Paper Citation Record · LEDGER

Achieving 3D Attention via Triplet Squeeze and Excitation Block

As of 16 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2505.05943.

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

pith.paper-citation-record.v1
2505.05943 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:55:38.635413Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

41 of 41 outbound references displayed

verified exact5
verified fuzzy13
unresolved21
parse uncertain0
malformed identifier2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a86d5b6d-220c-4859-bdf2-0e1d5b35d68f · outbound

This paper cites Gradient-based learning applied to document recognition,.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Gradient-based learning applied to document recognition,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T22:55:38.463893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b500c85-528e-4c40-bfe4-20a2bc9cd31a · outbound

This paper cites Imagenet classifica- tion with deep convolutional neural networks,.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Imagenet classifica- tion with deep convolutional neural networks,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T22:55:38.469109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de8679f5-4532-4d34-8bd5-2dffff4d81c6 · outbound

This paper cites Going Deeper with Convolutions.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Going Deeper with Convolutions

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T22:55:38.474209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb27c83a-b216-46ab-badf-e5358bd1162b · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T22:55:38.479357Z

Source-reported events for the cited work

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Observation 31f863de-26a4-4862-8ffd-2e11c54738bb · outbound

This paper cites Deep Residual Learning for Image Recognition.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Deep Residual Learning for Image Recognition

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T22:55:38.484398Z

Source-reported events for the cited work

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Observation dc57b220-f848-4f76-922c-5308cc211e56 · outbound

This paper cites MobileNetV2: Inverted Residuals and Linear Bottlenecks.

Achieving 3D Attention via Triplet Squeeze and Excitation Block MobileNetV2: Inverted Residuals and Linear Bottlenecks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T22:55:38.489102Z

Source-reported events for the cited work

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Observation e4ee95dc-f7e4-4a09-b526-0614ae429ce9 · outbound

This paper cites Facial expression and attributes recognition based on multi-task learning of lightweight neural networks,.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Facial expression and attributes recognition based on multi-task learning of lightweight neural networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:55:39.296387Z

Source-reported events for the cited work

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Observation f491b6e6-bf63-48f4-8759-ada9dfdf0fc5 · outbound

This paper cites Densely Connected Convolutional Networks.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Densely Connected Convolutional Networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T22:55:38.500569Z

Source-reported events for the cited work

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Observation 47609838-7f24-4fd0-b200-59b307f2635f · outbound

This paper cites Squeeze-and-Excitation Networks.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Squeeze-and-Excitation Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T22:55:38.506823Z

Source-reported events for the cited work

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Observation 3f8b9ac6-50d7-4430-b0b4-b059903e39c3 · outbound

This paper cites CBAM: Convolutional Block Attention Module.

Achieving 3D Attention via Triplet Squeeze and Excitation Block CBAM: Convolutional Block Attention Module

Reference 10

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Source-reported events for the cited work

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Observation 8198e05c-b7e8-4c5b-b0a7-a54cdb29037b · outbound

This paper cites BAM: Bottleneck Attention Module.

Achieving 3D Attention via Triplet Squeeze and Excitation Block BAM: Bottleneck Attention Module

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T22:55:38.515572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ce73195-0164-4e16-8fb8-db01de217743 · outbound

This paper cites Rotate to Attend: Convolutional Triplet Attention Module.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Rotate to Attend: Convolutional Triplet Attention Module

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:55:38.869964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3d5588f5-e190-42b4-852a-b7aac6871968 · outbound

This paper cites Attention Is All You Need.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Attention Is All You Need

Reference 13

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Source-reported events for the cited work

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Observation 9acab24b-ecf0-4f8b-a2e4-5c1cb998e738 · outbound

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

Achieving 3D Attention via Triplet Squeeze and Excitation Block An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T22:55:38.528288Z

Source-reported events for the cited work

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Observation 3ab70881-4466-4452-a72d-1323c9fd91b2 · outbound

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

Achieving 3D Attention via Triplet Squeeze and Excitation Block Training data-efficient image transformers & distillation through attention,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:55:39.275205Z

Source-reported events for the cited work

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Observation 582ebbd3-bcc6-4800-81ac-7f777e329bb4 · outbound

This paper cites A convnet for the 2020s,.

Achieving 3D Attention via Triplet Squeeze and Excitation Block A convnet for the 2020s,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:55:39.259313Z

Source-reported events for the cited work

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Observation 2b9aaf86-30ad-4017-88f5-a470ff32d5be · outbound

This paper cites Emonext: an adapted convnext for facial emotion recognition,.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Emonext: an adapted convnext for facial emotion recognition,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:55:39.243867Z

Source-reported events for the cited work

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Observation cb01d91b-4618-4ffc-81c9-58841e955432 · outbound

This paper cites Spatial Transformer Networks.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Spatial Transformer Networks

Reference 18

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Source-reported events for the cited work

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Observation 649a43f4-dabc-4f7c-845c-8d844175a2f8 · outbound

This paper cites An Attentive Survey of Attention Models.

Achieving 3D Attention via Triplet Squeeze and Excitation Block An Attentive Survey of Attention Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T22:55:38.547747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fa5d850-5994-4714-b30c-61c7740f1a6f · outbound

This paper cites Attention mechanisms in computer vision: A survey,.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Attention mechanisms in computer vision: A survey,

Reference 20

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Source-reported events for the cited work

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Observation e3d7e31e-5dc7-4567-a52b-bd5b922a3673 · outbound

This paper cites Global Second-order Pooling Convolutional Networks.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Global Second-order Pooling Convolutional Networks

Reference 21

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Source-reported events for the cited work

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Observation b90185a5-b6dd-44bf-a4b1-2614a793a282 · outbound

This paper cites SRM : A Style-based Recalibration Module for Convolutional Neural Networks.

Achieving 3D Attention via Triplet Squeeze and Excitation Block SRM : A Style-based Recalibration Module for Convolutional Neural Networks

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:55:38.774773Z

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Observation 311a89e8-1e33-4233-94bc-ac826032a2e5 · outbound

This paper cites Gated Channel Transformation for Visual Recognition.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Gated Channel Transformation for Visual Recognition

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:55:38.756039Z

Source-reported events for the cited work

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Observation e2ae2953-18bf-4fb4-a734-ec432b615f24 · outbound

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

Achieving 3D Attention via Triplet Squeeze and Excitation Block Eca-net: Efficient channel attention for deep convolutional neural networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:55:39.213817Z

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Observation 2645ffe9-9c71-4da3-a189-5837b68c381a · outbound

This paper cites FcaNet: Frequency Channel Attention Networks.

Achieving 3D Attention via Triplet Squeeze and Excitation Block FcaNet: Frequency Channel Attention Networks

Reference 25

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Source-reported events for the cited work

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Observation 3f47f421-10f7-44cf-a215-42044683259a · outbound

This paper cites Recurrent Models of Visual Attention.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Recurrent Models of Visual Attention

Reference 26

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Source-reported events for the cited work

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Observation a9d1388c-8b06-4469-82c6-18b34fa34128 · outbound

This paper cites Show, attend and tell: Neural image caption generation with visual attention,.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Show, attend and tell: Neural image caption generation with visual attention,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:55:39.199833Z

Source-reported events for the cited work

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Observation 52a97831-e80d-4782-a092-9aeb032244fa · outbound

This paper cites Non-local Neural Networks.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Non-local Neural Networks

Reference 28

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Source-reported events for the cited work

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Observation ae9971a0-9b4b-4487-a19e-0ff897598320 · outbound

This paper cites Residual Attention Network for Image Classification.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Residual Attention Network for Image Classification

Reference 29

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Source-reported events for the cited work

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Observation be94c61a-9037-4705-aba8-3acdb94ea401 · outbound

This paper cites Best fit activation functions for attention mechanism: Comparison and enhancement,.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Best fit activation functions for attention mechanism: Comparison and enhancement,

Reference 30

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Observation d6c53047-1b81-4ece-b2bd-8ff89a85d996 · outbound

This paper cites Facial expression recognition using residual masking network,.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Facial expression recognition using residual masking network,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:55:39.169268Z

Source-reported events for the cited work

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Observation ebab86a0-34b6-421b-8d00-bf8868c28333 · outbound

This paper cites A novel facial emotion recognition model using segmentation vgg- 19 architecture - international journal of information technology,.

Achieving 3D Attention via Triplet Squeeze and Excitation Block A novel facial emotion recognition model using segmentation vgg- 19 architecture - international journal of information technology,

Reference 32

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Observation e43b1545-947c-431e-9a0c-dc510e54cc3a · outbound

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Achieving 3D Attention via Triplet Squeeze and Excitation Block Unresolved cited work

Reference 33

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Source-reported events for the cited work

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Observation 2f766ce4-c892-41d6-b6e9-d491d3aeb422 · outbound

This paper cites Deep-emotion: Facial expression recognition using attentional convolutional network,.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Deep-emotion: Facial expression recognition using attentional convolutional network,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:55:39.124630Z

Source-reported events for the cited work

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Observation 4b91d3ac-7cb3-4664-b75e-0452d2e360c3 · outbound

This paper cites Facial emotion recognition: State of the art performance on fer2013,.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Facial emotion recognition: State of the art performance on fer2013,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:55:39.105519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 754b7f8c-5fb6-4fe9-979b-cd23312b8419 · outbound

This paper cites Ad-corre: Adaptive correlation-based loss for facial expression recognition in the wild,.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Ad-corre: Adaptive correlation-based loss for facial expression recognition in the wild,

Reference 36

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Source-reported events for the cited work

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Observation 0fde30e2-17a3-439e-bacd-10d1ca335efd · outbound

This paper cites Facial expression recogni- tion with deep learning,.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Facial expression recogni- tion with deep learning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:55:39.078648Z

Source-reported events for the cited work

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Observation 52615bdc-f2d2-413f-9340-144f231af72c · outbound

This paper cites A novel facial emotion recognition model using segmentation vgg- 19 architecture,.

Achieving 3D Attention via Triplet Squeeze and Excitation Block A novel facial emotion recognition model using segmentation vgg- 19 architecture,

Reference 38

Resolution: verified exact; doi, observed 2026-08-15T22:55:38.672902Z

Source-reported events for the cited work

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Observation 128982ca-2405-4f51-a1fc-a055104b58f4 · outbound

This paper cites Local multi-head channel self- attention for facial expression recognition,.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Local multi-head channel self- attention for facial expression recognition,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:55:39.065006Z

Source-reported events for the cited work

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Observation cc1d8b4b-7481-4c7f-a5c1-b0eb637bb87d · outbound

This paper cites ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks.

Achieving 3D Attention via Triplet Squeeze and Excitation Block ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-15T22:55:38.572727Z

Source-reported events for the cited work

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Observation 339055d1-5f29-4545-a1af-091946a2fe6a · outbound

This paper cites Available: https://www.mdpi.com/2078-2489/13/9/419.

Achieving 3D Attention via Triplet Squeeze and Excitation Block Available: https://www.mdpi.com/2078-2489/13/9/419

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:55:39.045450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.