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Paper Citation Record · LEDGER

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM

As of 13 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2501.18855.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.18855 v2

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:16:15.552649Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

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  • verified fuzzy44
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7ce8d3c5-2b20-4add-bbfc-059db9075187 · outbound

This paper cites Automation in road distress detection, diagnosis and treatment,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Automation in road distress detection, diagnosis and treatment,

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation b2f0c982-64f1-4702-88d4-45b016a5c210 · outbound

This paper cites A universal multi-view guided network for salient object and camouflaged object detection,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM A universal multi-view guided network for salient object and camouflaged object detection,

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 91e19569-93ea-41c1-bb14-b068b040f07f · outbound

This paper cites Computer vision frame- work for crack detection of civil infrastructure—a review,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Computer vision frame- work for crack detection of civil infrastructure—a review,

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 30a9d06e-131c-4769-9dcf-0325234a0c84 · outbound

This paper cites Erdunet: An efficient residual double- coding unet for medical image segmentation,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Erdunet: An efficient residual double- coding unet for medical image segmentation,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation dd24ecdd-3098-418f-b6d3-c91319b528ee · outbound

This paper cites Pixel dif- ference convolutional network for rgb-d semantic segmentation,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Pixel dif- ference convolutional network for rgb-d semantic segmentation,

Reference 5

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raw_fallback, observed 2026-08-09T22:16:16.128979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 1b5339ed-7d00-4140-91c3-fa89464a59be · outbound

This paper cites Boosting salient object detection with transformer-based asymmetric bilateral u-net,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Boosting salient object detection with transformer-based asymmetric bilateral u-net,

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 18c88083-e192-43a7-a303-ca1f2ef0f6ff · outbound

This paper cites Hybrid semantic segmentation for tunnel lining cracks based on swin transformer and convolutional neural network,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Hybrid semantic segmentation for tunnel lining cracks based on swin transformer and convolutional neural network,

Reference 7

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raw_fallback, observed 2026-08-09T22:16:16.105831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation e27112de-e792-4766-ae01-615e96d8d2cb · outbound

This paper cites Ctif-net: A cnn- transformer iterative fusion network for salient object detection,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Ctif-net: A cnn- transformer iterative fusion network for salient object detection,

Reference 8

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raw_fallback, observed 2026-08-09T22:16:16.092872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 6cc79d9e-606f-49af-8eb4-9b08dd4b927c · outbound

This paper cites Small sample image segmen- tation by coupling convolutions and transformers,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Small sample image segmen- tation by coupling convolutions and transformers,

Reference 9

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raw_fallback, observed 2026-08-09T22:16:16.077774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 1e79d4ec-3a65-45f7-9a48-319ceb716d1e · outbound

This paper cites Ghostformer: Efficiently amal- gamated cnn-transformer architecture for object detection,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Ghostformer: Efficiently amal- gamated cnn-transformer architecture for object detection,

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation d3c2cd3a-95a7-4e9f-9816-ef77a7c60c8a · outbound

This paper cites Long-short range adaptive transformer with dynamic sampling for 3d object detec- tion,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Long-short range adaptive transformer with dynamic sampling for 3d object detec- tion,

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation dc04a9df-5bc7-4106-a2eb-22ef41b5846c · outbound

This paper cites A state-of-the- art survey of deep learning models for automated pavement crack segmentation,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM A state-of-the- art survey of deep learning models for automated pavement crack segmentation,

Reference 12

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raw_fallback, observed 2026-08-09T22:16:16.035018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.400482Z digest=sha256:bb45ea67c222dabc148900aaa521647c48898dd5fee856cd85c4994ca56adede

Observation d5b945d8-1998-4075-bc36-688ca7a5c0d2 · outbound

This paper cites Automatic concrete defect detection and reconstruction by aligning aerial images onto semantic-rich building information model,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Automatic concrete defect detection and reconstruction by aligning aerial images onto semantic-rich building information model,

Reference 13

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verified fuzzy
raw_fallback, observed 2026-08-09T22:16:16.021710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.405740Z digest=sha256:392b71f3a41109ddb884c6a3dc4cb38d58f81aff6679f225bd531e9edd309971

Observation f42468c0-9b51-4dd4-a60a-7a814139ca11 · outbound

This paper cites Data augmentation in classification and segmentation: A survey and new strategies,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Data augmentation in classification and segmentation: A survey and new strategies,

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-09T22:16:16.008609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.410159Z digest=sha256:d60b8326b21a1345b050e2727ca61391e1cf8a02e6bd7773abd575a37e293e1d

Observation 2223f92b-fdfe-4048-8884-553287724f6b · outbound

This paper cites Understanding and combating robust overfitting via input loss landscape analysis and regularization,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Understanding and combating robust overfitting via input loss landscape analysis and regularization,

Reference 15

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raw_fallback, observed 2026-08-09T22:16:15.995172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 3056d31e-2043-404a-a0a4-394af79733c7 · outbound

This paper cites Gpt-3: What’s it good for?.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Gpt-3: What’s it good for?

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T22:16:15.418970Z digest=sha256:10d79b3d949145ec2fcdb556cd4d54277f37caff1efc7f2bf67d40e17f70be30

Observation 4039a657-6aee-4e76-8699-49abc2103fba · outbound

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

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Learning transferable visual models from natural language supervision,

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation c7c44190-6888-40d3-a59c-5df6b21ab389 · outbound

This paper cites Segment anything,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Segment anything,

Reference 18

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raw_fallback, observed 2026-08-09T22:16:15.961938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 528ef723-de5b-4754-9b1f-da5f7963e094 · outbound

This paper cites Detect any shadow: Segment anything for video shadow detection,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Detect any shadow: Segment anything for video shadow detection,

Reference 19

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raw_fallback, observed 2026-08-09T22:16:15.950083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 4874e062-42af-4440-9092-42650cf23332 · outbound

This paper cites Sam-adapter: Adapting segment anything in underperformed scenes,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Sam-adapter: Adapting segment anything in underperformed scenes,

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation bd2d4f0d-cd93-49e7-8f16-5f8881bf7163 · outbound

This paper cites Segment anything model for medical image analysis: an experimental study,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Segment anything model for medical image analysis: an experimental study,

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 0e28482b-6d34-4bfa-96cd-6bc50f8e9f3a · outbound

This paper cites Segment and caption anything,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Segment and caption anything,

Reference 22

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verified fuzzy
raw_fallback, observed 2026-08-09T22:16:15.917439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 84e0ab90-66dc-48ac-b5e8-cb963eb52f34 · outbound

This paper cites Teaching segment- anything-model domain-specific knowledge for road crack segmentation from on-board cameras,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Teaching segment- anything-model domain-specific knowledge for road crack segmentation from on-board cameras,

Reference 23

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raw_fallback, observed 2026-08-09T22:16:15.905426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.449868Z digest=sha256:e8527563559022b2a8cbaf178ae5be7136ce4913e5ae31c2af379be34a5fab9b

Observation 8e2435d7-b97d-42c6-b97a-8b3eaa37d8d8 · outbound

This paper cites EdgeSAM: Prompt-In-the-Loop Distillation for SAM.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM EdgeSAM: Prompt-In-the-Loop Distillation for SAM

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T22:16:15.455585Z digest=sha256:708b600abada0a8cc7384b5e5416eaed42bf7f72a7d771cf13f051d9c69ac809

Observation a6c4cd85-8594-4aa2-94e2-2d90f31beb92 · outbound

This paper cites Ecsnet: An accelerated real-time image segmentation cnn architecture for pavement crack detection,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Ecsnet: An accelerated real-time image segmentation cnn architecture for pavement crack detection,

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation ba2ad3af-8887-4316-8e07-3ff501c917f7 · outbound

This paper cites Crackvit: a unified cnn-transformer model for pixel-level crack extraction,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Crackvit: a unified cnn-transformer model for pixel-level crack extraction,

Reference 26

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raw_fallback, observed 2026-08-09T22:16:15.878415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 982fba30-1b23-4149-a741-4d174fadb364 · outbound

This paper cites A hybrid deep learning pavement crack semantic segmen- tation,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM A hybrid deep learning pavement crack semantic segmen- tation,

Reference 27

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raw_fallback, observed 2026-08-09T22:16:15.865392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.469056Z digest=sha256:c4a2ca58d3367e6ad064c592cea5fc67353fe89e4a04e390e6220b7c405250b6

Observation 0e3da90c-e19e-4c64-96a6-167e7f0c0fac · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Fully convolutional networks for semantic segmentation,

Reference 28

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raw_fallback, observed 2026-08-09T22:16:15.852415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.473349Z digest=sha256:75f2abe7febf7d6e72c35e01088f461aef81b60299f38f4e77de31e4dc5a5920

Observation 93b871f4-29b8-40d3-8ff0-8852106f889b · outbound

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

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM U-net: Convolutional networks for biomedical image segmentation,

Reference 29

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raw_fallback, observed 2026-08-09T22:16:15.837979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.478371Z digest=sha256:206a8c8b4aae956e2a7893f4462cbe2fcdb8b981eb0074b0648262157dc5b6bb

Observation dfcc63ef-31aa-4bc2-a291-53b74a7ebcb3 · outbound

This paper cites Segnet: A deep con- volutional encoder-decoder architecture for image segmentation,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Segnet: A deep con- volutional encoder-decoder architecture for image segmentation,

Reference 30

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raw_fallback, observed 2026-08-09T22:16:15.824618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.482688Z digest=sha256:aec2fc04fbec629189e94a07e4de3bde3065c9f1d886d729516d906325d9ae48

Observation 3ed86fa7-a9d3-4b38-83c7-dae80b253f00 · outbound

This paper cites Tv-net: A structure- level feature fusion network based on tensor voting for road crack segmentation,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Tv-net: A structure- level feature fusion network based on tensor voting for road crack segmentation,

Reference 31

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raw_fallback, observed 2026-08-09T22:16:15.811540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.486707Z digest=sha256:2ba8cbb01e745580b850da059f0e91cfbb1580dd1e851cd4e6ed094c224cddb2

Observation a5e6d1dc-9144-447e-a2ad-8ab722ef069b · outbound

This paper cites A nested unet with attention mechanism for road crack image segmentation,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM A nested unet with attention mechanism for road crack image segmentation,

Reference 32

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raw_fallback, observed 2026-08-09T22:16:15.798036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.491334Z digest=sha256:16b8052db5495993a2fd57c594b1dddbd41b63d803b511740408d90ac9c4af12

Observation 89cbcc21-29de-4b39-a52d-23b009efe934 · outbound

This paper cites Vision transformers for dense prediction,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Vision transformers for dense prediction,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:16:15.784525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.495949Z digest=sha256:5ca31314a8b494fc6fbde8c4040034afa9304f294397097102f15aedb7b4ac95

Observation 0eae8248-c6a5-45d5-b35e-acb974c738c3 · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Segformer: Simple and efficient design for semantic segmentation with transformers,

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-09T22:16:15.499861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T22:16:15.499861Z digest=sha256:d5b02d467d1c4ccbc7644881c6ae37c2d0afbf99b3e0524b67733c6b9df4502f

Observation 81f08926-6a2a-43e1-b1e4-e7a88630a665 · outbound

This paper cites Vision transformer-based autonomous crack detection on asphalt and concrete surfaces,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Vision transformer-based autonomous crack detection on asphalt and concrete surfaces,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:16:15.764334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.504209Z digest=sha256:82511377571a137094d5c065b4686328b0884a591a2f2e217df50c2665ce8f7b

Observation c0a4cc10-d9d5-488d-8074-de770eb7e4c0 · outbound

This paper cites A convolutional-transformer network for crack segmentation with boundary awareness,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM A convolutional-transformer network for crack segmentation with boundary awareness,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:16:15.751881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.507822Z digest=sha256:94534d51c1da456323c89f8b2ec9e886e0f7d7373b37008924f083c771196a16

Observation bc194ccd-66f8-4bb7-a759-5db2deb149cd · outbound

This paper cites Parameter-efficient transfer 13 learning for nlp,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Parameter-efficient transfer 13 learning for nlp,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:16:15.739957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.511507Z digest=sha256:cf6adeb076094ad9566148cc11f631d85a4c458b799c559da6becf1039e1759d

Observation 48ba8e15-4c32-487c-8a17-69bdb83c338b · outbound

This paper cites Deepcrack: A deep hierarchical feature learning architecture for crack segmentation,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Deepcrack: A deep hierarchical feature learning architecture for crack segmentation,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:16:15.728500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.515106Z digest=sha256:a6b08aab5cf6dbe264768a4c18fbc8098badd342b8c9a58f83854794c6c5cbfc

Observation 5839aceb-3695-4455-8a9d-7938052ce5ad · outbound

This paper cites Automatic road crack detection using random structured forests,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Automatic road crack detection using random structured forests,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:16:15.716891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.518791Z digest=sha256:d7842e3fa4763f140049173e00cbb03cc9bc9544c7be82f5780fb885c55ae855

Observation 30b504b3-18be-44e6-84ff-d2898a638add · outbound

This paper cites Feature pyramid and hierarchical boosting network for pavement crack detection,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Feature pyramid and hierarchical boosting network for pavement crack detection,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:16:15.703628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.522261Z digest=sha256:02cb83102aeba2aa5c7c198a0c89c26a7556dd835c65504bc7b8c1d6193a1a56

Observation ff4a2632-f499-4151-b6a8-44f4c1d1b7e1 · outbound

This paper cites Pyramid scene parsing network,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Pyramid scene parsing network,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:16:15.691030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.525541Z digest=sha256:dcd92b526cf5524db55cc3e033e61826a9f04fd04661ebfbf51245a271237e37

Observation b2dfaf6f-f15c-4e2a-8eef-25234390f277 · outbound

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

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Encoder- decoder with atrous separable convolution for semantic image segmen- tation,

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-09T22:16:15.529294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T22:16:15.529294Z digest=sha256:9971b87e63daadd00bdca68989cc2756513b40b03b60e2b1f6d9c1e76b9c4942

Observation 4e43b55c-3f7d-4341-9b98-d44a8ae1979b · outbound

This paper cites Emcad: Efficient multi- scale convolutional attention decoding for medical image segmentation,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Emcad: Efficient multi- scale convolutional attention decoding for medical image segmentation,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:16:15.668581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.533055Z digest=sha256:5bd142189cfa33fa7bc2bf74e3b6b009a3e3e86a4669336e5227adc18f1560e9

Observation 79c245ce-4c46-47dc-91a0-4f12e14c6f0d · outbound

This paper cites Multi-scale high-resolution vision transformer for semantic segmentation,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Multi-scale high-resolution vision transformer for semantic segmentation,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:16:15.654906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.536726Z digest=sha256:880a8b012ff20500d5434c669f419936fcdd274efaadb017427f00692ee5a3c0

Observation a19c16ad-200d-4e11-ab66-78f6de0412db · outbound

This paper cites Cmtfnet: Cnn and multiscale transformer fusion network for remote sensing image semantic segmentation,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Cmtfnet: Cnn and multiscale transformer fusion network for remote sensing image semantic segmentation,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:16:15.641756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.540825Z digest=sha256:18b0b8600a90c1ccda4460e726c3c021bfcac95a8368dff9414c73174ac42e13

Observation 964b6139-3e96-477e-9393-22831fedaec1 · outbound

This paper cites Deepcrack: Learning hierarchical convolutional features for crack detection,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Deepcrack: Learning hierarchical convolutional features for crack detection,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:16:15.628618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.544779Z digest=sha256:7bb85b6bfeba0af6a789b3d1f4d399642fe3bf3a3ac72794c1519783312a5f1d

Observation 2d17e920-0d77-4f3d-910d-2e0fdeb20cb6 · outbound

This paper cites Deepcrackat: An effective crack segmentation framework based on learning multi-scale crack features,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Deepcrackat: An effective crack segmentation framework based on learning multi-scale crack features,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:16:15.615631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.548808Z digest=sha256:fc0ab959b01f602ff968c1282350e8512e55d0ba355a695d98f8385d45e5c0e3

Observation dcce22b5-8c7a-4b24-a073-7fcf8015ecc7 · outbound

This paper cites Topology-aware mamba for crack segmentation in structures,.

FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM Topology-aware mamba for crack segmentation in structures,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T22:16:15.601750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-09T22:16:15.552649Z digest=sha256:3c013d75b1bc314811a8b34047578624405a05f40e666452f8ba637a3e0ddfb4

Pith citing papers

No inbound Pith citation observations are available.