Paper Citation Record · LEDGER

SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2209.08575.

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

pith.paper-citation-record.v1
2209.08575 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 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 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:08:59.813343Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T09:41:38.229386Z

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 50beec7a-4d24-4ec5-abce-835b36d023ac · inbound

Vision Transformers Need Registers cites this paper.

Vision Transformers Need Registers SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

Reference 282

Resolution: verified exact; arxiv_id, observed 2026-05-13T09:41:38.231194Z

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 c2b07af7-2ff4-49a7-884f-9abef142d929 · inbound

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery cites this paper.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T21:08:59.813343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation feadfcc1-fa40-435e-9f00-49e411c620cf · inbound

ROSE: Revolutionizing Open-Set Dense Segmentation with Patch-Wise Perceptual Large Multimodal Model cites this paper.

ROSE: Revolutionizing Open-Set Dense Segmentation with Patch-Wise Perceptual Large Multimodal Model SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T10:10:40.764202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15e790ef-d0af-43e9-b067-d0de908d2edb · inbound

UNet--: Memory-Efficient and Feature-Enhanced Network Architecture based on U-Net with Reduced Skip-Connections cites this paper.

UNet--: Memory-Efficient and Feature-Enhanced Network Architecture based on U-Net with Reduced Skip-Connections SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T04:54:35.474798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaee6ab0-2597-48e9-b77a-5f4ba76dd2cf · inbound

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations cites this paper.

RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T00:15:11.623367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 001fc0c4-4cc8-4a9c-8668-44ed5c02ae10 · inbound

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation cites this paper.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T22:26:32.573101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35ccdc3d-efe7-4ea4-ac59-16cb297572e0 · inbound

Data-driven Detection and Evaluation of Damages in Concrete Structures: Using Deep Learning and Computer Vision cites this paper.

Data-driven Detection and Evaluation of Damages in Concrete Structures: Using Deep Learning and Computer Vision SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:39.879080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a44ab6b-9987-48b5-a1e9-49538f1425e7 · 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 SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:13.173759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f61fee0-c88c-427d-ab0f-cdefc5db99dc · inbound

MetaScope: Optics-Driven Neural Network for Ultra-Micro Metalens Endoscopy cites this paper.

MetaScope: Optics-Driven Neural Network for Ultra-Micro Metalens Endoscopy SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T04:24:20.787001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3212336-d273-4e6d-92b4-55f563fcea01 · inbound

2K Retrofit: Entropy-Guided Efficient Sparse Refinement for High-Resolution 3D Geometry Prediction cites this paper.

2K Retrofit: Entropy-Guided Efficient Sparse Refinement for High-Resolution 3D Geometry Prediction SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-13T21:45:59.210064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c74de925-a79d-4013-a6e9-d167a5fd73ff · inbound

H3D-MarNet: Wavelet-Guided Dual-Path Learning for Metal Artifact Suppression and CT Modality Transformation for Radiotherapy Workflows cites this paper.

H3D-MarNet: Wavelet-Guided Dual-Path Learning for Metal Artifact Suppression and CT Modality Transformation for Radiotherapy Workflows SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:27:24.206427Z

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.