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Paper Citation Record · LEDGER

Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2411.09219.

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

pith.paper-citation-record.v1
2411.09219 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-13T19:56:11.865786Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T11:54:09.147710Z

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 cba7ff5d-3d7b-4b5e-afee-f79262bb3edd · inbound

RADSeg: Unleashing Parameter and Compute Efficient Zero-Shot Open-Vocabulary Segmentation Using Agglomerative Models cites this paper.

RADSeg: Unleashing Parameter and Compute Efficient Zero-Shot Open-Vocabulary Segmentation Using Agglomerative Models Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-05-17T05:24:04.703470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-17T05:23:11.261860Z digest=sha256:c99b067c8c5ead3a999951dc92cbc08e417e1ff1c8e9a03640d55db29198f9ce

Observation 7612ddd9-d3f9-474a-a908-8eb66604cd2f · inbound

Monocular Open Vocabulary Occupancy Prediction for Indoor Scenes cites this paper.

Monocular Open Vocabulary Occupancy Prediction for Indoor Scenes Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-21T11:54:09.150460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-21T11:50:37.423645Z digest=sha256:8936e525336f76888aa099cb9e71f321fa4dea5d705cdcfa2a29536376ce0071

Observation 1bcfea73-3539-4434-9e66-073024f21e02 · inbound

Low-Frequency Stochastic Gravitational-Wave Background in Gaia DR3 catalog cites this paper.

Low-Frequency Stochastic Gravitational-Wave Background in Gaia DR3 catalog Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation

Reference 39

Resolution
unresolved
no resolver link, observed 2026-07-13T19:56:11.865786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T19:56:11.865786Z digest=sha256:b77845e4b868963096233a32660464b4d722b18988dd2044cdff9134928ef8ab

Observation 4b37898c-2545-4031-80c5-4018293fea6f · inbound

Cross-Attentive Multiview Fusion of Vision-Language Embeddings cites this paper.

Cross-Attentive Multiview Fusion of Vision-Language Embeddings Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation

Reference 31

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T11:26:01.917938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T14:56:08.616492Z digest=sha256:53aba058714458bc19f3640b8d26d5375699cb1e2814d2aea8ff3d061dad729f

Observation 7aa5f4ca-726f-4dd1-ac9e-b4c27c1b051d · inbound

RADIO-ViPE: Online Tightly Coupled Multi-Modal Fusion for Open-Vocabulary Semantic SLAM in Dynamic Environments cites this paper.

RADIO-ViPE: Online Tightly Coupled Multi-Modal Fusion for Open-Vocabulary Semantic SLAM in Dynamic Environments Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-05-11T23:36:28.306329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-07T16:40:25.188693Z digest=sha256:e95fe811029f39405c59f4526e0a8312c9c9450f4e8f5f83aa3dc78aad0f17f8

Observation ae34fb32-47bf-4075-a8a5-9f3310f67f2e · inbound

FreeOcc: Training-Free Embodied Open-Vocabulary Occupancy Prediction cites this paper.

FreeOcc: Training-Free Embodied Open-Vocabulary Occupancy Prediction Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-05-12T10:31:29.018285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-07T05:53:06.790344Z digest=sha256:5f896ce843e2a08f4f777295a7461c4d27989b69e69264121d4acbb56abf8d0b

Observation 72a6353b-1662-4632-a783-749a5b059df7 · inbound

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

SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation

Reference 80

Resolution
metadata mismatch
arxiv_id, observed 2026-05-20T13:53:19.872199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-20T13:51:35.769341Z digest=sha256:f333a22d814fd9ee6270207e3fab4fbc991b36c330cd46caeb1530ac35b1ab19

Observation 94928779-5378-415d-9418-9d115843646b · inbound

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

SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation

Reference 80

Resolution
metadata mismatch
arxiv_id, observed 2026-05-21T07:44:02.841250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-21T07:43:28.627414Z digest=sha256:bb3ca3ac3b5c0e5396ec56cf25fc2064a546e00ab5590c831e97cfab1e135c66