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Paper Citation Record · LEDGER

GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain

As of 17 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2606.15937.

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

pith.paper-citation-record.v1
2606.15937 v2

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T03:29:04.409304Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

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  • verified fuzzy0
  • unresolved14
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 27a03c4b-bbaa-4d58-bf4b-d0d741d2d664 · outbound

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

GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain Masked- attention mask transformer for universal image segmentation,

Reference 1

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source=pdf_text observed=2026-06-27T03:29:04.409304Z digest=sha256:7a2a7eaa8c6fa7ed17ffec38c6c11e056db4ca76aa67f6e0b2ac0931d3be19cc

Observation e2d14c5c-2618-4d31-9326-7b2a77db1c9b · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 2

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source=pdf_text observed=2026-06-27T03:29:04.409304Z digest=sha256:2f43e86c7fa94193619a25896e4506be1935cbed464d118122857d7f80c2772e

Observation 6859ab8d-0430-4c39-b121-350f1c7a08e1 · outbound

This paper cites End-to-end object detection with transformers,.

GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain End-to-end object detection with transformers,

Reference 3

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source=pdf_text observed=2026-06-27T03:29:04.409304Z digest=sha256:3675a554a9fa41f8fdb1ca637dbdfa49f95d0cb5b236cc8f4b2d0233030e8964

Observation d99eb2f0-d70a-40b0-aaec-898847a9858f · outbound

This paper cites Deformable DETR: Deformable transformers for end-to-end object detection,.

GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain Deformable DETR: Deformable transformers for end-to-end object detection,

Reference 4

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source=pdf_text observed=2026-06-27T03:29:04.409304Z digest=sha256:656b6e634b8b15905cb478e1229ac3dfff586d95e31e2727acdc40f04054e34c

Observation 2d053672-2a46-444c-b021-7d0879257317 · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 5

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local_arxiv, observed 2026-07-03T17:58:47.480851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-27T03:29:04.409304Z digest=sha256:7afc7009a4f797b050e40e819f1a6c51592d0d602d296c9d583b16c3bd2a0c1d

Observation 7a6adb12-0c13-4aab-8d84-875787db0de9 · outbound

This paper cites CBAM: Convolutional block attention module,.

GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain CBAM: Convolutional block attention module,

Reference 6

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source=pdf_text observed=2026-06-27T03:29:04.409304Z digest=sha256:464c1179054ee5f7b237f3eed7c9e5fa3bcc9e433a6c34dcf92dcfb6d30fb2d8

Observation 863bb49e-221a-4a54-85ad-34e1fc1ebd1c · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain Fully convolutional networks for semantic segmentation,

Reference 7

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source=pdf_text observed=2026-06-27T03:29:04.409304Z digest=sha256:65ff029a32daa8c330bc96d80e0264cd78fb52afde994ddc2e3490fbe210db5b

Observation 9f9db498-a8db-4482-8be6-1fe81427c73d · outbound

This paper cites SegFormer: Simple and efficient design for semantic segmentation with transformers,.

GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain SegFormer: Simple and efficient design for semantic segmentation with transformers,

Reference 8

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source=pdf_text observed=2026-06-27T03:29:04.409304Z digest=sha256:9c4d3ead7f5bd2b564041826b60984e0655fefd5c5c659b7be097ab27580f126

Observation 98f47bd7-59be-4d00-a99c-6139e1f540cb · outbound

This paper cites The Cityscapes dataset for semantic urban scene understanding,.

GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain The Cityscapes dataset for semantic urban scene understanding,

Reference 9

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source=pdf_text observed=2026-06-27T03:29:04.409304Z digest=sha256:ed6f0fe65dedef6d8126e342b0f3b595f148cfd5c0f00b1ff6204755383641be

Observation 95adf80b-45af-418a-840d-e51b96e18283 · outbound

This paper cites Scene parsing through ADE20K dataset,.

GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain Scene parsing through ADE20K dataset,

Reference 10

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Observation 345c7a11-078f-4233-a1ee-ae54c72187fa · outbound

This paper cites Distribution-balanced loss for multi-label classification in long-tailed datasets,.

GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain Distribution-balanced loss for multi-label classification in long-tailed datasets,

Reference 11

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Observation bb41f930-9e60-4034-9ffd-b40c59e6525d · outbound

This paper cites LVIS: A dataset for large vocabulary instance segmentation,.

GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain LVIS: A dataset for large vocabulary instance segmentation,

Reference 12

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source=pdf_text observed=2026-06-27T03:29:04.409304Z digest=sha256:680e532997162ead574f5694d7a756833640184376e62f1fd028d1dcd133fbf7

Observation 30baa369-79f1-4e9e-b710-ca48bc1798c5 · outbound

This paper cites Simple copy-paste is a strong data augmentation method for instance segmentation,.

GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain Simple copy-paste is a strong data augmentation method for instance segmentation,

Reference 13

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source=pdf_text observed=2026-06-27T03:29:04.409304Z digest=sha256:f2089cd39c0e74d0f8ecc7db7a52c568710dfdbc131738280f8ef81bbf923ac7

Observation e332d126-c43a-4947-b9e9-d7df58587030 · outbound

This paper cites Decoupled weight decay regularization,.

GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain Decoupled weight decay regularization,

Reference 14

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source=pdf_text observed=2026-06-27T03:29:04.409304Z digest=sha256:826490ae39547af6a5dc311101d9f72e52230b62acbc6bf144d76b6137a12d6d

Observation 1455a575-19cd-42a0-90ea-98d3ae846490 · outbound

This paper cites The GOOSE dataset for perception in unstructured environments,.

GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain The GOOSE dataset for perception in unstructured environments,

Reference 15

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source=pdf_text observed=2026-06-27T03:29:04.409304Z digest=sha256:d47e38a3e292fe176a1d5224e43f8d4ff170f1145e12a342b49c53f64b4c9022

Observation 9bee5128-e268-44db-84b2-a0282dba96f5 · outbound

This paper cites Excavating in the wild: The goose-ex dataset for semantic segmentation,.

GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain Excavating in the wild: The goose-ex dataset for semantic segmentation,

Reference 16

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arxiv_id, observed 2026-07-03T17:58:47.483531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-27T03:29:04.409304Z digest=sha256:df322121f80ee0b7e9d31850ccedf582d1a90a2fa1989e6f9e677175d67bdfdb

Pith citing papers

No inbound Pith citation observations are available.