Paper Citation Record · LEDGER

Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2401.02955.

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

pith.paper-citation-record.v1
2401.02955 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:45:19.885988Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T15:18:47.926547Z

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 7b5862a7-b2f2-47e2-953d-119483416fda · inbound

High-Quality Mask Tuning Matters for Open-Vocabulary Segmentation cites this paper.

High-Quality Mask Tuning Matters for Open-Vocabulary Segmentation Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-11T14:58:17.308157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ca79fc6-8334-484c-98f6-9a6c41bd07be · inbound

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation cites this paper.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T12:28:54.555338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95a6ee87-e9df-4547-a246-92d19ab07af1 · inbound

DynAlign: Unsupervised Dynamic Taxonomy Alignment for Cross-Domain Segmentation cites this paper.

DynAlign: Unsupervised Dynamic Taxonomy Alignment for Cross-Domain Segmentation Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T13:35:59.721446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19eef83b-d1c8-43a2-8c37-c6d1299f2904 · inbound

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation cites this paper.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.885988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 632dfc9b-daa1-42d8-bbc5-a7939c911bd0 · inbound

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation cites this paper.

Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:18:47.960712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c046d5e2-c36e-4f2d-a4c0-a106ffaf75fb · inbound

SAM-MI: A Mask-Injected Framework for Enhancing Open-Vocabulary Semantic Segmentation with SAM cites this paper.

SAM-MI: A Mask-Injected Framework for Enhancing Open-Vocabulary Semantic Segmentation with SAM Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T20:27:24.714050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.