Paper Citation Record · LEDGER

A feature refinement module for light-weight semantic segmentation network

As of 21 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2412.08670.

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

pith.paper-citation-record.v1
2412.08670 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:19:42.576493Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:19:42.396150Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T18:19:42.725897Z

Reference resolution

31 of 31 outbound references displayed

verified exact2
verified fuzzy25
unresolved4
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1fc4dd45-e94d-4175-88e5-8ec23dcc3f73 · outbound

This paper cites A feature refinement module for light-weight semantic segmentation network.

A feature refinement module for light-weight semantic segmentation network A feature refinement module for light-weight semantic segmentation network

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-11T18:19:42.730993Z

Source-reported events for the cited work

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Observation 44a83a02-11e9-40e2-a902-aeb3e5c85849 · outbound

This paper cites We first present the whole framework of the proposed method.

A feature refinement module for light-weight semantic segmentation network We first present the whole framework of the proposed method

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:43.214226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7b38c46b-ce4a-4c52-bd40-5060828e4885 · outbound

This paper cites Specifi- cally, FRM firstly pools the multi-stage features with differ- ent scales to the same size H 32 × W 32 and concatenates them together.

A feature refinement module for light-weight semantic segmentation network Specifi- cally, FRM firstly pools the multi-stage features with differ- ent scales to the same size H 32 × W 32 and concatenates them together

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:43.193197Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5e5679d1-d207-4ec8-9bd1-f505a931061b · outbound

This paper cites Datasets The performance of the proposed method is evaluated on two semantic segmentation datasets, Cityscapes [15] and Bdd100K [16].

A feature refinement module for light-weight semantic segmentation network Datasets The performance of the proposed method is evaluated on two semantic segmentation datasets, Cityscapes [15] and Bdd100K [16]

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:43.164303Z

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Observation 19c440cf-7647-458a-9ab9-d1e97d5bb8eb · outbound

This paper cites FRM extracts rich semantics by aggregating multi-stage feature maps from the light-weight backbone.

A feature refinement module for light-weight semantic segmentation network FRM extracts rich semantics by aggregating multi-stage feature maps from the light-weight backbone

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:43.145356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation de54691b-4312-44fe-b6b6-67ea9a2e5ca3 · outbound

This paper cites Real-time fusion network for RGB-D semantic segmentation incorporating unexpected obstacle detection for road-driving images,.

A feature refinement module for light-weight semantic segmentation network Real-time fusion network for RGB-D semantic segmentation incorporating unexpected obstacle detection for road-driving images,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:43.127821Z

Source-reported events for the cited work

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Observation 65603d43-2a4e-4d4d-a3c2-3ca84600373a · outbound

This paper cites Real- time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in cnns,.

A feature refinement module for light-weight semantic segmentation network Real- time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in cnns,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:43.111534Z

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Observation e2fdd57e-264a-4f21-a6f1-3a298a806679 · outbound

This paper cites Global and local feature reconstruction for medical im- age segmentation,.

A feature refinement module for light-weight semantic segmentation network Global and local feature reconstruction for medical im- age segmentation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:43.093562Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 421d7d55-1214-426b-a104-fdc9d2142aea · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

A feature refinement module for light-weight semantic segmentation network Fully convolutional networks for semantic segmentation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:43.075407Z

Source-reported events for the cited work

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Observation 5a38400e-1bff-484d-b69d-e79510eddd21 · outbound

This paper cites Contextnet: Exploring context and detail for semantic segmentation in real-time,.

A feature refinement module for light-weight semantic segmentation network Contextnet: Exploring context and detail for semantic segmentation in real-time,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:43.057256Z

Source-reported events for the cited work

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Observation 3a0b0a6b-2689-45e1-9f8e-c795e1efd6d5 · outbound

This paper cites Dfanet: Deep feature aggregation for real-time semantic seg- mentation,.

A feature refinement module for light-weight semantic segmentation network Dfanet: Deep feature aggregation for real-time semantic seg- mentation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:43.039866Z

Source-reported events for the cited work

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Observation 810532a4-00a5-4c37-a941-955c1b653e9f · outbound

This paper cites ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.

A feature refinement module for light-weight semantic segmentation network ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

Reference 12

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Source-reported events for the cited work

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Observation b1f212af-0fb5-4a33-b0c0-f4e570bcb60a · outbound

This paper cites Cgnet: A light-weight context guided network for semantic segmentation,.

A feature refinement module for light-weight semantic segmentation network Cgnet: A light-weight context guided network for semantic segmentation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:43.022894Z

Source-reported events for the cited work

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Observation 9c5cb42b-92b7-4fe6-8675-ee34854b7a10 · outbound

This paper cites ParseNet: Looking Wider to See Better.

A feature refinement module for light-weight semantic segmentation network ParseNet: Looking Wider to See Better

Reference 14

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Source-reported events for the cited work

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Observation 93588502-e916-4560-a8f3-6bf5120fd83d · outbound

This paper cites Pyramid scene parsing network,.

A feature refinement module for light-weight semantic segmentation network Pyramid scene parsing network,

Reference 15

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Source-reported events for the cited work

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Observation da67496f-7e6e-49a1-b286-d4f183c7bbc8 · outbound

This paper cites Deep dual-resolution networks for real-time and accurate se- mantic segmentation of traffic scenes,.

A feature refinement module for light-weight semantic segmentation network Deep dual-resolution networks for real-time and accurate se- mantic segmentation of traffic scenes,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:42.976399Z

Source-reported events for the cited work

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Observation 81d62f82-f0ce-4612-a87b-e9a98ced03ab · outbound

This paper cites Se- mantic flow for fast and accurate scene parsing,.

A feature refinement module for light-weight semantic segmentation network Se- mantic flow for fast and accurate scene parsing,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:42.956562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cbe53c1c-9d4d-40b2-850b-baf346bf597e · outbound

This paper cites Disentangled non-local neural net- works,.

A feature refinement module for light-weight semantic segmentation network Disentangled non-local neural net- works,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:42.936340Z

Source-reported events for the cited work

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Observation 2fdd4ec5-46a5-4ee5-a2e9-fa2ede426ca1 · outbound

This paper cites Exploring cross-image pixel contrast for semantic segmentation,.

A feature refinement module for light-weight semantic segmentation network Exploring cross-image pixel contrast for semantic segmentation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:42.919209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c3c0ddae-9e06-4e03-a13f-5d17f5a68df2 · outbound

This paper cites The cityscapes dataset for se- mantic urban scene understanding,.

A feature refinement module for light-weight semantic segmentation network The cityscapes dataset for se- mantic urban scene understanding,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:42.899672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0d8156c0-5629-452f-8a68-71f1ad3a6a64 · outbound

This paper cites BDD100K: A diverse driving dataset for heterogeneous mul- titask learning,.

A feature refinement module for light-weight semantic segmentation network BDD100K: A diverse driving dataset for heterogeneous mul- titask learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:42.880663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 95b87d25-3777-4a7c-a1fd-6a2709613092 · outbound

This paper cites Segformer: Simple and effi- cient design for semantic segmentation with transformers,.

A feature refinement module for light-weight semantic segmentation network Segformer: Simple and effi- cient design for semantic segmentation with transformers,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:42.863231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c6c6bba4-c887-4594-8ca2-a2558b6f98b9 · outbound

This paper cites Dy- namic neural representational decoders for high-resolution se- mantic segmentation,.

A feature refinement module for light-weight semantic segmentation network Dy- namic neural representational decoders for high-resolution se- mantic segmentation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:42.842552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 070b0164-afcb-4320-bc1e-c2616dcb0a9f · outbound

This paper cites Fast and accurate scene parsing via bi-direction alignment networks,.

A feature refinement module for light-weight semantic segmentation network Fast and accurate scene parsing via bi-direction alignment networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:42.822510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6bdd4fd7-765d-4b74-9dc5-3c5cae7cb255 · outbound

This paper cites Stage-aware feature alignment network for real-time seman- tic segmentation of street scenes,.

A feature refinement module for light-weight semantic segmentation network Stage-aware feature alignment network for real-time seman- tic segmentation of street scenes,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:42.802578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 044e0f2f-592d-49d9-a57d-253a900a6e46 · outbound

This paper cites Learning implicit fea- ture alignment function for semantic segmentation,.

A feature refinement module for light-weight semantic segmentation network Learning implicit fea- ture alignment function for semantic segmentation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:42.784880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 610649cb-b21b-4a2b-ac6c-40fd5ebdcee4 · outbound

This paper cites RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer.

A feature refinement module for light-weight semantic segmentation network RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-11T18:19:42.672045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 24fda8ae-b7cf-4910-9e38-eaba8ad45d63 · outbound

This paper cites PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers.

A feature refinement module for light-weight semantic segmentation network PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T18:19:42.548798Z

Source-reported events for the cited work

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Observation 3b8e1595-3bb5-4752-8e17-503c583e682c · outbound

This paper cites Visual Attention Network.

A feature refinement module for light-weight semantic segmentation network Visual Attention Network

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T18:19:42.562878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50a322e1-fbc5-403a-8325-dd4a3ba77592 · outbound

This paper cites Cars can’t fly up in the sky: Improving urban-scene segmentation via height-driven attention networks,.

A feature refinement module for light-weight semantic segmentation network Cars can’t fly up in the sky: Improving urban-scene segmentation via height-driven attention networks,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:42.765436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d2199732-bdf9-488f-a410-a0ba534d9da9 · outbound

This paper cites Pointflow: Flowing semantics through points for aerial im- age segmentation,.

A feature refinement module for light-weight semantic segmentation network Pointflow: Flowing semantics through points for aerial im- age segmentation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:19:42.747120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 1fc4dd45-e94d-4175-88e5-8ec23dcc3f73 · inbound