Paper Citation Record · LEDGER

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 26 inbound Pith citation observations for arXiv:2105.15203.

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

pith.paper-citation-record.v1
2105.15203 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 26 of 26 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T12:56:46.243979Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T00:47:30.288665Z

Reference resolution

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verified exact0
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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f972082e-51d5-4035-91f5-8cc3d7a27e0d · inbound

CarboFormer: A Lightweight Semantic Segmentation Architecture for Efficient Carbon Dioxide Detection Using Optical Gas Imaging cites this paper.

CarboFormer: A Lightweight Semantic Segmentation Architecture for Efficient Carbon Dioxide Detection Using Optical Gas Imaging SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:14.302147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d02d0c4f-649d-41b1-87ca-eadde096c214 · inbound

Breaking the Illusion of Security via Interpretation: Interpretable Vision Transformer Systems under Attack cites this paper.

Breaking the Illusion of Security via Interpretation: Interpretable Vision Transformer Systems under Attack SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T16:25:09.001151Z

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Observation 2901f6ab-db19-4409-bdab-e3345fa083b3 · inbound

A Multimodal Architecture for Endpoint Position Prediction in Team-based Multiplayer Games cites this paper.

A Multimodal Architecture for Endpoint Position Prediction in Team-based Multiplayer Games SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T13:27:31.134865Z

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Observation ac6f6113-9b50-478a-a4c9-0e68afad73df · inbound

TransForSeg: A Multitask Stereo ViT for Joint Stereo Segmentation and 3D Force Estimation in Catheterization cites this paper.

TransForSeg: A Multitask Stereo ViT for Joint Stereo Segmentation and 3D Force Estimation in Catheterization SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-05T12:28:45.509517Z

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Observation faa554f6-49ce-410d-bba2-7cacef75b27f · inbound

E-ARMOR: Edge case Assessment and Review of Multilingual Optical Character Recognition cites this paper.

E-ARMOR: Edge case Assessment and Review of Multilingual Optical Character Recognition SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T10:52:13.751107Z

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Observation 6e450a04-d1de-42d9-a674-50bf933c0d42 · inbound

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation cites this paper.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T17:57:09.291344Z

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Observation 4b5eda15-7e7d-446c-bde1-b48c322b751c · inbound

Human-Inspired Neuro-Symbolic World Modeling and Logic Reasoning for Interpretable Safe UAV Landing Site Assessment cites this paper.

Human-Inspired Neuro-Symbolic World Modeling and Logic Reasoning for Interpretable Safe UAV Landing Site Assessment SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T08:13:19.855162Z

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Observation 4e23cae0-b275-4ed3-85d8-96d20d387404 · inbound

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders cites this paper.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:53.653765Z

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Unavailable: canonical work link unavailable.

Observation 6d264bf6-8e43-4455-88c6-7e61112ce49c · inbound

SalFormer360: a transformer-based saliency estimation model for 360-degree videos cites this paper.

SalFormer360: a transformer-based saliency estimation model for 360-degree videos SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T04:35:16.332249Z

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Observation eda9702e-80a9-4d8a-b8c8-c217ca6990a6 · inbound

Geographically-Weighted Weakly Supervised Bayesian High-Resolution Transformer for 200m Resolution Pan-Arctic Sea Ice Concentration Mapping and Uncertainty Estimation using Sentinel-1, RCM, and AMSR2 Data cites this paper.

Geographically-Weighted Weakly Supervised Bayesian High-Resolution Transformer for 200m Resolution Pan-Arctic Sea Ice Concentration Mapping and Uncertainty Estimation using Sentinel-1, RCM, and AMSR2 Data SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-02T19:09:10.630630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8248a7f2-6f08-42b6-9e2c-40a48fe27a68 · inbound

SEM-ROVER: Semantic Voxel-Guided Diffusion for Large-Scale Driving Scene Generation cites this paper.

SEM-ROVER: Semantic Voxel-Guided Diffusion for Large-Scale Driving Scene Generation SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:50:49.138992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c67d59af-1986-443b-838b-af38d782d6a3 · inbound

Efficient Semantic Image Communication for Traffic Monitoring at the Edge cites this paper.

Efficient Semantic Image Communication for Traffic Monitoring at the Edge SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T09:26:01.059150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 76d85364-55e9-4761-8c67-9dda329c5037 · inbound

From Boundaries to Semantics: Prompt-Guided Multi-Task Learning for Petrographic Thin-section Segmentation cites this paper.

From Boundaries to Semantics: Prompt-Guided Multi-Task Learning for Petrographic Thin-section Segmentation SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T11:00:03.865660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 725db601-f64b-46be-9382-39d71e7a57b0 · inbound

Railway Artificial Intelligence Learning Benchmark (RAIL-BENCH): A Benchmark Suite for Perception in the Railway Domain cites this paper.

Railway Artificial Intelligence Learning Benchmark (RAIL-BENCH): A Benchmark Suite for Perception in the Railway Domain SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T19:06:11.026336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4740fd55-3b31-4f08-9b06-c5d62255d6ec · inbound

TripVVT: A Large-Scale Triplet Dataset and a Coarse-Mask Baseline for In-the-Wild Video Virtual Try-On cites this paper.

TripVVT: A Large-Scale Triplet Dataset and a Coarse-Mask Baseline for In-the-Wild Video Virtual Try-On SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 41

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T10:31:30.520844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c179780c-d779-4f06-aacf-e2e795e24a93 · inbound

Toward Visually Realistic Simulation: A Benchmark for Evaluating Robot Manipulation in Simulation cites this paper.

Toward Visually Realistic Simulation: A Benchmark for Evaluating Robot Manipulation in Simulation SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 45

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T20:26:10.858498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8c5d766c-3a43-4b11-839a-25be232607f4 · inbound

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

SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T13:53:19.875358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 20a73dc4-5d44-459e-8f9a-e6b32b5e6ba4 · inbound

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

SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T07:44:02.845993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f31ddd92-f0ae-454d-bbe7-dab5aec38827 · inbound

Efficient 3D Content Reconstruction and Generation cites this paper.

Efficient 3D Content Reconstruction and Generation SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 284

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T11:43:15.527157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 27822676-6eb1-4648-8696-d6a1a77107f5 · inbound

Revitalizing Dense Material Segmentation: Stabilized Vision Transformers and the Generalization Paradox cites this paper.

Revitalizing Dense Material Segmentation: Stabilized Vision Transformers and the Generalization Paradox SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-25T04:40:24.535294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 67a01517-e385-4e2b-a7cc-2e715610bf3b · inbound

A Simulation Platform for Flapping-Wing Vehicles cites this paper.

A Simulation Platform for Flapping-Wing Vehicles SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T23:26:22.142906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8ce69973-9bf3-4cf3-8c27-e2e0bfc7d1a6 · inbound

A Joint Finite-Sample Certificate for Adaptive Selective Conformal Risk Control cites this paper.

A Joint Finite-Sample Certificate for Adaptive Selective Conformal Risk Control SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T22:37:26.418112Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b901608b-ea41-4f54-8a76-5017a3a93b74 · inbound

Zero-Parameter Geometric Gating for Temporally Stable Low-Altitude UAV Video Semantic Segmentation cites this paper.

Zero-Parameter Geometric Gating for Temporally Stable Low-Altitude UAV Video Semantic Segmentation SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:47:30.290463Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1a7609da-2673-46bd-afc4-849359bf6f78 · inbound

Milo, a Fully Autonomous Indoor/Outdoor Robotic Guide Dog cites this paper.

Milo, a Fully Autonomous Indoor/Outdoor Robotic Guide Dog SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T12:31:21.484109Z

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Unavailable: canonical work link unavailable.

Observation f378ee90-999a-425e-a013-558ae154b62c · inbound

SARATR-X-v2: Scale-Aware Structural Pre-Training for SAR Foundation Models cites this paper.

SARATR-X-v2: Scale-Aware Structural Pre-Training for SAR Foundation Models SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-01T03:22:13.632220Z

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Observation 552700df-1eed-40eb-a5d1-dbc0649bad1c · inbound

Modeling E-Bike Route Choice in Washington, DC: A Path Size Logit Approach cites this paper.

Modeling E-Bike Route Choice in Washington, DC: A Path Size Logit Approach SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-08T12:56:46.243979Z

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Unavailable: canonical work link unavailable.