Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T13:45:44.800185Z
Paper Citation Record · LEDGER
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model
As of 9 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 3 inbound Pith citation observations for arXiv:2507.20186.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-30T16:00:40.812912Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T04:47:37.903346Z
Reference resolution
77 of 77 outbound references displayed
- verified exact17
- verified fuzzy32
- unresolved21
- parse uncertain0
- malformed identifier3
- metadata mismatch4
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation ce2fadf6-9792-46ea-814c-1ca510881872 · outbound
This paper cites A data-free approach to mitigate catastrophic forgetting in federated class incremental learning for vision tasks.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model A data-free approach to mitigate catastrophic forgetting in federated class incremental learning for vision tasks
Reference 1
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation e473bf3c-1824-4550-9cc5-a0e4cec1c871 · outbound
This paper cites WM-DOV A maps for accurate polyp highlighting in colonoscopy: Validation vs.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model WM-DOV A maps for accurate polyp highlighting in colonoscopy: Validation vs
Reference 2
- Resolution
- malformed identifier
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b5138f50-4698-4660-b1a0-c573c52d267c · outbound
This paper cites Solving the catastrophic forgetting problem in generalized category discovery.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Solving the catastrophic forgetting problem in generalized category discovery
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:45:57.223726Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 83df723e-d837-4baa-a593-0a0a9f36d74b · outbound
This paper cites End-to-end object detection with transformers.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model End-to-end object detection with transformers
Reference 5
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4b168b4c-426e-4816-9e2a-a141f0c42e3c · outbound
This paper cites Saving 100x storage: Prototype replay for reconstructing training sample distribution in class-incremental semantic segmentation.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Saving 100x storage: Prototype replay for reconstructing training sample distribution in class-incremental semantic segmentation
Reference 6
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 3e10c8cd-8845-4062-892c-f13446d45056 · outbound
This paper cites Sam fails to segment any- thing? – sam-adapter: Adapting sam in underperformed scenes: Camouflage, shadow, and more, 2023.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Sam fails to segment any- thing? – sam-adapter: Adapting sam in underperformed scenes: Camouflage, shadow, and more, 2023
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:45:56.888503Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 52b19cf9-be93-4c7d-a8e5-958cbf588eeb · outbound
This paper cites SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More
Reference 8
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d10e0cbc-3135-4a89-bb6a-fa50f71251e2 · outbound
This paper cites Vision transformer adapter for dense predictions.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Vision transformer adapter for dense predictions
Reference 9
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 328317dc-6057-49b7-8c7b-1b05be892029 · outbound
This paper cites A multi-task mean teacher for semi-supervised shadow detection.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model A multi-task mean teacher for semi-supervised shadow detection
Reference 10
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0de143fc-569d-4545-9953-86417fd9b5f8 · outbound
This paper cites Schwing, and Alexander Kirillov.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Schwing, and Alexander Kirillov
Reference 11
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b99e4300-db51-4a9a-9f88-73f8e311b6a9 · outbound
This paper cites Image splicing localization via semi-global network and fully connected conditional random fields.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Image splicing localization via semi-global network and fully connected conditional random fields
Reference 12
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6106b25b-bb41-48c9-a2f2-3083de51f584 · outbound
This paper cites Kroese, Shie Mannor, and Reuven Y.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Kroese, Shie Mannor, and Reuven Y
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:45:56.535785Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 097a6d6d-2f3a-4e86-a4f0-3e3b7067246e · outbound
This paper cites Casia image tampering detection evaluation database.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Casia image tampering detection evaluation database
Reference 14
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T13:45:50.135649Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation c64e9842-8fc8-4350-9b5d-30f0136ba5ab · outbound
This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model An image is worth 16x16 words: Trans- formers for image recognition at scale
Reference 15
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 434895d6-8dce-4849-a6aa-e7f58113deca · outbound
This paper cites Structure-measure: A new way to evaluate foreground maps.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Structure-measure: A new way to evaluate foreground maps
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:45:55.939253Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation b35b45e6-fe65-402d-981d-7f1379fafdf3 · outbound
This paper cites Camouflaged object detection.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Camouflaged object detection
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:45:55.631626Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 03e2f2c6-c9f7-48d8-8a0e-304cdc1d9477 · outbound
This paper cites Fridrich and Jan Kodovský.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Fridrich and Jan Kodovský
Reference 18
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T13:45:49.952129Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 1aaf7fc6-6f84-4355-ac4c-244a060b7def · outbound
This paper cites CIRCOD: co-saliency inspired referring camouflaged object discovery.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model CIRCOD: co-saliency inspired referring camouflaged object discovery
Reference 19
- Resolution
- malformed identifier
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 28dcacf8-d052-49be-97a4-641b719fdee7 · outbound
This paper cites Selective amnesia: A continual learning approach to forgetting in deep generative models.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Selective amnesia: A continual learning approach to forgetting in deep generative models
Reference 21
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation a78c9f2e-6c3e-43b8-a150-8aab239b1641 · outbound
This paper cites Parameter- efficient transfer learning for NLP.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Parameter- efficient transfer learning for NLP
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:45:55.133501Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 24adfb42-679c-4b78-ac2e-b98565d72efe · outbound
This paper cites Direction- aware spatial context features for shadow detection.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Direction- aware spatial context features for shadow detection
Reference 23
- Resolution
- verified exact
- raw_fallback, observed 2026-08-06T13:45:49.649817Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation ef08bca4-76f6-4bdc-abd4-b5d4543d5b1b · outbound
This paper cites SPAN: spatial pyramid attention network for image manipulation localization.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model SPAN: spatial pyramid attention network for image manipulation localization
Reference 24
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8824353c-7611-4a04-8903-201f7e84625a · outbound
This paper cites Feature shrinkage pyramid for camouflaged object detection with trans- formers.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Feature shrinkage pyramid for camouflaged object detection with trans- formers
Reference 25
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation b8de2ee7-ff15-45ad-b535-6a41b56ee6af · outbound
This paper cites Smedsrud, Michael A.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Smedsrud, Michael A
Reference 26
- Resolution
- verified exact
- doi, observed 2026-08-06T13:45:54.884999Z
Source-reported events for the cited work
correction dated 2020-02-06. Source: crossref record 10.1007/978-3-030-37734-2_75->10.1007/978-3-030-37734-2:correction, observed 2026-07-11T03:06:47.493341+00:00. This notice travels one citation hop only.
Observation 2db67984-3d8a-45ea-9b5f-b9c49f1ca8c2 · outbound
This paper cites an unresolved cited work.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Unresolved cited work
Reference 27
- Resolution
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Source-reported events for the cited work
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Observation a1b7b529-7403-47a3-a996-1a072eb45c7a · outbound
This paper cites Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of Artifacts.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of Artifacts
Reference 29
- Resolution
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Source-reported events for the cited work
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Observation f0dbbce3-5d0b-4d30-bd08-d1bcd1af2c65 · outbound
This paper cites Nguyen, Zhongliang Nie, Minh-Triet Tran, and Akihiro Sugi- moto.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Nguyen, Zhongliang Nie, Minh-Triet Tran, and Akihiro Sugi- moto
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:45:54.750344Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation ef541813-09c6-4e26-8361-8e090246cfca · outbound
This paper cites Uncertainty-aware joint salient object and camouflaged object detection.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Uncertainty-aware joint salient object and camouflaged object detection
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:45:54.566592Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 35cb4d37-9961-4af6-9463-efa666dac2da · outbound
This paper cites Ailurus: A scalable vit framework for dense prediction.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Ailurus: A scalable vit framework for dense prediction
Reference 33
- Resolution
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Source-reported events for the cited work
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Observation cfa7c74b-d728-4c74-8203-d68cbc22a694 · outbound
This paper cites Girshick, and Kaiming He.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Girshick, and Kaiming He
Reference 35
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1d5e33b9-d127-4b6a-a8a6-4b1961b70553 · outbound
This paper cites an unresolved cited work.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Unresolved cited work
Reference 36
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 4282f3da-5787-49d1-aeff-40c7b53dd334 · outbound
This paper cites Explicit visual prompt- ing for low-level structure segmentations.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Explicit visual prompt- ing for low-level structure segmentations
Reference 37
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3f158a7f-a8db-40f0-b00a-30d51d0d31bc · outbound
This paper cites Pscc-net: Progressive spatio- channel correlation network for image manipulation detection and localization.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Pscc-net: Progressive spatio- channel correlation network for image manipulation detection and localization
Reference 38
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation db3a8dac-e1f3-4384-8cde-d56ce3827d60 · outbound
This paper cites Darenerf: Direction-aware representation for dynamic scenes.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Darenerf: Direction-aware representation for dynamic scenes
Reference 39
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 7259d2a2-1497-4a91-89aa-a1bb7ebc94be · outbound
This paper cites Prompt guided transformer for multi-task dense prediction.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Prompt guided transformer for multi-task dense prediction
Reference 40
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 2c9566cd-6fda-4bd2-bd66-793da495c201 · outbound
This paper cites Simultaneously localize, segment and rank the camouflaged objects.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Simultaneously localize, segment and rank the camouflaged objects
Reference 42
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation f3a3fd51-3ea1-4108-8a9c-2f1cc959c09e · outbound
This paper cites How to evaluate foreground maps? In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model How to evaluate foreground maps? In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014
Reference 43
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation fb465aa7-5791-46ed-81f1-29adcae075f4 · outbound
This paper cites Camouflaged object segmentation with distraction mining.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Camouflaged object segmentation with distraction mining
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:45:53.450333Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 3ee7f3ad-7dc0-48a6-8fd5-8c713e690475 · outbound
This paper cites Camouflaged object segmentation with distraction mining.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Camouflaged object segmentation with distraction mining
Reference 45
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 99554149-8bf8-49a4-a746-8eeb5b05c700 · outbound
This paper cites Imd2020: A large-scale annotated dataset tailored for detecting manipulated images.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Imd2020: A large-scale annotated dataset tailored for detecting manipulated images
Reference 46
- Resolution
- verified exact
- arxiv_id_nonexistent, observed 2026-08-06T13:45:48.370719Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation a6a2a625-2398-41ec-80cd-cc5b2ee5d760 · outbound
This paper cites an unresolved cited work.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Unresolved cited work
Reference 47
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T13:45:52.967211Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 7f58be72-dd26-4b94-b728-6e10a7c50bc3 · outbound
This paper cites Learning transferable visual models from natural language supervision.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Learning transferable visual models from natural language supervision
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:45:52.646033Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 453cf23a-9279-47df-ad6f-33d696de8e3e · outbound
This paper cites Zoom in and out: A mixed-scale triplet network for camouflaged object detection.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Zoom in and out: A mixed-scale triplet network for camouflaged object detection
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:45:52.830471Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation b2011884-a2e1-47a7-b1d3-59e1e9bdec16 · outbound
This paper cites Gir- shick, Piotr Dollár, and Christoph Feichtenhofer.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Gir- shick, Piotr Dollár, and Christoph Feichtenhofer
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:45:52.290634Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 911f3d8f-fb24-4eb1-99d5-d3217fdc8f21 · outbound
This paper cites an unresolved cited work.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Unresolved cited work
Reference 54
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T13:45:52.483395Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 67919166-8a57-406b-8118-8ec45ab64a69 · outbound
This paper cites Das, and Ulas Bagci.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Das, and Ulas Bagci
Reference 55
- Resolution
- verified exact
- arxiv_id_nonexistent, observed 2026-08-06T13:45:48.035838Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation a052ee6d-c700-49d1-837d-b30e81fc9115 · outbound
This paper cites Discriminative blur detection features.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Discriminative blur detection features
Reference 56
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ebf95e53-49e1-411c-a05f-7c0f4918c3d5 · outbound
This paper cites SAM 2: Segment Anything in Images and Videos.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model SAM 2: Segment Anything in Images and Videos
Reference 57
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5e030700-491a-4650-951b-882543e605f4 · outbound
This paper cites Continual learning with deep generative replay.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Continual learning with deep generative replay
Reference 58
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation d087aa02-8113-485f-9772-b9338aeaed76 · outbound
This paper cites Baraniuk.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Baraniuk
Reference 59
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 17eb9ff2-40d8-43b9-aed7-dd18266b311e · outbound
This paper cites Animal camouflage analysis: Chameleon database.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Animal camouflage analysis: Chameleon database
Reference 60
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation f311888c-565c-45d4-b2c4-d44971f550b0 · outbound
This paper cites Over- coming catastrophic forgetting for multi-label class-incremental learning.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Over- coming catastrophic forgetting for multi-label class-incremental learning
Reference 61
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 9969c041-17b7-4466-85c0-e0a876f26f53 · outbound
This paper cites Just noticeable defocus blur detection and estima- tion.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Just noticeable defocus blur detection and estima- tion
Reference 62
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation e4073208-14a3-4a05-b675-2d39a047a658 · outbound
This paper cites Gurudu, and Jianming Liang.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Gurudu, and Jianming Liang
Reference 63
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 1d4b2d91-0c9a-48bc-a292-cff05b4f2898 · outbound
This paper cites Toward embedded detection of polyps in wce images for early di- agnosis of colorectal cancer.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Toward embedded detection of polyps in wce images for early di- agnosis of colorectal cancer
Reference 64
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 142a1b9a-5475-4040-be00-7aff486ab63a · outbound
This paper cites an unresolved cited work.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Unresolved cited work
Reference 65
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 3297f716-2421-40ff-b291-fedb435db963 · outbound
This paper cites an unresolved cited work.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Unresolved cited work
Reference 66
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation ef22aede-50a4-42c2-b31c-c1e6565640cd · outbound
This paper cites an unresolved cited work.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Unresolved cited work
Reference 67
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 256f8931-0dbf-4ba6-859b-6e37ef666c49 · outbound
This paper cites Sam-eg: Segment anything model with egde guidance framework for efficient polyp segmentation.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Sam-eg: Segment anything model with egde guidance framework for efficient polyp segmentation
Reference 68
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation d4d5f8f9-bed2-4522-ae72-7ea629b83392 · outbound
This paper cites an unresolved cited work.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Unresolved cited work
Reference 70
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 2da96ff2-a652-4e81-9280-adeae8859336 · outbound
This paper cites Stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal
Reference 71
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation d1e0acd4-2bf7-4c4f-a86d-bb99186fdb13 · outbound
This paper cites M2unet: Metaformer multi- scale upsampling network for polyp segmentation.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model M2unet: Metaformer multi- scale upsampling network for polyp segmentation
Reference 72
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 45c5c60a-e108-40ed-8d71-1a253cc3d53c · outbound
This paper cites Mantra-net: Manipulation tracing network for detection and localization of image forgeries with anoma- lous features.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Mantra-net: Manipulation tracing network for detection and localization of image forgeries with anoma- lous features
Reference 73
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation fed4b8d2-38c7-4036-bbb1-b9d552711f1e · outbound
This paper cites Fccns: Fully complex-valued convo- lutional networks using complex-valued color model and loss function.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Fccns: Fully complex-valued convo- lutional networks using complex-valued color model and loss function
Reference 74
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4efc9668-21c3-4ca1-b490-45ab2c76ad68 · outbound
This paper cites Multi-task learning for dense prediction tasks: A survey.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Multi-task learning for dense prediction tasks: A survey
Reference 75
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 4c14128b-2f0c-4b2a-b055-0ca268e506b3 · outbound
This paper cites CamoFormer: Masked Separable Attention for Camouflaged Object Detection.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model CamoFormer: Masked Separable Attention for Camouflaged Object Detection
Reference 76
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 101a08ad-b63c-4e79-9075-006e44ed5b7b · outbound
This paper cites Defocus blur detec- tion via multi-stream bottom-top-bottom fully convolutional network.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Defocus blur detec- tion via multi-stream bottom-top-bottom fully convolutional network
Reference 77
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 15685c95-da0d-47e1-b944-695b5eae391a · outbound
This paper cites Objectformer for image manipulation detection and localization.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Objectformer for image manipulation detection and localization
Reference 78
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 3133db8e-32f5-4798-928f-1d69bb337eeb · outbound
This paper cites Defocus blur detection via boosting diversity of deep ensemble networks.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Defocus blur detection via boosting diversity of deep ensemble networks
Reference 79
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 873c744c-7914-4d53-a825-08c29d7e57cc · outbound
This paper cites Self-generated defocus blur detection via dual adversarial discriminators.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Self-generated defocus blur detection via dual adversarial discriminators
Reference 80
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 15d67600-2c06-4ad6-a2bf-054904458dae · outbound
This paper cites SAMwave: wavelet- driven feature enrichment for effective adaptation of segment anything model.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model SAMwave: wavelet- driven feature enrichment for effective adaptation of segment anything model
Reference 81
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 78bfed8b-0be2-486d-992c-cfc35b7a68a0 · outbound
This paper cites an unresolved cited work.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Unresolved cited work
Reference 83
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5a088930-7042-4fae-85b3-b8dcb47e47f4 · outbound
This paper cites Enhancing di- versity of defocus blur detectors via cross-ensemble network.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Enhancing di- versity of defocus blur detectors via cross-ensemble network
Reference 84
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 3cfb9317-3ff9-41b7-b835-7eac873c0f98 · outbound
This paper cites an unresolved cited work.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Unresolved cited work
Reference 87
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation f7fbad27-0750-40a0-8e17-c4a230708bb4 · outbound
This paper cites doi: 10.23919/EUSIPCO58844.2023.10290110.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model doi: 10.23919/EUSIPCO58844.2023.10290110
Reference 1119
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 2d4005fb-8870-4b68-8dc7-46e63dc5d1d3 · outbound
This paper cites URL https://doi.org/10.1007/ s10479-005-5724-z.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model URL https://doi.org/10.1007/ s10479-005-5724-z
Reference 2005
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a5142a5d-6436-4cc0-9f11-94e3ad0dc328 · outbound
This paper cites an unresolved cited work.
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model Unresolved cited work
Reference 2021
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Pith citing papers
Observation cc21614d-c323-40e3-a549-c174c0ce56a4 · inbound
B-GRTO: Bootstrapped Group Relative Tool Optimization for Referring Segmentation cites this paper.
B-GRTO: Bootstrapped Group Relative Tool Optimization for Referring Segmentation SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model
Reference 61
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation c2cb243b-c64c-4b28-9d2b-744af1080a0a · inbound
B-GRTO: Bootstrapped Group Relative Tool Optimization for Referring Segmentation cites this paper.
B-GRTO: Bootstrapped Group Relative Tool Optimization for Referring Segmentation SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model
Reference 61
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 68c181c3-852f-47e0-9790-6859bec15d38 · inbound
Listen, Look, and Learn: Learning Without Forgetting through SAM-Audio cites this paper.
Listen, Look, and Learn: Learning Without Forgetting through SAM-Audio SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model
Reference 16
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.