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Paper Citation Record · LEDGER

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections

As of 9 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2508.20955.

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

pith.paper-citation-record.v1
2508.20955 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:45:30.868808Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

47 of 47 outbound references displayed

  • verified exact1
  • verified fuzzy30
  • unresolved16
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f9482762-c029-42d8-a6b0-1fd84b05f3b9 · outbound

This paper cites Back- propagation applied to handwritten zip code recognition.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Back- propagation applied to handwritten zip code recognition

Reference 1

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verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.320644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.725167Z digest=sha256:0397685dce5e15b458b12400b7685cf760d12a4f6e853f6be00e1418d2ed74be

Observation 42952b25-24d5-4584-8c9c-b9ca785260f6 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.Advances in neural information processing systems, 25, 2012.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Imagenet classification with deep convolutional neural networks.Advances in neural information processing systems, 25, 2012

Reference 2

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no resolver link, observed 2026-08-05T14:45:30.728286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:45:30.728286Z digest=sha256:a4a24a207865129cd3f9457930cab01811b65559a140a53b8f93f19f1d02f412

Observation 6652efb5-7d9a-4958-b830-c7fbe6fae04c · outbound

This paper cites Deep residual learning for image recognition.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Deep residual learning for image recognition

Reference 3

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no resolver link, observed 2026-08-05T14:45:30.731342Z

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source=pdf_text observed=2026-08-05T14:45:30.731342Z digest=sha256:87060d7b3187f8753284c6c3e01a97954e6811ee7b34fd0aaa59fd5282b8ae03

Observation 0b019cf3-8e34-4500-a4cb-dc1d779ff18b · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 4

Resolution
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no resolver link, observed 2026-08-05T14:45:30.735539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:45:30.735539Z digest=sha256:d3743eb308693223b4fabaad64c6bab858283c7edba93233748b30a8c8d2a2e6

Observation 28a23ebd-cbd9-43b2-a07e-f7e012c861b4 · outbound

This paper cites At- tention is all you need.Advances in neural information processing systems, 30, 2017.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections At- tention is all you need.Advances in neural information processing systems, 30, 2017

Reference 5

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no resolver link, observed 2026-08-05T14:45:30.738619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:45:30.738619Z digest=sha256:206d4f0533bdb5bbcbfb874b54dabcef5a77137cc4f3ad09d20e7ad618ee2f9f

Observation 1bea85eb-ac8e-4a97-b494-96eb0bb0f69e · outbound

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

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 6

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source=pdf_text observed=2026-08-05T14:45:30.741921Z digest=sha256:0b5920fb81817d5869f848f8ddbdba24b9056c586aef25281c1169e327c1d847

Observation 25c22196-75bf-4bde-8ab9-8d4288afaa5c · outbound

This paper cites A convnet for the 2020s.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections A convnet for the 2020s

Reference 7

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no resolver link, observed 2026-08-05T14:45:30.745574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:45:30.745574Z digest=sha256:88184f3bf6cf8dc05ccd069913e488194287c54a7c69a2b37dd766c8ec29e97c

Observation bd290ba1-e7d3-4641-a31e-4758e952cb52 · outbound

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

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 8

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no resolver link, observed 2026-08-05T14:45:30.748506Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-05T14:45:30.748506Z digest=sha256:3a8887f7e4dede3fb6bd557af99e7387c06920e0c75db529b6a1e1ce771b5364

Observation 866e1711-199c-4a81-a5de-2b91934e11d5 · outbound

This paper cites Swintransformer:Hierarchicalvision transformerusingshiftedwindows.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Swintransformer:Hierarchicalvision transformerusingshiftedwindows

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.281026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.752495Z digest=sha256:d133aea7e765b2cdf416d7263556bb94bf3e042a220c0f845f4b0b7e5a3af4c3

Observation bdff1478-8a1f-4827-9ab9-7830c7f6d5eb · outbound

This paper cites Imagenet large scale visual recognition challenge.Internationaljournalofcomputervision ,115(3):211–252, 2015.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Imagenet large scale visual recognition challenge.Internationaljournalofcomputervision ,115(3):211–252, 2015

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.271184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.755658Z digest=sha256:f6c278169ee2ec87d88fe213c708d6a08253ebdeeaf1c1d9cbe9bed77a912367

Observation 9f11736e-7bbe-40bf-9c41-d195ffe1be5f · outbound

This paper cites Semantic understanding of scenes through the ade20k dataset.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Semantic understanding of scenes through the ade20k dataset

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.261487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.758475Z digest=sha256:2a669f9a8b9c8d81c3f316d490e16cd16c3b4b6041c0390e33b0b8cdfbb4cc1b

Observation dd19954c-aa38-40c2-995b-4d9cc41a867c · outbound

This paper cites Mi- crosoftcoco:Commonobjectsincontext.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Mi- crosoftcoco:Commonobjectsincontext

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.251301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.761842Z digest=sha256:d350e7e6862022e933f1e26e34ba847cbdf9d958f0c20d1c1a8aaaacd4ad3719

Observation 21860653-dd81-4ae3-9c44-49f8e6ae28cd · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.240996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.764653Z digest=sha256:84f5d3f4b2bad36e013f3f93a89c598387e935f81089e4325e2a9504f15920ec

Observation 548c2ada-4733-449f-8f1e-d5bf5729b5f2 · outbound

This paper cites Searching for mobilenetv3.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Searching for mobilenetv3

Reference 14

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-05T14:45:30.767516Z digest=sha256:ed333b425d59a403a2691bfe7a9d56aba5562bc189399d3334e4f1c49c9c4fc6

Observation 30f10645-c9ae-4b2d-bef3-4a46897e897b · outbound

This paper cites Run, don’t walk: chasing higher flops for faster neural networks.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Run, don’t walk: chasing higher flops for faster neural networks

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.225031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.770403Z digest=sha256:395c24d9984152f3d21ca77074cae10e0e2b7506fa1b02d8b9dc324b84187e8e

Observation 08bb6f3d-8618-4438-9db7-d3f32bdc5a6c · outbound

This paper cites Cspnet: A new backbone that can enhance learning capability of cnn.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Cspnet: A new backbone that can enhance learning capability of cnn

Reference 16

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source=pdf_text observed=2026-08-05T14:45:30.773333Z digest=sha256:be9f5db0f4569560cd4bf3e8a8c18a3f428d4d67303aededcbd968855ee2232f

Observation d100fe98-fa75-451c-8137-0891764ef0d5 · outbound

This paper cites Shuf- flenet:Anextremelyefficientconvolutionalneuralnetworkformobile devices, 2017.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Shuf- flenet:Anextremelyefficientconvolutionalneuralnetworkformobile devices, 2017

Reference 17

Resolution
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raw_fallback, observed 2026-08-05T14:45:31.209109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.777597Z digest=sha256:c0f974ab462326818a7187807f4e2486a6ff9b95c5d1be91e3ccca7d96d6a9b1

Observation e15c4357-b287-4f1b-8f1b-502a3db5a309 · outbound

This paper cites Shuf- flenet:Anextremelyefficientconvolutionalneuralnetworkformobile devices.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Shuf- flenet:Anextremelyefficientconvolutionalneuralnetworkformobile devices

Reference 18

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raw_fallback, observed 2026-08-05T14:45:31.199732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.781876Z digest=sha256:bdfe66537e16ff0eb09ee73d3028519a87687979fafdbbc3d95284d87e8777d6

Observation d0d8c65f-3ae0-4389-a047-e98739d8aac7 · outbound

This paper cites In International conference on machine learning, pages 10096–10106.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections In International conference on machine learning, pages 10096–10106

Reference 19

Resolution
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raw_fallback, observed 2026-08-05T14:45:31.190511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.784495Z digest=sha256:a244f5c124ab096a7807236a73acafbad8cd55d71bc38f6fdaffd8a2dd770c74

Observation 6d58da77-a354-4e5d-af61-f23c9d18374c · outbound

This paper cites Densely connected convolutional networks.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Densely connected convolutional networks

Reference 20

Resolution
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raw_fallback, observed 2026-08-05T14:45:31.181151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.787601Z digest=sha256:edb962630705a52fb8bcf0ff9df01676ab6b258844bbcf626982a71195232ddb

Observation eb7cbd91-5599-4806-8cdf-c59cad7842b3 · outbound

This paper cites Aggregatedresidualtransformationsfordeepneuralnetworks.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Aggregatedresidualtransformationsfordeepneuralnetworks

Reference 21

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raw_fallback, observed 2026-08-05T14:45:31.171849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.790516Z digest=sha256:ec4a3906b7e9248c5a69be22074d65005dd59ad6af51f3ccf114f1e77bcc52f6

Observation 58ed5617-5384-468d-b559-f83a813671fb · outbound

This paper cites Squeeze-and-excitationnetworks.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Squeeze-and-excitationnetworks

Reference 22

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raw_fallback, observed 2026-08-05T14:45:31.162410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.793456Z digest=sha256:083c04ebc3b24260b3ef6c0795cea40848b6df937a47f0a94e99a23d58bdc1d5

Observation 23e5f1f4-564e-454c-9347-068a1b9b133b · outbound

This paper cites Eca-net: Efficient channel attention for deep convolutional neural networks.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Eca-net: Efficient channel attention for deep convolutional neural networks

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.152994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.796635Z digest=sha256:08480d1732de5ed4eda4d43b38d9cb46982eccfa216a0b49de3ee11f5e5996b2

Observation bbf08187-0b07-43d9-8e03-2c63de002da0 · outbound

This paper cites Global second-order pooling convolutional networks.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Global second-order pooling convolutional networks

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.142773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.799380Z digest=sha256:76760d0dc8857877c7aa03abdbb50508b71aa1bc4e7cb512312809ec0004af28

Observation f6f77fbb-06a8-4c37-b04a-038ef39255ea · outbound

This paper cites Srm: A style- based recalibration module for convolutional neural networks.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Srm: A style- based recalibration module for convolutional neural networks

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.132557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.802048Z digest=sha256:9b6e8812fce214d9f6b4ff1768eb90c2ed95a334634003cd2a714625b1be754e

Observation 43c11c41-1450-4145-8341-789481a90a07 · outbound

This paper cites Fcanet: Frequency channel attention networks.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Fcanet: Frequency channel attention networks

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.121953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.805385Z digest=sha256:b5132d59785cb9857be6107395e103b250d297d339f7f2ca07d5c9a02a5eb731

Observation 985da56a-71b5-4f5d-9435-ac95762f49bd · outbound

This paper cites BEiT: BERT Pre-Training of Image Transformers.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections BEiT: BERT Pre-Training of Image Transformers

Reference 27

Resolution
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no resolver link, observed 2026-08-05T14:45:30.808283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:45:30.808283Z digest=sha256:1db7a771cbcf64793455cf0648ac329e2557f72b1bffc6567ee5acbcedf2153b

Observation 5504e1bb-6ede-46eb-ba25-12bc776b35e8 · outbound

This paper cites Batch renormalization: Towards reducing minibatch dependence in batch-normalized models.Advances in neural infor- mation processing systems, 30, 2017.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Batch renormalization: Towards reducing minibatch dependence in batch-normalized models.Advances in neural infor- mation processing systems, 30, 2017

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.112059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.812282Z digest=sha256:fec1ef5a434c564466272c80ddfce1fa18282fafcd1b4f043af5e99ed891412f

Observation 654ff665-cfb2-4e71-85d7-cb3e1062e5fa · outbound

This paper cites Centermask: Real-time anchor- free instance segmentation.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Centermask: Real-time anchor- free instance segmentation

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.101382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.815261Z digest=sha256:82e56fcee21cab272dd1baed5f5a20d2c71f318fc30f444161107c9bc2fac4a1

Observation 2452ea62-3506-4e89-9a84-c7f571713244 · outbound

This paper cites Original approach for the localisation of objects in images.IEE Proceedings- Vision, Image and Signal Processing, 141(4):245–250, 1994.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Original approach for the localisation of objects in images.IEE Proceedings- Vision, Image and Signal Processing, 141(4):245–250, 1994

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.091068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.818173Z digest=sha256:057c61228572d87a8d02b3e376b7e859cbc66f37bde85d0f3949886b301e950c

Observation acb625ee-db64-4711-a112-28eb8b9ff70b · outbound

This paper cites Decoupled Weight Decay Regularization.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Decoupled Weight Decay Regularization

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-05T14:45:30.821879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:45:30.821879Z digest=sha256:0a29e85319f91b16ad0a0593296d7bf6a1be4f0fa4d5404a8cef16d77c43c7ff

Observation 69113330-6b3e-464e-961e-c2c0fb30a63a · outbound

This paper cites Randaugment:Practicalautomateddataaugmentationwithareduced search space.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Randaugment:Practicalautomateddataaugmentationwithareduced search space

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.080249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.824786Z digest=sha256:8bc3aee6d0ece788040dcd5bbe19f0a129f80549115b7ca48b0e54177a06b0d3

Observation 2eb4581a-853e-4d19-bb09-b0c7b8c228e7 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections mixup: Beyond Empirical Risk Minimization

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-05T14:45:30.827447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:45:30.827447Z digest=sha256:63c1011f3ed80d5baa4c6c710f693aad7a3f09c06d3e57968bc04754d329d8f3

Observation f0206a4d-4b22-4375-8083-7b3721245a8d · outbound

This paper cites Cutmix: Regularization strategy to train strong classifiers with localizable features.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Cutmix: Regularization strategy to train strong classifiers with localizable features

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-05T14:45:30.830436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:45:30.830436Z digest=sha256:92b99332ede3a7bc5ab8684f33dae493ba30c57fb8c64c32b9c8bece577e425c

Observation 9328e8d8-5486-45a8-bc34-d51c98e5aa82 · outbound

This paper cites Random erasing data augmentation.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Random erasing data augmentation

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.063936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.833218Z digest=sha256:c267c22e73cfa42fd5d87570558aac087b3adb653203fd7f319863608fd1f7ef

Observation b4e84dca-8d5d-4a98-86f0-2b3e99cdb85b · outbound

This paper cites Rethinking the inception architecture for computer vision.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Rethinking the inception architecture for computer vision

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.054288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.836033Z digest=sha256:fc43e74142597f47b3aa98426608f422c05ac775f72b257785413e7b3129b5ee

Observation f01092ee-e6b5-4d1d-8f16-0704652ffcaf · outbound

This paper cites Ghostnet: More features from cheap operations.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Ghostnet: More features from cheap operations

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.044502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.838924Z digest=sha256:215a63a1403577c882954c928cedaa1d3415e7767011dc4e50bd2abaccb82864

Observation 7b934e42-c92b-4e8e-baf2-520625f03345 · outbound

This paper cites Shuf- flenetv2:Practicalguidelinesforefficientcnnarchitecturedesign.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Shuf- flenetv2:Practicalguidelinesforefficientcnnarchitecturedesign

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.034274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.841742Z digest=sha256:7ad792b1c64da2a18fe7f5aa5ca1571ff9724f428c3b385c3c8f632894c9187d

Observation 07d5e589-b6d0-4cc1-92a5-ddb0e84e1b5c · outbound

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

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-05T14:45:30.844559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:45:30.844559Z digest=sha256:7172be4db3b7d7987a35f4cbd8366b354b50ac7f6dc5657a7858621c7697ece0

Observation 9f66f47c-e238-4dec-8457-36e75a028983 · outbound

This paper cites Edgenext: efficiently amalgamated cnn-transformer archi- tecture for mobile vision applications.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Edgenext: efficiently amalgamated cnn-transformer archi- tecture for mobile vision applications

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.023015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.847579Z digest=sha256:a52df76b171f600c0c0d0a1b9143c61a1961364648a5924c07bf0e349700d292

Observation b31f56d8-c0e2-49f0-bfa2-cb46dd378c68 · outbound

This paper cites Rewrite the stars.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Rewrite the stars

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.012996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.850508Z digest=sha256:372bc5ecf971f32c86b35736d62d093b30931334f2917e0ef03ef5be1ece35c2

Observation 8bfd7371-fcab-491c-8bd0-13845fa1cdbc · outbound

This paper cites CycleMLP: A MLP-like Architecture for Dense Prediction.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections CycleMLP: A MLP-like Architecture for Dense Prediction

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-05T14:45:30.853193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:45:30.853193Z digest=sha256:642b5101ee67dbc0e04e125a72f3906cfb53be210c75919b02bbe68bf1e386bb

Observation 893b9478-4149-4d1e-a23f-5e8e311152e9 · outbound

This paper cites Metaformer is actually what F Wang et al.:Preprint submitted to Elsevier Page 12 of 13 E-ConvNeXt you need for vision.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Metaformer is actually what F Wang et al.:Preprint submitted to Elsevier Page 12 of 13 E-ConvNeXt you need for vision

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:31.002311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.856428Z digest=sha256:fde3b500ab335da73e575fb5932ad89da2bdf88b931196d5b3c4379e558c1168

Observation 5e46d772-c85e-4c2b-82a8-628e004630b4 · outbound

This paper cites Pyramid vision transformer: A versatile backbone for dense prediction without con- volutions.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Pyramid vision transformer: A versatile backbone for dense prediction without con- volutions

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:30.992208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.859550Z digest=sha256:4719ee3ddac8c1570f0226b8c38c6b047cdc711aa7e2853effe9725addeb0d5c

Observation 91f2696b-3286-470b-ada5-42ac11db8e32 · outbound

This paper cites A Dataset And Benchmark Of Underwater Object Detection For Robot Picking.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections A Dataset And Benchmark Of Underwater Object Detection For Robot Picking

Reference 45

Resolution
verified exact
local_arxiv, observed 2026-08-05T14:45:30.907510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.862545Z digest=sha256:dd3305c22cdccd637306eda71e0bd1b4d5e54474824ad3d5ee3e97fefe8c6582

Observation 10c949ab-840d-4c4e-b0c3-52ff50fcc5f8 · outbound

This paper cites PP-YOLOE: An evolved version of YOLO.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections PP-YOLOE: An evolved version of YOLO

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-05T14:45:30.865679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:45:30.865679Z digest=sha256:8c72fd3caaa9a68245f406e09a421eb688099d2c375f7f3805f8ba1f7572323e

Observation 801bc52e-2deb-442b-90ff-fda2e8dd6fa7 · outbound

This paper cites Yolov10: Real-time end-to-end object detection.Advances in Neural Information Processing Systems, 37:107984–108011, 2024.

E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections Yolov10: Real-time end-to-end object detection.Advances in Neural Information Processing Systems, 37:107984–108011, 2024

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:45:30.981743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T14:45:30.868808Z digest=sha256:6fa3f051d5dccc313162b9a363d175333a74f90cf9c554b6b96f734b75a8c5ae

Pith citing papers

No inbound Pith citation observations are available.