Paper Citation Record · LEDGER

Semantic-Fast-SAM: Efficient Semantic Segmenter

As of 15 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2604.20169.

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

pith.paper-citation-record.v1
2604.20169 v2

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T01:13:45.407798Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

16 of 16 outbound references displayed

verified exact4
verified fuzzy11
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b7f15931-9f3a-41fc-9eae-00b7c2e03e5a · outbound

This paper cites Segment Anything.

Semantic-Fast-SAM: Efficient Semantic Segmenter Segment Anything

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-11T13:41:06.215226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2e5b7fdb-f99a-4dc6-8bab-ba8ae53fb7eb · outbound

This paper cites Semantic segment anything.

Semantic-Fast-SAM: Efficient Semantic Segmenter Semantic segment anything

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T09:57:49.214632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ff23c27b-5284-4970-b978-43cb0d38c995 · outbound

This paper cites Oneformer: One transformer to rule universal image segmentation.

Semantic-Fast-SAM: Efficient Semantic Segmenter Oneformer: One transformer to rule universal image segmentation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T09:57:49.226801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation eee7046a-2113-4e11-9d31-4f23c7fcc0ed · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

Semantic-Fast-SAM: Efficient Semantic Segmenter Masked-attention mask transformer for universal image segmentation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T09:57:49.218825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 98fd7412-bb30-40a4-a4e8-9fbc0ceefaa2 · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

Semantic-Fast-SAM: Efficient Semantic Segmenter Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T09:57:49.222938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8b073701-c5a7-4119-bf3c-fe81d4c4b4bc · outbound

This paper cites Learning transferable visual models from natural language supervision.

Semantic-Fast-SAM: Efficient Semantic Segmenter Learning transferable visual models from natural language supervision

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T09:57:49.189531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 55a3f923-37e9-4e35-a81b-a248783f07ea · outbound

This paper cites Fast Segment Anything.

Semantic-Fast-SAM: Efficient Semantic Segmenter Fast Segment Anything

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T13:41:06.252350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 11523d5a-9b7d-451a-9907-a66078e7a0fc · outbound

This paper cites Yolact: Real-time instance segmentation.

Semantic-Fast-SAM: Efficient Semantic Segmenter Yolact: Real-time instance segmentation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T09:57:49.201863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation decc9aa0-38d0-4de6-82fb-ae13472c2225 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

Semantic-Fast-SAM: Efficient Semantic Segmenter The cityscapes dataset for semantic urban scene understanding

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T09:57:49.185313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e203c423-3f44-485e-85f6-1598acf98550 · outbound

This paper cites Semantic understanding of scenes through the ade20k dataset.

Semantic-Fast-SAM: Efficient Semantic Segmenter Semantic understanding of scenes through the ade20k dataset

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T09:57:49.206980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 05edc81d-f30a-419a-b4a8-f642330433ef · outbound

This paper cites YOLOv4: Optimal Speed and Accuracy of Object Detection.

Semantic-Fast-SAM: Efficient Semantic Segmenter YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-12T14:34:24.827997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 855b60f0-c54e-4117-9fe8-93283409f06e · outbound

This paper cites Image segmentation using text and image prompts.

Semantic-Fast-SAM: Efficient Semantic Segmenter Image segmentation using text and image prompts

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T09:57:49.193653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 57101132-ad04-474e-9ad8-1c8bbbce64cf · outbound

This paper cites Groupvit: Semantic segmentation emerges from text supervision.

Semantic-Fast-SAM: Efficient Semantic Segmenter Groupvit: Semantic segmentation emerges from text supervision

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T09:57:49.198069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2b78c372-0406-4e51-ab7d-d2e32fe1ede3 · outbound

This paper cites Open-vocabulary universal image segmen- tation with maskclip.

Semantic-Fast-SAM: Efficient Semantic Segmenter Open-vocabulary universal image segmen- tation with maskclip

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T09:57:49.210735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d8b63e34-f45d-4a21-8368-8a2ff8cc00b8 · outbound

This paper cites Ultra-light test-time adaptation for Vision–Language models.

Semantic-Fast-SAM: Efficient Semantic Segmenter Ultra-light test-time adaptation for Vision–Language models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:41:06.225253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7023774c-8c93-4c3a-866a-2559b0e96d87 · outbound

This paper cites OT-UVGS: Revisiting UV Mapping for Gaussian Splatting as a Capacity Allocation Problem.

Semantic-Fast-SAM: Efficient Semantic Segmenter OT-UVGS: Revisiting UV Mapping for Gaussian Splatting as a Capacity Allocation Problem

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-11T13:41:06.244228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.