-16b218fe5666 · outbound

This paper cites A Simple Framework for Contrastive Learning of Visual Representations,.

Open-World Panoptic Segmentation A Simple Framework for Contrastive Learning of Visual Representations,

Reference 11

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Observation 4ce0c8a1-58b8-47ee-9b76-8ed390d856f2 · outbound

This paper cites Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation,.

Open-World Panoptic Segmentation Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation,

Reference 12

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Observation 34d035a1-c23d-4a13-83d2-6ef6704a4149 · outbound

This paper cites Masked- attention mask transformer for universal image segmentation,.

Open-World Panoptic Segmentation Masked- attention mask transformer for universal image segmentation,

Reference 13

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Observation d67739de-baa8-4990-bc96-d342cca4c16d · outbound

This paper cites Xception: Deep learning with depthwise separable convolu- tions,.

Open-World Panoptic Segmentation Xception: Deep learning with depthwise separable convolu- tions,

Reference 14

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Observation 787ff5b8-426c-4d84-98e2-7abda008f93e · outbound

This paper cites Weakly and semi-supervised detection, segmentation and tracking of table grapes with limited and noisy data,.

Open-World Panoptic Segmentation Weakly and semi-supervised detection, segmentation and tracking of table grapes with limited and noisy data,

Reference 15

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Observation c8cd433d-e967-4102-b6f8-6c8e286af326 · outbound

This paper cites The Cityscapes dataset for semantic urban scene understanding,.

Open-World Panoptic Segmentation The Cityscapes dataset for semantic urban scene understanding,

Reference 16

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Observation 834b72d6-2832-4b59-8d1b-bf6afd5a158a · outbound

This paper cites Outlier detection by ensembling uncertainty with negative objectness.

Open-World Panoptic Segmentation Outlier detection by ensembling uncertainty with negative objectness

Reference 17

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Observation 53c935c9-7f78-4ae9-8bff-25041989883c · outbound

This paper cites Learning Confidence for Out-of-Distribution Detection in Neural Networks.

Open-World Panoptic Segmentation Learning Confidence for Out-of-Distribution Detection in Neural Networks

Reference 18

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Observation 23900a0e-6967-4791-9dbf-9852b3b61f65 · outbound

This paper cites Reducing network agnosto- phobia,.

Open-World Panoptic Segmentation Reducing network agnosto- phobia,

Reference 19

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Observation b3ba1330-37a2-4218-9640-53f143b2d4d7 · outbound

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

Open-World Panoptic Segmentation The pascal visual object classes (voc) challenge,

Reference 20

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Observation 15cb0a41-d491-4167-8c76-6d1286b09cee · outbound

This paper cites Dropout as a Bayesian Approximation: Representing model uncertainty in deep learning,.

Open-World Panoptic Segmentation Dropout as a Bayesian Approximation: Representing model uncertainty in deep learning,

Reference 21

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Observation a42cb119-e95b-40a9-834e-b5b5bcad1d9c · outbound

This paper cites Segmenting known objects and unseen unknowns without prior knowledge,.

Open-World Panoptic Segmentation Segmenting known objects and unseen unknowns without prior knowledge,

Reference 22

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Observation 925a7a3d-d120-4dc3-886c-a29d5cca2307 · outbound

This paper cites Scaling open-vocabulary image segmentation with image-level labels,.

Open-World Panoptic Segmentation Scaling open-vocabulary image segmentation with image-level labels,

Reference 23

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Observation 1a6b79c8-8122-42e2-981a-41d84971cee2 · outbound

This paper cites Fast R-CNN,.

Open-World Panoptic Segmentation Fast R-CNN,

Reference 24

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Observation e177558a-aa08-468b-91e2-e6c5b68e62a6 · outbound

This paper cites Densehybrid: Hybrid anomaly detection for dense open-set recognition,.

Open-World Panoptic Segmentation Densehybrid: Hybrid anomaly detection for dense open-set recognition,

Reference 25

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Observation 33440472-9416-4e5f-9b01-3cc6c93d646c · outbound

This paper cites On advantages of mask-level recog- nition for outlier-aware segmentation,.

Open-World Panoptic Segmentation On advantages of mask-level recog- nition for outlier-aware segmentation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.448215Z

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Observation ffd3bb5f-dcf2-4257-87d8-fd77e6907f93 · outbound

This paper cites Disadvantages of using the area under the receiver operating characteristic curve to assess imaging tests: a discussion and proposal for an alternative approach,.

Open-World Panoptic Segmentation Disadvantages of using the area under the receiver operating characteristic curve to assess imaging tests: a discussion and proposal for an alternative approach,

Reference 27

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Observation 6412a2c6-1130-46d7-89cb-84a4cfe9f3c0 · outbound

This paper cites Mask r-cnn,.

Open-World Panoptic Segmentation Mask r-cnn,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.432188Z

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Observation b74de2dd-b4f4-4005-a844-b5c5d5c3e054 · outbound

This paper cites Deep residual learning for image recognition,.

Open-World Panoptic Segmentation Deep residual learning for image recognition,

Reference 29

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Observation 5d8eae61-2517-4f4e-ae0b-45fbff5dcc84 · outbound

This paper cites Scaling out-of-distribution detection for real- world settings,.

Open-World Panoptic Segmentation Scaling out-of-distribution detection for real- world settings,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.415110Z

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Observation bab5b4ff-6683-4d7c-b783-9e6bf9f1b2a2 · outbound

This paper cites A baseline for detecting misclassified and out-of-distribution examples in neural networks,.

Open-World Panoptic Segmentation A baseline for detecting misclassified and out-of-distribution examples in neural networks,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.398386Z

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Observation 8c96ad12-57ee-48ba-9dc3-e4a2b6f62254 · outbound

This paper cites Bidirectional projec- tion network for cross dimension scene understanding,.

Open-World Panoptic Segmentation Bidirectional projec- tion network for cross dimension scene understanding,

Reference 32

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Observation ba794c98-66e2-4a11-9c13-8755a29300a4 · outbound

This paper cites Beyond auroc & co. for evaluating out-of-distribution detection performance,.

Open-World Panoptic Segmentation Beyond auroc & co. for evaluating out-of-distribution detection performance,

Reference 33

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Observation 8cde2e7c-08f7-448e-bbdc-5cbb5a53cd2f · outbound

This paper cites Exemplar-based open-set panoptic segmentation network,.

Open-World Panoptic Segmentation Exemplar-based open-set panoptic segmentation network,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.372680Z

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Observation 73795e97-b6b8-4576-8ad8-8d3470359628 · outbound

This paper cites Semantic segmentation of underwater imagery: Dataset and benchmark,.

Open-World Panoptic Segmentation Semantic segmentation of underwater imagery: Dataset and benchmark,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.364816Z

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Observation 0b8caa8e-efaf-4942-9b7f-57737a340e9b · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision,.

Open-World Panoptic Segmentation Scaling up visual and vision-language representation learning with noisy text supervision,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.357223Z

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Observation 7910111e-3bdb-43d2-b508-733c7b60f681 · outbound

This paper cites Adam: A Method for Stochastic Optimization,.

Open-World Panoptic Segmentation Adam: A Method for Stochastic Optimization,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.349120Z

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Observation 647d6114-1108-4582-aa6c-54fdb3d8f373 · outbound

This paper cites Panoptic feature pyramid networks,.

Open-World Panoptic Segmentation Panoptic feature pyramid networks,

Reference 38

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Observation e0606bc6-470f-4cde-9322-309df6a7e8c6 · outbound

This paper cites Panoptic segmentation,.

Open-World Panoptic Segmentation Panoptic segmentation,

Reference 39

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Observation b65d09d6-796f-41e5-99b5-9f60cde336e8 · outbound

This paper cites Opengan: Open-set recognition via open data generation,.

Open-World Panoptic Segmentation Opengan: Open-set recognition via open data generation,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.323924Z

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Observation 019f1b95-497f-4476-99e8-a3006b9071c4 · outbound

This paper cites Virtual multi-view fusion for 3d semantic segmentation,.

Open-World Panoptic Segmentation Virtual multi-view fusion for 3d semantic segmentation,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.316263Z

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Observation 22bbfda4-2d31-4680-bde2-578831388d22 · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles,.

Open-World Panoptic Segmentation Simple and scalable predictive uncertainty estimation using deep ensembles,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.308365Z

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Observation c97307e3-c5da-4e62-9647-a48b3f6c2502 · outbound

This paper cites Open-vocabulary semantic segmentation with mask- adapted clip,.

Open-World Panoptic Segmentation Open-vocabulary semantic segmentation with mask- adapted clip,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.298432Z

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Observation 79034747-a841-4a40-8e63-89d95f278f49 · outbound

This paper cites Microsoft COCO: Common objects in context,.

Open-World Panoptic Segmentation Microsoft COCO: Common objects in context,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.288410Z

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Observation 9d1bf550-d948-4eef-bc03-91692fc97529 · outbound

This paper cites Path aggregation network for instance segmentation,.

Open-World Panoptic Segmentation Path aggregation network for instance segmentation,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.278572Z

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Observation 2bb3d23c-c65d-4f82-9116-f284869c3b38 · outbound

This paper cites Energy-based out-of-distribution detection,.

Open-World Panoptic Segmentation Energy-based out-of-distribution detection,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.269408Z

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Observation 6d183dd5-12c8-434e-8498-60d3c1cb2b2e · outbound

This paper cites Opening up open world tracking,.

Open-World Panoptic Segmentation Opening up open world tracking,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.260015Z

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Observation 983c5631-7259-474e-bbf2-e9fc943042a9 · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

Open-World Panoptic Segmentation Fully convolutional networks for semantic segmentation,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.251929Z

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Observation ba968868-fe98-4920-8ea3-ac5e769bdd26 · outbound

This paper cites Pixel-wise gradient uncertainty for convo- lutional neural networks applied to out-of-distribution segmentation,.

Open-World Panoptic Segmentation Pixel-wise gradient uncertainty for convo- lutional neural networks applied to out-of-distribution segmentation,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.242694Z

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Observation b29a82da-ea94-4551-a9bb-d5132b5c6190 · outbound

This paper cites High precision leaf instance segmentation for phenotyping in point clouds obtained under real field conditions,.

Open-World Panoptic Segmentation High precision leaf instance segmentation for phenotyping in point clouds obtained under real field conditions,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.232864Z

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Observation fff09e42-87e3-4f4a-ba82-568a90bed489 · outbound

This paper cites HDBScan: Hierarchical density based clustering.

Open-World Panoptic Segmentation HDBScan: Hierarchical density based clustering

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.223221Z

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Observation 60bc9a7d-3342-4a85-a628-e271173f7916 · outbound

This paper cites Fast Instance and Semantic Segmentation Exploiting Local Connectivity, Metric Learning, and One- Shot Detection for Robotics,.

Open-World Panoptic Segmentation Fast Instance and Semantic Segmentation Exploiting Local Connectivity, Metric Learning, and One- Shot Detection for Robotics,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.215589Z

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Observation 64bb010e-7968-4249-b986-50f7ae8693e7 · outbound

This paper cites Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs,.

Open-World Panoptic Segmentation Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.206917Z

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Observation 90d8d94f-c7d0-4ee8-aa22-640efae91020 · outbound

This paper cites Real-time Semantic Segmen- tation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs,.

Open-World Panoptic Segmentation Real-time Semantic Segmen- tation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.196400Z

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Observation a8412451-79af-467c-88d5-53e4d9fa17b3 · outbound

This paper cites Confidence prediction for lexicon-free ocr,.

Open-World Panoptic Segmentation Confidence prediction for lexicon-free ocr,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.188458Z

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Observation 76758121-60fe-498d-860a-eb0e935cce6c · outbound

This paper cites Rba: Segmenting unknown regions rejected by all,.

Open-World Panoptic Segmentation Rba: Segmenting unknown regions rejected by all,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.180316Z

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Observation 85f3cbcb-1f75-4353-afdd-a05de2935697 · outbound

This paper cites Ugains: Uncer- tainty guided anomaly instance segmentation,.

Open-World Panoptic Segmentation Ugains: Uncer- tainty guided anomaly instance segmentation,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.171942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 557c527f-339b-4a13-8a14-5c98f1cb676d · outbound

This paper cites OoDIS: Anomaly Instance Segmentation and Detection Benchmark.

Open-World Panoptic Segmentation OoDIS: Anomaly Instance Segmentation and Detection Benchmark

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T13:51:21.746895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83ce7f3c-359a-4303-ab63-9326ff244b50 · outbound

This paper cites Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth,.

Open-World Panoptic Segmentation Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.163028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3aa23788-51e7-4219-bc63-4f2a498a0ec9 · outbound

This paper cites Deep neural networks are easily fooled: High confidence predictions for unrecognizable images,.

Open-World Panoptic Segmentation Deep neural networks are easily fooled: High confidence predictions for unrecognizable images,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.154694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6688b92c-0d3f-47e0-b445-bb2bac170104 · outbound

This paper cites In defense of pre- trained imagenet architectures for real-time semantic segmentation of road-driving images,.

Open-World Panoptic Segmentation In defense of pre- trained imagenet architectures for real-time semantic segmentation of road-driving images,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.146219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fc2c47b9-21fa-4392-b954-02841d7da638 · outbound

This paper cites Lost and found: detecting small road hazards for self-driving vehicles,.

Open-World Panoptic Segmentation Lost and found: detecting small road hazards for self-driving vehicles,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.135618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f89a8479-4d2e-42f5-9efa-f1c74765ac3a · outbound

This paper cites Freeseg: Unified, universal and open-vocabulary image segmentation,.

Open-World Panoptic Segmentation Freeseg: Unified, universal and open-vocabulary image segmentation,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.127456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6a1c6115-4466-4ebf-92c6-e1399f0d9d5c · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Open-World Panoptic Segmentation Learning transferable visual models from natural language supervision,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.120064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 583f23f9-1262-42db-b60a-d1c658c49795 · outbound

This paper cites Mask2anomaly: Mask transformer for universal open-set segmentation,.

Open-World Panoptic Segmentation Mask2anomaly: Mask transformer for universal open-set segmentation,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.112761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c54a8057-9548-43a9-8720-55f2eebab82c · outbound

This paper cites Faster r-cnn: Towards real- time object detection with region proposal networks,.

Open-World Panoptic Segmentation Faster r-cnn: Towards real- time object detection with region proposal networks,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.104516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0eb7013a-7d51-4913-9591-25e9b42f1b7a · outbound

This paper cites Hierarchical approach for joint semantic, plant instance, and leaf instance segmentation in the agricultural domain,.

Open-World Panoptic Segmentation Hierarchical approach for joint semantic, plant instance, and leaf instance segmentation in the agricultural domain,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.096129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6e3558a4-53d4-4fa5-bb8d-2d06d09b0966 · outbound

This paper cites Erfnet: Ef- ficient residual factorized convnet for real-time semantic segmentation,.

Open-World Panoptic Segmentation Erfnet: Ef- ficient residual factorized convnet for real-time semantic segmentation,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.085997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b3d03042-f8c8-4bbf-8d33-edf780b8434c · outbound

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

Open-World Panoptic Segmentation U-net: Convolutional networks for biomedical image segmentation,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.077730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0f65c5d8-45ef-49d0-add9-2460345bd4a2 · outbound

This paper cites V-measure: A Conditional Entropy- Based External Cluster Evaluation Measure,.

Open-World Panoptic Segmentation V-measure: A Conditional Entropy- Based External Cluster Evaluation Measure,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.068509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 83d483c5-243f-44ba-8bb3-4853c4837774 · outbound

This paper cites Multiresolution Knowledge Distillation for Anomaly Detection,.

Open-World Panoptic Segmentation Multiresolution Knowledge Distillation for Anomaly Detection,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.061927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8cf48902-64b3-4d4d-96cc-7de259e90b25 · outbound

This paper cites Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detection,.

Open-World Panoptic Segmentation Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detection,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.054531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c6007a5b-c75b-484b-a265-0fd922df62ce · outbound

This paper cites Super-convergence: Very fast training of neural networks using large learning rates,.

Open-World Panoptic Segmentation Super-convergence: Very fast training of neural networks using large learning rates,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.047338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9f3f5b0b-357c-46dd-8d9e-695200ee4198 · outbound

This paper cites Robust double-encoder network for rgb-d panoptic segmentation,.

Open-World Panoptic Segmentation Robust double-encoder network for rgb-d panoptic segmentation,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.039054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2fd167f9-ce19-4ae2-bdc6-dc82357917b2 · outbound

This paper cites Open- world semantic segmentation including class similarity,.

Open-World Panoptic Segmentation Open- world semantic segmentation including class similarity,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.032031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a205376e-17a2-4450-a021-4df642f31ea7 · outbound

This paper cites Look closer to segment better: Boundary patch refinement for instance segmentation,.

Open-World Panoptic Segmentation Look closer to segment better: Boundary patch refinement for instance segmentation,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.016824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cefdad42-9285-4f12-aba8-aade626847e6 · outbound

This paper cites A deeper look at dataset bias,.

Open-World Panoptic Segmentation A deeper look at dataset bias,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.010252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8ecfc327-7cd9-40b4-9e6f-91f6d781379d · outbound

This paper cites Open-set recognition: A good closed-set classifier is all you need,.

Open-World Panoptic Segmentation Open-set recognition: A good closed-set classifier is all you need,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:22.003708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 99d1ae0c-0306-4fc3-9b4e-72ee8b017aaa · outbound

This paper cites Toward reproducible version-controlled perception plat- forms: Embracing simplicity in autonomous vehicle dataset acquisition,.

Open-World Panoptic Segmentation Toward reproducible version-controlled perception plat- forms: Embracing simplicity in autonomous vehicle dataset acquisition,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:21.996469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation db585a4c-ec77-4847-bbb6-43a439e8aa21 · outbound

This paper cites Out-of-distribution detection using an ensemble of self supervised leave- out classifiers,.

Open-World Panoptic Segmentation Out-of-distribution detection using an ensemble of self supervised leave- out classifiers,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:21.988052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 50febc22-ff0d-4c55-ba6d-c9faa963adcc · outbound

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

Open-World Panoptic Segmentation Internimage: Exploring large-scale vision foundation models with deformable convolutions,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:21.978646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2f2bb381-1874-4063-a505-e08529b0af1e · outbound

This paper cites Openauc: Towards auc-oriented open-set recognition,.

Open-World Panoptic Segmentation Openauc: Towards auc-oriented open-set recognition,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:21.971115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 23b3e49a-ece6-4891-8648-69579c37b30a · outbound

This paper cites Panoptic Segmentation with Partial Annotations for Agricultural Robots,.

Open-World Panoptic Segmentation Panoptic Segmentation with Partial Annotations for Agricultural Robots,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:21.962622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3c48673b-7635-4af8-8a56-d4a3bdcc8c9a · outbound

This paper cites PhenoBench–A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain,.

Open-World Panoptic Segmentation PhenoBench–A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:21.955363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 24ec0691-50f3-48ab-bad4-4e28f5013dd7 · outbound

This paper cites Identifying unknown instances for autonomous driving,.

Open-World Panoptic Segmentation Identifying unknown instances for autonomous driving,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:21.946501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4610e977-9a9b-4ee1-951b-497aeff991d4 · outbound

This paper cites Masqclip for open-vocabulary universal image segmentation,.

Open-World Panoptic Segmentation Masqclip for open-vocabulary universal image segmentation,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:21.938913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 56ea80c0-1342-4d50-890f-4e9526030426 · outbound

This paper cites Codabench: Flexible, easy-to-use, and reproducible meta- benchmark platform,.

Open-World Panoptic Segmentation Codabench: Flexible, easy-to-use, and reproducible meta- benchmark platform,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:21.931311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 64ca9aaa-8459-48cc-82c7-effa617b64e1 · outbound

This paper cites BDD100K: A diverse driving dataset for heterogeneous multitask learning,.

Open-World Panoptic Segmentation BDD100K: A diverse driving dataset for heterogeneous multitask learning,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:21.920500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e60f798b-f7f6-4df2-b2e3-a26a552de2ae · outbound

This paper cites Wilddash-creating hazard-aware benchmarks,.

Open-World Panoptic Segmentation Wilddash-creating hazard-aware benchmarks,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:21.911892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5c2f3d62-c9ea-4a15-a1df-43d60c819560 · outbound

This paper cites A simple framework for open-vocabulary segmentation and detection,.

Open-World Panoptic Segmentation A simple framework for open-vocabulary segmentation and detection,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:21.902808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1a1d7cf9-cf93-430d-aa02-a9e21df1cf89 · outbound

This paper cites Pyramid Scene Parsing Network,.

Open-World Panoptic Segmentation Pyramid Scene Parsing Network,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:21.894050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9ccff16c-0121-4cd4-906a-ffcefa9b8f94 · outbound

This paper cites OmniAL: A Unified CNN Framework for Unsupervised Anomaly Localization,.

Open-World Panoptic Segmentation OmniAL: A Unified CNN Framework for Unsupervised Anomaly Localization,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:51:21.884433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 59f07e59-0523-45c9-9b9c-182254259717 · outbound

This paper cites an unresolved cited work.

Open-World Panoptic Segmentation Unresolved cited work

Reference 2022

Resolution: parse uncertain; raw_fallback, observed 2026-08-11T13:51:22.406831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 17e9624f-4963-4c89-b649-c5c9cc16da09 · outbound

This paper cites an unresolved cited work.

Open-World Panoptic Segmentation Unresolved cited work

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-11T13:51:22.024118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers