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Paper Citation Record · LEDGER

EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2312.00863.

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

pith.paper-citation-record.v1
2312.00863 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:37:04.474751Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T13:48:19.715498Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 4931a258-6a2c-4fa7-93df-a87005a97de3 · inbound

Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks cites this paper.

Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

Reference 63

Resolution
verified exact
arxiv_id, observed 2026-05-11T06:20:15.828200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-11T06:20:15.656356Z digest=sha256:35988d22ffb280a8c3f3e8b814644ce59f977dfd6170d013c5fe8fa1001179e7

Observation e10f7ba8-4c73-4185-82b2-c229c3f1cbf7 · inbound

Rethinking Detecting Salient and Camouflaged Objects in Unconstrained Scenes cites this paper.

Rethinking Detecting Salient and Camouflaged Objects in Unconstrained Scenes EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

Reference 77

Resolution
unresolved
no resolver link, observed 2026-08-11T15:33:31.190488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:33:31.190488Z digest=sha256:7a5545f9823e27f0390b4a85a85e1d6caec744ca7aaa54d40ca6433fdd92c7ff

Observation c69e7840-00f8-468c-854a-c9a527812ae7 · inbound

Locate n' Rotate: Two-stage Openable Part Detection with Foundation Model Priors cites this paper.

Locate n' Rotate: Two-stage Openable Part Detection with Foundation Model Priors EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-11T13:24:47.130008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:24:47.130008Z digest=sha256:e18182026c74c67f59cfacdde121df32f681f741b5df3332039fc52f3c8cac34

Observation b1807004-4610-4e87-9e7d-d8620f8f41f1 · inbound

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation cites this paper.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-09T11:19:23.161236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T11:19:23.161236Z digest=sha256:7bf4ad0770874c1945af279c319a4f1a4b2127301d4b267968630bb60c8bff85

Observation a8540285-5f7d-4a9d-aa91-17cdcaba8555 · inbound

IRS: Incremental Relationship-guided Segmentation for Digital Pathology cites this paper.

IRS: Incremental Relationship-guided Segmentation for Digital Pathology EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-07T13:04:30.647045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:04:30.647045Z digest=sha256:01d2947b6e5116d9ccbb33cb0bfb05e7f6387adae0038a0bde8cf254ab496719

Observation ee6d4960-afe5-4b1c-9271-2392a7a93bad · inbound

Inter2Former: Dynamic Hybrid Attention for Efficient High-Precision Interactive cites this paper.

Inter2Former: Dynamic Hybrid Attention for Efficient High-Precision Interactive EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-06T17:59:44.221558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:59:44.221558Z digest=sha256:763d7270199a971609ea9d16acef11904783bc2a76e134a5aebefb7cbd9da338

Observation 9ea5bad5-b353-4a73-967f-3c010aaaeb95 · inbound

Zero-Shot Polygon Matching with Pre-trained Models for Pose Estimation and Polygon Cloud from Challenging Stereo cites this paper.

Zero-Shot Polygon Matching with Pre-trained Models for Pose Estimation and Polygon Cloud from Challenging Stereo EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-03T23:28:25.846478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:28:25.846478Z digest=sha256:f4cb29d0bca99ad1eec6032d7678b81851bcd5fd0168153775856102bcc18d8f

Observation 1f74f94b-0330-4494-aed6-53645f5827ec · inbound

Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement cites this paper.

Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

Reference 85

Resolution
metadata mismatch
arxiv_id, observed 2026-05-16T18:13:13.166338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-16T18:11:47.141366Z digest=sha256:27fdb551eebf7d45a59678869f85dce8671415f03fbeba282eb22d740db9079f

Observation 6ffa774f-7082-4caf-acd5-028420dab5d5 · inbound

Lightweight Distillation of SAM 3 and DINOv3 for Edge-Deployable Individual-Level Livestock Monitoring and Longitudinal Visual Analytics cites this paper.

Lightweight Distillation of SAM 3 and DINOv3 for Edge-Deployable Individual-Level Livestock Monitoring and Longitudinal Visual Analytics EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-12T09:56:26.726950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-07T08:51:41.508108Z digest=sha256:854593b43b6ab69840ee4183195c7e53059fa2c2569907e9f460379cce20eafa

Observation 233c3beb-d218-461b-9177-1fa9834854cc · inbound

SparseSAM: Structured Sparsification of Activations in Segment Anything Models cites this paper.

SparseSAM: Structured Sparsification of Activations in Segment Anything Models EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-20T13:48:19.717709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-20T13:45:26.106499Z digest=sha256:d1d52fac01ea09c1f67075c70c5ab2892465a9f17a68322bea842638c35e372c

Observation 6cb6212c-e438-4fb6-8517-2d3c7a4349a7 · inbound

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation cites this paper.

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

Reference 10

Resolution
unresolved
no resolver link, observed 2026-07-12T00:08:46.276767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T00:08:46.276767Z digest=sha256:86162705097191449f1d116d85a7ed03368f1d8a3618a535d5f9063986cae887

Observation 132f1aa2-dc54-447c-8c5a-e911f6638beb · inbound

OGG-FR: Orthogonal Gradient Gaming and Frequency Rectification for Unmanned Aerial Vehicle Infrared Image Super-Resolution cites this paper.

OGG-FR: Orthogonal Gradient Gaming and Frequency Rectification for Unmanned Aerial Vehicle Infrared Image Super-Resolution EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-11T22:37:04.474751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T22:37:04.474751Z digest=sha256:fee30dcb96792a411f4948470a387c4aab5b0e63b0744feeb4955cc063808d8d