Paper Citation Record · LEDGER

Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 29 inbound Pith citation observations for arXiv:2305.13310.

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

pith.paper-citation-record.v1
2305.13310 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 29 of 29 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:55:41.587569Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-07T21:34:09.311757Z

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 4200dc5d-dbae-48c5-a905-f59c7a3e14df · inbound

Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation cites this paper.

Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-12T21:02:00.933456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4146c1b4-9cbd-452a-bcdc-b8d519f7a2bd · inbound

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals cites this paper.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.686681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec5d69f2-c1c9-4403-be31-49853750e33f · inbound

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain cites this paper.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:02.894792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32880388-945d-48d9-82c6-95cf2d7757b8 · inbound

Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes cites this paper.

Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-12T04:37:38.544303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61a607d5-92a5-413b-b502-c1d323c807dc · inbound

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering cites this paper.

SAMIC: Segment Anything with In-Context Spatial Prompt Engineering Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T14:27:23.695896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79c4e1fb-2b6e-4a1a-be54-68a2d83666ec · inbound

SAM-Aware Graph Prompt Reasoning Network for Cross-Domain Few-Shot Segmentation cites this paper.

SAM-Aware Graph Prompt Reasoning Network for Cross-Domain Few-Shot Segmentation Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T23:00:19.161177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8df3bab2-7cab-45a8-95f8-629e54f2d026 · inbound

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models cites this paper.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T16:13:53.206446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 587a9033-7773-4190-adda-f6a12ad73cdc · inbound

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection cites this paper.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T23:55:41.587569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cb52e1d-d651-48db-9115-d528c14678ba · inbound

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation cites this paper.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T23:45:53.057371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b55bf842-efc8-4bcc-91fa-969e41825801 · inbound

Unlocking the Power of SAM 2 for Few-Shot Segmentation cites this paper.

Unlocking the Power of SAM 2 for Few-Shot Segmentation Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:29.451756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c3886c2-bc8f-441b-9d07-f3cfb0fa013e · inbound

ProSAM: Enhancing the Robustness of SAM-based Visual Reference Segmentation with Probabilistic Prompts cites this paper.

ProSAM: Enhancing the Robustness of SAM-based Visual Reference Segmentation with Probabilistic Prompts Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T22:23:49.774512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8ac10fd-a231-4e2b-87d5-c4c685a1b3b3 · inbound

One Polyp Identifies All: One-Shot Polyp Segmentation with SAM via Cascaded Priors and Iterative Prompt Evolution cites this paper.

One Polyp Identifies All: One-Shot Polyp Segmentation with SAM via Cascaded Priors and Iterative Prompt Evolution Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T15:18:56.289550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06341536-66a8-4205-8e93-d25e47bfd669 · inbound

RPG-SAM: Reliability-Weighted Prototypes and Geometric Adaptive Threshold Selection for Training-Free One-Shot Polyp Segmentation cites this paper.

RPG-SAM: Reliability-Weighted Prototypes and Geometric Adaptive Threshold Selection for Training-Free One-Shot Polyp Segmentation Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-15T15:20:08.179556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9f0cdfb4-1c0f-4eb8-afb2-073de2d5d40f · inbound

Example-Based Object Detection cites this paper.

Example-Based Object Detection Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:50:41.207241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1d385d8a-f354-4233-ab2f-66a73847d2ae · inbound

SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation cites this paper.

SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:53:19.826805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 65e66ad0-a79e-4cfa-901c-f0fe34cc5986 · inbound

SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation cites this paper.

SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-21T07:44:02.807494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 03d91bee-1469-4c2a-a3c4-0b57cad86331 · inbound

Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines cites this paper.

Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T20:47:23.243409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1d24f145-2f14-4a5a-b955-164c355abfc7 · inbound

Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline cites this paper.

Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-07-02T20:57:23.249975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a4b873cd-4898-4ec0-83e3-fcbe4178304b · inbound

Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision cites this paper.

Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:17:28.729038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2976a6c0-2ad2-4d9d-b807-167b68bf268e · inbound

Training-free Cross-domain Few-shot Segmentation via Robust Semantic Representation and Matching cites this paper.

Training-free Cross-domain Few-shot Segmentation via Robust Semantic Representation and Matching Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-04T16:09:56.413471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 46681f07-7b73-4224-9256-4e43cdb18e2f · inbound

Mask to Concept: Auto-Promptable SAM3 via Efficient Test-Time Concept Embedding Search for Few-Shot Annotation cites this paper.

Mask to Concept: Auto-Promptable SAM3 via Efficient Test-Time Concept Embedding Search for Few-Shot Annotation Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T12:59:52.435391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5797632a-2a96-4c0b-8f44-f5b27b1d0f69 · inbound

Mask to Concept: Auto-Promptable SAM3 via Efficient Test-Time Concept Embedding Search for Few-Shot Annotation cites this paper.

Mask to Concept: Auto-Promptable SAM3 via Efficient Test-Time Concept Embedding Search for Few-Shot Annotation Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T09:25:41.398481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fced147a-75bd-4395-a3a9-041a75785951 · inbound

ExACT: Exemplar-Driven Calibrated Refinement for Training-Free Visual Grounding in Remote Sensing Images cites this paper.

ExACT: Exemplar-Driven Calibrated Refinement for Training-Free Visual Grounding in Remote Sensing Images Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-06-30T09:54:35.201340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6746a5a1-6faa-40ed-b6b3-0a5c7fb1ba88 · inbound

FROST: Training-Free Few-Shot Segmentation with Frozen Features and Nonparametric Statistics cites this paper.

FROST: Training-Free Few-Shot Segmentation with Frozen Features and Nonparametric Statistics Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-01T08:55:35.955239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1f7e637d-b78e-4c98-96f5-ebca4dbdbcbd · inbound

Repurposing CLIP to Localize at Pixel Level cites this paper.

Repurposing CLIP to Localize at Pixel Level Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 5

Resolution: metadata mismatch; local_arxiv, observed 2026-07-07T21:34:09.312983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5f5e2f1c-e0a4-4efe-8f2c-bdd43a75dd3d · inbound

Repurposing CLIP to Localize at Pixel Level cites this paper.

Repurposing CLIP to Localize at Pixel Level Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-11T07:30:42.555053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d81e2493-665b-4c10-afc4-9795a57b2cd5 · inbound

GFR-SAM: Training-Free Referring Camouflaged Object Segmentation via Cross-Image Prompting cites this paper.

GFR-SAM: Training-Free Referring Camouflaged Object Segmentation via Cross-Image Prompting Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-14T03:33:38.303340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21b2bdfb-645a-4b98-9870-efe56376e2c9 · inbound

Memory-Supported Synergistic Adaptation for Training-Free Test-Time Medical Image Segmentation cites this paper.

Memory-Supported Synergistic Adaptation for Training-Free Test-Time Medical Image Segmentation Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T17:19:30.547759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a7b969a-510c-4b61-8924-5be0fe81e25e · inbound

Few-Shot Open-Vocabulary Remote Sensing Segmentation via Textual Inversion cites this paper.

Few-Shot Open-Vocabulary Remote Sensing Segmentation via Textual Inversion Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T02:09:22.765762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.