Paper Citation Record · LEDGER

SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2304.09148.

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

pith.paper-citation-record.v1
2304.09148 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:24:35.721574Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

41
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 22bea206-199f-4f05-baf1-c0e37f8ae5d8 · inbound

On Efficient Variants of Segment Anything Model: A Survey cites this paper.

On Efficient Variants of Segment Anything Model: A Survey SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:43:23.527774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7a962c6b-660b-4bf6-aca5-f92fb3cfc5f5 · inbound

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation cites this paper.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T12:24:35.721574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f58b02d-ecee-4c9c-80ca-e85e0ee6cadb · inbound

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery cites this paper.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T10:32:29.086047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0935ae9c-ef36-42a3-b864-5498d39dabdd · inbound

MedSeg-R: Medical Image Segmentation with Clinical Reasoning cites this paper.

MedSeg-R: Medical Image Segmentation with Clinical Reasoning SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T23:19:07.687862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 530306e1-be6a-44f7-a5ac-d6b4b70ea7cb · inbound

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment cites this paper.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:45.391711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 828c6c39-fc51-461b-bef2-022cf05b37e4 · inbound

Rethink Domain Generalization in Heterogeneous Sequence MRI Segmentation cites this paper.

Rethink Domain Generalization in Heterogeneous Sequence MRI Segmentation SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T11:07:52.569093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39a61a63-2c40-4852-88eb-ef07680e885b · 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 SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 81

Resolution: verified exact; arxiv_id, observed 2026-05-16T18:13:13.161923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6e6ff979-5360-4407-bdd2-8814285a8c00 · inbound

Amodal SAM: A Unified Amodal Segmentation Framework with Generalization cites this paper.

Amodal SAM: A Unified Amodal Segmentation Framework with Generalization SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T01:10:08.919937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 74913b44-d8d2-4aa5-9843-2f946a5b0f66 · inbound

SGP-SAM: Self-Gated Prompting for Transferring 3D Segment Anything Models to Lesion Segmentation cites this paper.

SGP-SAM: Self-Gated Prompting for Transferring 3D Segment Anything Models to Lesion Segmentation SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T06:51:46.308524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f8dc59af-8d27-461b-80a8-35024ba156e9 · inbound

CLIP-Guided SAM: Parameter-Efficient Semantic Conditioning for Promptable Segmentation cites this paper.

CLIP-Guided SAM: Parameter-Efficient Semantic Conditioning for Promptable Segmentation SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-30T12:34:39.036543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.