Paper Citation Record · LEDGER

LeMoRe: Learn More Details for Lightweight Semantic Segmentation

As of 10 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2505.23093.

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

pith.paper-citation-record.v1
2505.23093 v1

Coverage vector

measured 35 of 35 reference resolution

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measured 35 of 35 standing notices

One-hop event checks from named stored sources.

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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

35 of 35 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 16c43403-3885-4095-a7d4-9335e52b641a · outbound

This paper cites A systematic review and meta-analysis of takeover performance during con- ditionally automated driving,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation A systematic review and meta-analysis of takeover performance during con- ditionally automated driving,

Reference 1

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Observation 673980a9-c248-4f28-a7f9-c668ada5377a · outbound

This paper cites Unetformer: A unet-like trans- former for efficient semantic segmentation of remote sensing urban scene imagery,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Unetformer: A unet-like trans- former for efficient semantic segmentation of remote sensing urban scene imagery,

Reference 2

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Observation 441c625f-0999-4b4f-9e71-dd9f53958552 · outbound

This paper cites U-MixFormer: UNet-like Transformer with Mix-Attention for Efficient Semantic Segmentation.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation U-MixFormer: UNet-like Transformer with Mix-Attention for Efficient Semantic Segmentation

Reference 3

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Observation b45714a9-60db-46f3-8b59-6bc909b94356 · outbound

This paper cites Scene parsing through ade20k dataset,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Scene parsing through ade20k dataset,

Reference 4

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Observation 7fb956b5-42d7-45b3-8b5e-819d4c8488a0 · outbound

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

LeMoRe: Learn More Details for Lightweight Semantic Segmentation The cityscapes dataset for semantic urban scene understanding,

Reference 5

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Observation 09e771bf-72e9-416d-b251-067a2bcb1ef6 · outbound

This paper cites The role of con- text for object detection and semantic segmentation in the wild,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation The role of con- text for object detection and semantic segmentation in the wild,

Reference 6

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Observation bea1ca71-f79c-4a35-affa-11d667f9ef32 · outbound

This paper cites Coco-stuff: Thing and stuff classes in context,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Coco-stuff: Thing and stuff classes in context,

Reference 7

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Observation b108fbb4-a8df-4fa1-838e-8830e0e152fd · outbound

This paper cites Deep residual learning for image recognition,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Deep residual learning for image recognition,

Reference 8

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Observation aaa31bf8-3704-4327-887a-991e21496598 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 9

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Observation 4b4a92d0-07df-432a-9e8d-7d130bf334ea · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Mobilenetv2: Inverted residuals and linear bottlenecks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:37.702664Z

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Observation d3dba7e3-a8f5-4c9d-889e-f79bc2a8af00 · outbound

This paper cites Shufflenet v2: Practical guidelines for efficient cnn architecture de- sign,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Shufflenet v2: Practical guidelines for efficient cnn architecture de- sign,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:37.564751Z

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Observation e30e0420-a2de-41d2-98f4-24379cd9d631 · outbound

This paper cites Ghostnet: More features from cheap operations,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Ghostnet: More features from cheap operations,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:37.373288Z

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Observation 22d5d536-4944-4a75-80b3-55b41045cce5 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 13

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This paper cites Training data-efficient image transformers & distillation through attention,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Training data-efficient image transformers & distillation through attention,

Reference 14

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Observation 3cd5d62c-d303-48fc-a78c-bc0c6c805f21 · outbound

This paper cites Levit: a vision transformer in convnet’s clothing for faster inference,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Levit: a vision transformer in convnet’s clothing for faster inference,

Reference 15

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Observation 56c1a9de-cbcf-45a1-af03-65c66752991f · outbound

This paper cites Efficientformer: Vi- sion transformers at mobilenet speed,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Efficientformer: Vi- sion transformers at mobilenet speed,

Reference 16

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Observation 76395f7e-7875-4ae9-a1f4-3b0d4d2ca6eb · outbound

This paper cites MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

Reference 17

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Observation 3e33313a-93b8-40e9-8cd5-eb3b434efae0 · outbound

This paper cites Panoptic feature pyramid networks,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Panoptic feature pyramid networks,

Reference 18

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Observation 61d0ffa2-d142-4623-a83b-c463c1eb7bc1 · outbound

This paper cites Pyramid scene parsing network,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Pyramid scene parsing network,

Reference 19

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This paper cites Fully convolu- tional networks for semantic segmentation,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Fully convolu- tional networks for semantic segmentation,

Reference 20

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Observation 5d4d5699-05a7-482b-ae65-56c069965128 · outbound

This paper cites Convmlp: Hierarchical convolutional mlps for vision,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Convmlp: Hierarchical convolutional mlps for vision,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:36.168477Z

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Observation 0fcb9a60-d791-4056-be9f-1688a95d6e64 · outbound

This paper cites Encoder-decoder with atrous separable con- volution for semantic image segmentation,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Encoder-decoder with atrous separable con- volution for semantic image segmentation,

Reference 22

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Observation 8afb391d-3425-4f95-98a6-e0700338fb6e · outbound

This paper cites Efficientnet: Rethinking model scal- ing for convolutional neural networks,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Efficientnet: Rethinking model scal- ing for convolutional neural networks,

Reference 23

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This paper cites Pem: Prototype- based efficient maskformer for image segmentation,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Pem: Prototype- based efficient maskformer for image segmentation,

Reference 24

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This paper cites Object-contextual representations for semantic segmentation,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Object-contextual representations for semantic segmentation,

Reference 25

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Observation 25855298-9ec3-4dec-a69b-3e79ca051a2c · outbound

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

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Segformer: Simple and efficient design for semantic segmentation with transformers,

Reference 26

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This paper cites Feed- former: Revisiting transformer decoder for efficient se- mantic segmentation,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Feed- former: Revisiting transformer decoder for efficient se- mantic segmentation,

Reference 27

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This paper cites Segnext: Rethinking convolutional attention design for semantic segmentation,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Segnext: Rethinking convolutional attention design for semantic segmentation,

Reference 28

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This paper cites Hr-nas: Searching efficient high-resolution neural architectures with lightweight transformers,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Hr-nas: Searching efficient high-resolution neural architectures with lightweight transformers,

Reference 29

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This paper cites Searching for mobilenetv3,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Searching for mobilenetv3,

Reference 30

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Observation 32d5e76c-9d9b-49d5-90a4-23e60d56ded8 · outbound

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

LeMoRe: Learn More Details for Lightweight Semantic Segmentation ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

Reference 31

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This paper cites Metaseg: Metaformer-based global contexts-aware network for efficient semantic segmentation,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Metaseg: Metaformer-based global contexts-aware network for efficient semantic segmentation,

Reference 32

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Observation 7e22085e-db00-4f60-b13e-fcaad484a1b4 · outbound

This paper cites Topformer: Token pyramid trans- former for mobile semantic segmentation,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Topformer: Token pyramid trans- former for mobile semantic segmentation,

Reference 33

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Observation 44c223ef-d4c5-44d0-8cb6-b01c54c17661 · outbound

This paper cites MMSegmentation: Openmm- lab semantic segmentation toolbox and bench- mark,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation MMSegmentation: Openmm- lab semantic segmentation toolbox and bench- mark,

Reference 34

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Observation c16d9a4b-4819-442e-aba5-9fecbd2cd8bd · outbound

This paper cites Imagenet: A large-scale hierarchical im- age database,.

LeMoRe: Learn More Details for Lightweight Semantic Segmentation Imagenet: A large-scale hierarchical im- age database,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:33.652964Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

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