Ensemble Foreground Management for Unsupervised Object Discovery | Paper Citation Record

Observation 82993cb6-d9d7-4fc6-a1dc-e442eff9e7a1 · outbound

This paper cites Detreg: Unsupervised pretrain- ing with region priors for object detection.

Ensemble Foreground Management for Unsupervised Object Discovery Detreg: Unsupervised pretrain- ing with region priors for object detection

Reference 1

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Observation f0651565-ff36-46e0-bc46-49836f4080e8 · outbound

This paper cites An experimental comparison of min-cut/max-flow algorithms for energy min- imization in vision.IEEE transactions on pattern analysis and machine intelligence, 26(9):1124–1137, 2004.

Ensemble Foreground Management for Unsupervised Object Discovery An experimental comparison of min-cut/max-flow algorithms for energy min- imization in vision.IEEE transactions on pattern analysis and machine intelligence, 26(9):1124–1137, 2004

Reference 2

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Observation d9e5d5bd-ec4c-4a9c-bc67-84e93584c43d · outbound

This paper cites Interactive graph cuts for optimal boundary & region segmentation of objects in nd images.

Ensemble Foreground Management for Unsupervised Object Discovery Interactive graph cuts for optimal boundary & region segmentation of objects in nd images

Reference 3

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Observation 31bae2a5-54cb-484b-8546-336a6e897dc5 · outbound

This paper cites Bagging predictors.Machine learning, 24: 123–140, 1996.

Ensemble Foreground Management for Unsupervised Object Discovery Bagging predictors.Machine learning, 24: 123–140, 1996

Reference 4

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Observation 796228bc-05bd-4a74-af2b-9b9dcc34b3e2 · outbound

This paper cites Pasting small votes for classification in large databases and on-line.Machine learning, 36:85–103, 1999.

Ensemble Foreground Management for Unsupervised Object Discovery Pasting small votes for classification in large databases and on-line.Machine learning, 36:85–103, 1999

Reference 5

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Observation 269452c3-dd58-4422-9453-8346737b3e4e · outbound

This paper cites Random forests.Machine learning, 45:5–32,.

Ensemble Foreground Management for Unsupervised Object Discovery Random forests.Machine learning, 45:5–32,

Reference 6

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Observation b5dd0e78-8241-4210-9ab6-c7d666b59874 · outbound

This paper cites Cascade r-cnn: Delv- ing into high quality object detection.

Ensemble Foreground Management for Unsupervised Object Discovery Cascade r-cnn: Delv- ing into high quality object detection

Reference 7

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Observation 400fa310-305c-4fb6-b082-9a2e5e40fd36 · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assignments.Ad- vances in neural information processing systems, 33:9912– 9924, 2020.

Ensemble Foreground Management for Unsupervised Object Discovery Unsupervised learning of visual features by contrasting cluster assignments.Ad- vances in neural information processing systems, 33:9912– 9924, 2020

Reference 8

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Observation 80203f79-12ce-447e-bde0-eefa18bf7e1e · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

Ensemble Foreground Management for Unsupervised Object Discovery Emerg- ing properties in self-supervised vision transformers

Reference 9

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Observation 2556c040-1774-4ca1-a2a8-07f5d35f23c2 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Ensemble Foreground Management for Unsupervised Object Discovery A simple framework for contrastive learning of visual representations

Reference 10

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Observation d89d3154-9339-4459-86b1-bdac41446a37 · outbound

This paper cites Exploring simple siamese rep- resentation learning.

Ensemble Foreground Management for Unsupervised Object Discovery Exploring simple siamese rep- resentation learning

Reference 11

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Observation fdd4c47b-d65e-403d-97f9-458cbfbfc604 · outbound

This paper cites Semi-supervised semantic segmentation with cross pseudo supervision.

Ensemble Foreground Management for Unsupervised Object Discovery Semi-supervised semantic segmentation with cross pseudo supervision

Reference 12

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Observation f1eba3b3-e157-4a69-a957-930334fb87cd · outbound

This paper cites Class re-activation maps for weakly-supervised semantic segmentation.

Ensemble Foreground Management for Unsupervised Object Discovery Class re-activation maps for weakly-supervised semantic segmentation

Reference 13

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Observation 98075003-7c84-40f3-a8f7-ebed7fe5a56d · outbound

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

Ensemble Foreground Management for Unsupervised Object Discovery The cityscapes dataset for semantic urban scene understanding

Reference 14

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Observation 7567411a-b491-42ea-9d8e-4e940925d466 · outbound

This paper cites Unsupervised learning from video to de- tect foreground objects in single images.

Ensemble Foreground Management for Unsupervised Object Discovery Unsupervised learning from video to de- tect foreground objects in single images

Reference 15

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Observation 5ca12e8b-37f6-44a7-9f50-ca6730cae2ad · outbound

This paper cites Unsupervised learning of foreground ob- ject segmentation.International Journal of Computer Vision, 127:1279–1302, 2019.

Ensemble Foreground Management for Unsupervised Object Discovery Unsupervised learning of foreground ob- ject segmentation.International Journal of Computer Vision, 127:1279–1302, 2019

Reference 16

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Observation 4cd87a63-ac66-4bca-aa6f-1865c1d1bd2c · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Ensemble Foreground Management for Unsupervised Object Discovery Imagenet: A large-scale hierarchical image database

Reference 17

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Observation 21eac819-6c2d-4415-9a17-89e299034c13 · outbound

This paper cites Ensemble methods in machine learn- ing.

Ensemble Foreground Management for Unsupervised Object Discovery Ensemble methods in machine learn- ing

Reference 18

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Observation 23fa3fb8-03b8-49e6-9189-89b95bc034cc · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

Ensemble Foreground Management for Unsupervised Object Discovery An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 19

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Observation c3c1eba5-9a13-4233-90d0-83ad82bebeda · outbound

This paper cites Bb-unet: U-net with bounding box prior.IEEE Journal of Selected Topics in Signal Processing, 14(6):1189– 1198, 2020.

Ensemble Foreground Management for Unsupervised Object Discovery Bb-unet: U-net with bounding box prior.IEEE Journal of Selected Topics in Signal Processing, 14(6):1189– 1198, 2020

Reference 20

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Observation d8694297-6158-47f0-beed-0255ce953631 · outbound

This paper cites Wanget al.

Ensemble Foreground Management for Unsupervised Object Discovery Wanget al

Reference 21

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Observation 440cabfd-c578-4e2f-8192-7ee987abf96d · outbound

This paper cites The pascal visual object classes (voc) challenge.International journal of computer vision, 88(2):303–338, 2010.

Ensemble Foreground Management for Unsupervised Object Discovery The pascal visual object classes (voc) challenge.International journal of computer vision, 88(2):303–338, 2010

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.647666Z

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Observation 5e70b19a-1076-44c6-91df-dd4d93d6badc · outbound

This paper cites A decision-theoretic generalization of on-line learning and an application to boosting.Journal of computer and system sciences, 55(1): 119–139, 1997.

Ensemble Foreground Management for Unsupervised Object Discovery A decision-theoretic generalization of on-line learning and an application to boosting.Journal of computer and system sciences, 55(1): 119–139, 1997

Reference 23

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Observation 63ed05a7-1414-4107-857a-0a2e0136f199 · outbound

This paper cites The estimation of the gradient of a density function, with applications in pat- tern recognition.IEEE Transactions on information theory, 21(1):32–40, 1975.

Ensemble Foreground Management for Unsupervised Object Discovery The estimation of the gradient of a density function, with applications in pat- tern recognition.IEEE Transactions on information theory, 21(1):32–40, 1975

Reference 24

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Observation ecb400df-37e5-4207-b9f1-b10ff435c3c5 · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.Advances in neural information processing systems, 33:21271–21284, 2020.

Ensemble Foreground Management for Unsupervised Object Discovery Bootstrap your own latent-a new approach to self-supervised learning.Advances in neural information processing systems, 33:21271–21284, 2020

Reference 25

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Observation c0e351cb-6e58-47b6-be9c-fff5fca198b1 · outbound

This paper cites an unresolved cited work.

Ensemble Foreground Management for Unsupervised Object Discovery Unresolved cited work

Reference 26

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Observation b8d6ff1f-55fe-48a1-919c-e447e1895f46 · outbound

This paper cites Mask r-cnn.

Ensemble Foreground Management for Unsupervised Object Discovery Mask r-cnn

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.588362Z

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Observation c6dfeca9-67d4-483b-86ef-db078b3f5421 · outbound

This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

Ensemble Foreground Management for Unsupervised Object Discovery Momentum contrast for unsupervised visual rep- resentation learning

Reference 28

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Observation 2c6b2f3e-0cb3-4d36-80c0-eda31f309ea8 · outbound

This paper cites Adversarial Learning for Semi-Supervised Semantic Segmentation.

Ensemble Foreground Management for Unsupervised Object Discovery Adversarial Learning for Semi-Supervised Semantic Segmentation

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Observation 11f9fb75-a9b1-4c1a-943e-f402571f94f8 · outbound

This paper cites Unsupervised detection of regions of interest using iterative link analysis.Advances in neural information processing systems, 22, 2009.

Ensemble Foreground Management for Unsupervised Object Discovery Unsupervised detection of regions of interest using iterative link analysis.Advances in neural information processing systems, 22, 2009

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Observation d221cef8-2f77-4666-9ab1-3d7a143126f1 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Ensemble Foreground Management for Unsupervised Object Discovery Adam: A Method for Stochastic Optimization

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Observation c37e630e-8bd3-4c85-806c-7290caa451ed · outbound

This paper cites Segment any- thing.

Ensemble Foreground Management for Unsupervised Object Discovery Segment any- thing

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Observation 87789ba0-34ca-4427-8026-3a5daeb7f7bb · outbound

This paper cites Box2seg: Attention weighted loss and discriminative feature learning for weakly supervised segmentation.

Ensemble Foreground Management for Unsupervised Object Discovery Box2seg: Attention weighted loss and discriminative feature learning for weakly supervised segmentation

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.540744Z

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Observation d86c0597-eb1b-4261-90ec-f920cd2ee3fa · outbound

This paper cites Bbam: Bounding box attribution map for weakly super- vised semantic and instance segmentation.

Ensemble Foreground Management for Unsupervised Object Discovery Bbam: Bounding box attribution map for weakly super- vised semantic and instance segmentation

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.527896Z

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Observation a2912b4b-0d76-4bcd-ad17-01e682e255db · outbound

This paper cites Promerge: Prompt and merge for unsupervised instance segmentation.

Ensemble Foreground Management for Unsupervised Object Discovery Promerge: Prompt and merge for unsupervised instance segmentation

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.515604Z

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Observation a65a57c6-768a-4cb4-9d9c-78264c7a83f5 · outbound

This paper cites A weighted sparse cod- ing framework for saliency detection.

Ensemble Foreground Management for Unsupervised Object Discovery A weighted sparse cod- ing framework for saliency detection

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.502954Z

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Observation 488362ce-71c8-4fbf-98f0-11f381c4dd5f · outbound

This paper cites Microsoft coco: Common objects in context.

Ensemble Foreground Management for Unsupervised Object Discovery Microsoft coco: Common objects in context

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.491013Z

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Observation e758c2ed-dc39-4da3-bd59-6c8994fd7d33 · outbound

This paper cites Decoupled Weight Decay Regularization.

Ensemble Foreground Management for Unsupervised Object Discovery Decoupled Weight Decay Regularization

Reference 38

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Observation d291e0ca-15c8-44d1-a07e-97a2e1588b16 · outbound

This paper cites PCAMs: Weakly Supervised Semantic Segmentation Using Point Supervision.

Ensemble Foreground Management for Unsupervised Object Discovery PCAMs: Weakly Supervised Semantic Segmentation Using Point Supervision

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-06T13:18:08.753188Z

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Observation cfccd0ee-610b-47de-b12e-53e9c3725e2d · outbound

This paper cites Deep spectral methods: A surprisingly strong baseline for unsupervised semantic segmentation and localization.

Ensemble Foreground Management for Unsupervised Object Discovery Deep spectral methods: A surprisingly strong baseline for unsupervised semantic segmentation and localization

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.478975Z

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Observation af7f3ec6-31bc-4008-aa6e-e2a1306c6255 · outbound

This paper cites Deepusps: Deep robust unsupervised saliency prediction via self-supervision.Advances in Neu- ral Information Processing Systems, 32, 2019.

Ensemble Foreground Management for Unsupervised Object Discovery Deepusps: Deep robust unsupervised saliency prediction via self-supervision.Advances in Neu- ral Information Processing Systems, 32, 2019

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.466860Z

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Observation 7b9be554-c052-448a-99dc-ec486f0a479b · outbound

This paper cites Dinov2: Learning robust visual features without super- vision.Transactions on Machine Learning Research, 2023.

Ensemble Foreground Management for Unsupervised Object Discovery Dinov2: Learning robust visual features without super- vision.Transactions on Machine Learning Research, 2023

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Observation dfa2b44f-3c97-466c-b51e-24180fc1008e · outbound

This paper cites Semi- supervised semantic segmentation with cross-consistency training.

Ensemble Foreground Management for Unsupervised Object Discovery Semi- supervised semantic segmentation with cross-consistency training

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.443371Z

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Observation b98ff353-f436-407f-9848-13a33750fe30 · outbound

This paper cites Weakly supervised scene parsing with point-based distance metric learning.

Ensemble Foreground Management for Unsupervised Object Discovery Weakly supervised scene parsing with point-based distance metric learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.430805Z

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Observation b4dd8959-dbc0-44d0-bdf6-ee63f0ac45fb · outbound

This paper cites Most: Multiple object localization with self-supervised transformers for object discovery.

Ensemble Foreground Management for Unsupervised Object Discovery Most: Multiple object localization with self-supervised transformers for object discovery

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.418864Z

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Observation 7a9c0d53-36bf-420a-80e1-80cff21ff795 · outbound

This paper cites Mudit Adityaja, Saurabh J.

Ensemble Foreground Management for Unsupervised Object Discovery Mudit Adityaja, Saurabh J

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.406174Z

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Observation 6b022822-5250-487c-9e41-85ea0d688e3e · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks.IEEE transactions on pattern analysis and machine intelligence, 39(6):1137–1149, 2016.

Ensemble Foreground Management for Unsupervised Object Discovery Faster r-cnn: Towards real-time object detection with region proposal networks.IEEE transactions on pattern analysis and machine intelligence, 39(6):1137–1149, 2016

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.394406Z

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Observation 88ac89b6-a615-4aa3-9445-20da7a10832b · outbound

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

Ensemble Foreground Management for Unsupervised Object Discovery U- net: Convolutional networks for biomedical image segmen- tation

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.381969Z

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Observation a88b71f6-d830-4051-ab2b-f68c3d5eb555 · outbound

This paper cites ” grabcut” interactive foreground extraction using iterated graph cuts.ACM transactions on graphics (TOG), 23(3): 309–314, 2004.

Ensemble Foreground Management for Unsupervised Object Discovery ” grabcut” interactive foreground extraction using iterated graph cuts.ACM transactions on graphics (TOG), 23(3): 309–314, 2004

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.369854Z

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Observation 6cb5e04f-caed-430f-99d3-bb67e43d317a · outbound

This paper cites Normalized cuts and image segmentation.IEEE Transactions on pattern analysis and machine intelligence, 22(8):888–905, 2000.

Ensemble Foreground Management for Unsupervised Object Discovery Normalized cuts and image segmentation.IEEE Transactions on pattern analysis and machine intelligence, 22(8):888–905, 2000

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.356825Z

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Observation 8e112b96-9446-4b93-8d7b-f0af73e72e26 · outbound

This paper cites Hierarchical image saliency detection on extended cssd.IEEE transac- tions on pattern analysis and machine intelligence, 38(4): 717–729, 2015.

Ensemble Foreground Management for Unsupervised Object Discovery Hierarchical image saliency detection on extended cssd.IEEE transac- tions on pattern analysis and machine intelligence, 38(4): 717–729, 2015

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.344837Z

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Observation 97ba2deb-cf0b-4028-89f6-288b232ed58f · outbound

This paper cites Unsuper- vised salient object detection with spectral cluster voting.

Ensemble Foreground Management for Unsupervised Object Discovery Unsuper- vised salient object detection with spectral cluster voting

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.332786Z

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Observation cea2322f-8429-4837-8ea2-8f0cd73987aa · outbound

This paper cites Localizing objects with self-supervised transformers and no labels.

Ensemble Foreground Management for Unsupervised Object Discovery Localizing objects with self-supervised transformers and no labels

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.320237Z

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Observation 914831e8-1d57-4a57-a3cb-62cd9d58b32e · outbound

This paper cites Unsupervised object localization: Observing the background to discover objects.

Ensemble Foreground Management for Unsupervised Object Discovery Unsupervised object localization: Observing the background to discover objects

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.307658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 63a76484-2100-47b0-ad25-1755be3ac47b · outbound

This paper cites Semi supervised semantic segmentation using generative ad- versarial network.

Ensemble Foreground Management for Unsupervised Object Discovery Semi supervised semantic segmentation using generative ad- versarial network

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.295684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bf511a1a-423a-466b-8302-18e83e8de8de · outbound

This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.Advances in neural information processing systems, 30, 2017.

Ensemble Foreground Management for Unsupervised Object Discovery Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.Advances in neural information processing systems, 30, 2017

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.283557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b82c114a-1eb4-442c-98fe-447279e43b73 · outbound

This paper cites Boxinst: High-performance instance segmentation with box annotations.

Ensemble Foreground Management for Unsupervised Object Discovery Boxinst: High-performance instance segmentation with box annotations

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.271121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b5b5b91f-206f-4f20-9d7f-cfc4b0053f21 · outbound

This paper cites Selective search for object recognition.International journal of computer vision, 104: 154–171, 2013.

Ensemble Foreground Management for Unsupervised Object Discovery Selective search for object recognition.International journal of computer vision, 104: 154–171, 2013

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.258733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bf92eded-4d86-491e-b3f2-3a962ecd9672 · outbound

This paper cites Discovering Object Masks with Transformers for Unsupervised Semantic Segmentation.

Ensemble Foreground Management for Unsupervised Object Discovery Discovering Object Masks with Transformers for Unsupervised Semantic Segmentation

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:08.545528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ee7d15f-b747-4ec3-a142-4af546084042 · outbound

This paper cites Rapid object detection using a boosted cascade of simple features.

Ensemble Foreground Management for Unsupervised Object Discovery Rapid object detection using a boosted cascade of simple features

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.246242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ae2f3267-ad87-4c4e-9561-a87288dfab45 · outbound

This paper cites Toward unsu- pervised, multi-object discovery in large-scale image col- lections.

Ensemble Foreground Management for Unsupervised Object Discovery Toward unsu- pervised, multi-object discovery in large-scale image col- lections

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.234042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f9f3f308-61ba-4f58-a001-cbc1ea114b26 · outbound

This paper cites Large-scale unsupervised object dis- covery.Advances in Neural Information Processing Systems, 34:16764–16778, 2021.

Ensemble Foreground Management for Unsupervised Object Discovery Large-scale unsupervised object dis- covery.Advances in Neural Information Processing Systems, 34:16764–16778, 2021

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.221449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cb543b58-3813-487f-aee9-33fca4efd61e · outbound

This paper cites Object segmentation without labels with large-scale genera- tive models.

Ensemble Foreground Management for Unsupervised Object Discovery Object segmentation without labels with large-scale genera- tive models

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.208239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3ec41961-2581-441e-a127-2c0b59f22e11 · outbound

This paper cites Learning to de- tect salient objects with image-level supervision.

Ensemble Foreground Management for Unsupervised Object Discovery Learning to de- tect salient objects with image-level supervision

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.196041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 385725d8-7e1b-42a1-8a5b-c3a0868e5576 · outbound

This paper cites Solov2: Dynamic and fast instance segmenta- tion.Advances in Neural information processing systems, 33:17721–17732, 2020.

Ensemble Foreground Management for Unsupervised Object Discovery Solov2: Dynamic and fast instance segmenta- tion.Advances in Neural information processing systems, 33:17721–17732, 2020

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.183509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 72e9125d-3227-4e25-a5fa-4bd7c68af005 · outbound

This paper cites Dense contrastive learning for self-supervised visual pre-training.

Ensemble Foreground Management for Unsupervised Object Discovery Dense contrastive learning for self-supervised visual pre-training

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.170657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4ded3660-d566-4742-bb0f-fcb9267835ca · outbound

This paper cites Freesolo: Learning to segment objects without annotations.

Ensemble Foreground Management for Unsupervised Object Discovery Freesolo: Learning to segment objects without annotations

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.158634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 89d75a68-dc5b-4126-84a9-4ccd2ab4133f · outbound

This paper cites Contrastmask: Contrastive learn- ing to segment every thing.

Ensemble Foreground Management for Unsupervised Object Discovery Contrastmask: Contrastive learn- ing to segment every thing

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.145760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 08db6020-82ba-4c77-9412-eed633d65dc4 · outbound

This paper cites Cut and learn for unsupervised object detection and instance segmentation.

Ensemble Foreground Management for Unsupervised Object Discovery Cut and learn for unsupervised object detection and instance segmentation

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.133555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f1ad8362-020a-4218-9e20-2c8ee26a0b8e · outbound

This paper cites Unsupervised object discovery and co-localization by deep descriptor transformation.Pattern Recognition, 88:113–126, 2019.

Ensemble Foreground Management for Unsupervised Object Discovery Unsupervised object discovery and co-localization by deep descriptor transformation.Pattern Recognition, 88:113–126, 2019

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.121422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 86909480-c093-4a77-8cf2-c0e5fb5ffe46 · outbound

This paper cites Perturbation consistency and mutual information regularization for semi-supervised semantic seg- mentation.Multimedia Systems, 29(2):511–523, 2023.

Ensemble Foreground Management for Unsupervised Object Discovery Perturbation consistency and mutual information regularization for semi-supervised semantic seg- mentation.Multimedia Systems, 29(2):511–523, 2023

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.109132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f856ac65-206a-4920-9c1e-25a77bc45b50 · outbound

This paper cites Leveraging auxiliary tasks with affinity learning for weakly supervised semantic segmentation.

Ensemble Foreground Management for Unsupervised Object Discovery Leveraging auxiliary tasks with affinity learning for weakly supervised semantic segmentation

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.096822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e42c63ad-c5dd-4106-abd0-3794e35c339e · outbound

This paper cites Hierarchical saliency detection.

Ensemble Foreground Management for Unsupervised Object Discovery Hierarchical saliency detection

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.083904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a688f42f-9353-4afe-8631-ba4509915a91 · outbound

This paper cites Saliency detection via graph-based man- ifold ranking.

Ensemble Foreground Management for Unsupervised Object Discovery Saliency detection via graph-based man- ifold ranking

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.071997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation eec3c238-89bd-4c5b-8bf3-5d968ff6bab8 · outbound

This paper cites Object discovery from a single unlabeled image by mining frequent itemsets with multi-scale features.IEEE Transactions on Image Pro- cessing, 29:8606–8621, 2020.

Ensemble Foreground Management for Unsupervised Object Discovery Object discovery from a single unlabeled image by mining frequent itemsets with multi-scale features.IEEE Transactions on Image Pro- cessing, 29:8606–8621, 2020

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.059558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0bef04c6-f30d-4536-8497-8245f2f48c99 · outbound

This paper cites Image bert pre-training with online tokenizer.

Ensemble Foreground Management for Unsupervised Object Discovery Image bert pre-training with online tokenizer

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.047520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 16b898de-aedc-451e-80bc-95d90ef962e3 · outbound

This paper cites Saliency optimization from robust background detection.

Ensemble Foreground Management for Unsupervised Object Discovery Saliency optimization from robust background detection

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.035091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a0150376-10e2-4948-83f1-86b17b0246a1 · outbound

This paper cites Deformable DETR: Deformable Transformers for End-to-End Object Detection.

Ensemble Foreground Management for Unsupervised Object Discovery Deformable DETR: Deformable Transformers for End-to-End Object Detection

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T13:18:08.617684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fb20a3b-5a79-4b5e-9b1f-ccda56d2a3e4 · outbound

This paper cites Deep learning in remote sensing: A comprehensive review and list of resources.IEEE geoscience and remote sensing magazine, 5(4):8–36, 2017.

Ensemble Foreground Management for Unsupervised Object Discovery Deep learning in remote sensing: A comprehensive review and list of resources.IEEE geoscience and remote sensing magazine, 5(4):8–36, 2017

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.023247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 77f7fbd6-e01e-4359-96d1-ea145961ffc3 · outbound

This paper cites Edge boxes: Lo- cating object proposals from edges.

Ensemble Foreground Management for Unsupervised Object Discovery Edge boxes: Lo- cating object proposals from edges

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:09.009761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6e468e4f-8d83-48db-a936-0fdf0478205b · outbound

This paper cites an unresolved cited work.

Ensemble Foreground Management for Unsupervised Object Discovery Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-06T13:18:08.997529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e3211b32-7f20-49ee-8076-f39275f5cc8e · outbound

This paper cites 3.2 that UnionCut can stay effective on images of large foreground areas.

Ensemble Foreground Management for Unsupervised Object Discovery 3.2 that UnionCut can stay effective on images of large foreground areas

Reference 82

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T13:18:08.985534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e409d31d-222a-43b9-87c2-8ca609e0b90d · outbound

This paper cites Here, we make matching similar patches with cosine similarity used by [53, 54, 59] as an example.

Ensemble Foreground Management for Unsupervised Object Discovery Here, we make matching similar patches with cosine similarity used by [53, 54, 59] as an example

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.972872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b274ad02-1d71-408c-8a88-a1b90b6e7074 · outbound

This paper cites Specifically, we calculate the success rate by assessing the proportion of images in each dataset where the union of the ground truth occupies less than four corners of the image.

Ensemble Foreground Management for Unsupervised Object Discovery Specifically, we calculate the success rate by assessing the proportion of images in each dataset where the union of the ground truth occupies less than four corners of the image

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.960188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 85929d07-2381-4941-a527-390bd06d9330 · outbound

This paper cites UnionSeg Fig.

Ensemble Foreground Management for Unsupervised Object Discovery UnionSeg Fig

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.947965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1de7836f-8f10-48fe-946d-ce3c4a0e886d · outbound

This paper cites an unresolved cited work.

Ensemble Foreground Management for Unsupervised Object Discovery Unresolved cited work

Reference 86

Resolution: unresolved; raw_fallback, observed 2026-08-06T13:18:08.935952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f5bbe58a-c912-4ddf-b57d-37ac172c4c52 · outbound

This paper cites In contrast, UnionSeg's pseudo- labels are generated by UnionCut and are designed to cover most of the object regions in the image, i.e., the foreground union.

Ensemble Foreground Management for Unsupervised Object Discovery In contrast, UnionSeg's pseudo- labels are generated by UnionCut and are designed to cover most of the object regions in the image, i.e., the foreground union

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.923928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2e161d54-2417-4e6c-8756-8f58f9452253 · outbound

This paper cites The comparison of the framework between FOUND [54] and UnionSeg.

Ensemble Foreground Management for Unsupervised Object Discovery The comparison of the framework between FOUND [54] and UnionSeg

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.910974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e694fa2b-9805-4262-a835-0f14b11996fd · outbound

This paper cites In this section, we intro- duce how to apply UnionCut/UnionSeg to existing UOD methods.

Ensemble Foreground Management for Unsupervised Object Discovery In this section, we intro- duce how to apply UnionCut/UnionSeg to existing UOD methods

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.898097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5c2ca03f-1566-44a8-ad72-87313e6f59ef · outbound

This paper cites an unresolved cited work.

Ensemble Foreground Management for Unsupervised Object Discovery Unresolved cited work

Reference 90

Resolution: unresolved; raw_fallback, observed 2026-08-06T13:18:08.885861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 776a8290-3432-4408-a319-354dddf1604f · outbound

This paper cites 80% area) of the foreground union given by UnionCut or UnionSeg has been discovered.

Ensemble Foreground Management for Unsupervised Object Discovery 80% area) of the foreground union given by UnionCut or UnionSeg has been discovered

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.873198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 116741e1-2526-425d-9351-1f257e3b5f34 · outbound

This paper cites the num- ber of links connected to a patch) in ascending order, and the first patch after being sorted is made as the foreground seed based on the assumption made by Sim ´eoniet al.

Ensemble Foreground Management for Unsupervised Object Discovery the num- ber of links connected to a patch) in ascending order, and the first patch after being sorted is made as the foreground seed based on the assumption made by Sim ´eoniet al

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.859345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bca4f03d-4d88-42d9-958f-6daae3a0ae12 · outbound

This paper cites an unresolved cited work.

Ensemble Foreground Management for Unsupervised Object Discovery Unresolved cited work

Reference 93

Resolution: unresolved; raw_fallback, observed 2026-08-06T13:18:08.846669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 10041892-7bac-44ae-9513-2d729d6abfbd · outbound

This paper cites After that, these pseudo- labels are used to train a class-agnostic SOLOv2 [65] model.

Ensemble Foreground Management for Unsupervised Object Discovery After that, these pseudo- labels are used to train a class-agnostic SOLOv2 [65] model

Reference 94

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T13:18:08.833606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c9bf6dd3-87a3-4139-90b4-4ecf8cc5bd83 · outbound

This paper cites As shown in Fig.

Ensemble Foreground Management for Unsupervised Object Discovery As shown in Fig

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.820823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9f5796ef-dbd9-4516-958a-71ae51dcabef · outbound

This paper cites TokenCut and MaskCut, and provide more visualization.

Ensemble Foreground Management for Unsupervised Object Discovery TokenCut and MaskCut, and provide more visualization

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:18:08.808124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers