Paper Citation Record · LEDGER

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks

As of 16 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:1908.02095.

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

pith.paper-citation-record.v1
1908.02095 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:57:57.559749Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

29 of 29 outbound references displayed

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verified fuzzy25
unresolved3
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d2b92e62-0a8a-4017-85fc-853f41472077 · outbound

This paper cites Visualizing and understanding convolu- tional networks,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Visualizing and understanding convolu- tional networks,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.892628Z

Source-reported events for the cited work

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Observation 41efe5da-9afc-41a8-899a-529bdea66771 · outbound

This paper cites ImageNet classiﬁcation with deep convolutional neural networks,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks ImageNet classiﬁcation with deep convolutional neural networks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.882909Z

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Observation b14139ff-8cf8-48e4-8b46-03feaed95e6a · outbound

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

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 3

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Observation 9c010776-fef1-40a2-a424-14ed5200a44f · outbound

This paper cites Going deeper with convolutions,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Going deeper with convolutions,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.872621Z

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Observation 16379bab-0cdb-4e23-a486-60055c41ad20 · outbound

This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Rich feature hierarchies for accurate object detection and semantic segmentation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.862916Z

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Observation c8fab4e0-3c33-4595-bf5f-81d7ac2f23d3 · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Fully convolutional networks for semantic segmentation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.851631Z

Source-reported events for the cited work

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Observation 22b464b4-9345-4971-be3f-e1993c9a71d7 · outbound

This paper cites A survey on deep learning in medical image analysis,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks A survey on deep learning in medical image analysis,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.841631Z

Source-reported events for the cited work

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Observation a4e277ba-1d55-4b98-b7fc-da8f45e98df2 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks U-net: Convolutional networks for biomedical image segmentation,

Reference 8

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Source-reported events for the cited work

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Observation d29bee55-e4cb-4810-91c2-4b96c3b91238 · outbound

This paper cites DCAN: Deep contour-aware networks for object instance segmentation from histology images,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks DCAN: Deep contour-aware networks for object instance segmentation from histology images,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.820777Z

Source-reported events for the cited work

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Observation a7a668ad-2007-46a0-8ea6-46277056f860 · outbound

This paper cites Gland instance segmentation by deep multichannel side supervision,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Gland instance segmentation by deep multichannel side supervision,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.810201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1aa3dbeb-b82c-4934-bb56-1220bbf0a94a · outbound

This paper cites Gland instance segmentation using deep multichannel neural networks,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Gland instance segmentation using deep multichannel neural networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.799739Z

Source-reported events for the cited work

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Observation a010fcb3-6365-4419-8788-dc62f5cc4e41 · outbound

This paper cites A decision-theoretic generalization of on-line learning and an application to boosting,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks A decision-theoretic generalization of on-line learning and an application to boosting,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.789109Z

Source-reported events for the cited work

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Observation 2b63850b-470f-4545-a4f4-08952491745f · outbound

This paper cites Auto-context and its application to high-level vision tasks and 3D brain image segmentation,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Auto-context and its application to high-level vision tasks and 3D brain image segmentation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.778834Z

Source-reported events for the cited work

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Observation f49798fc-f8a9-4546-a5f8-a22aff279659 · outbound

This paper cites Iterative instance segmentation,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Iterative instance segmentation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.768245Z

Source-reported events for the cited work

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Observation 167a57b8-ac77-4964-9f4b-234569481cfb · outbound

This paper cites Boundary-aware fully convolutional network for brain tumor segmentation,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Boundary-aware fully convolutional network for brain tumor segmentation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.758273Z

Source-reported events for the cited work

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Observation 08cc9284-d3d1-48b4-a7e8-b0480a6da02e · outbound

This paper cites Detect, replace, reﬁne: Deep structured prediction for pixel wise labeling,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Detect, replace, reﬁne: Deep structured prediction for pixel wise labeling,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.747527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2956e144-cb21-470a-8d43-5d71a64a0ecb · outbound

This paper cites Image Segmentation by Iterative Inference from Conditional Score Estimation.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Image Segmentation by Iterative Inference from Conditional Score Estimation

Reference 17

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Source-reported events for the cited work

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Observation 61a7efde-d957-4aff-8de5-37c67c5f8cad · outbound

This paper cites Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.736854Z

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Observation e8eec99b-ef7f-40d5-9d94-c21fa5ea6402 · outbound

This paper cites SegNet: A deep convolutional encoder-decoder architecture for image segmentation,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks SegNet: A deep convolutional encoder-decoder architecture for image segmentation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.726085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e065d8ba-78fd-4c47-8b2f-bccbcfc0f05d · outbound

This paper cites Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.715184Z

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Observation 1da35a51-55f6-4926-ae07-4747d6a1f081 · outbound

This paper cites Focal loss for dense object detection,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Focal loss for dense object detection,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.704659Z

Source-reported events for the cited work

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Observation dbf294d1-440c-408e-8952-2c3f9d6e2acc · outbound

This paper cites Boosting neural networks,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Boosting neural networks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.693266Z

Source-reported events for the cited work

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Observation 210fd121-9b0f-46d1-ac93-0cf0a5a45fac · outbound

This paper cites Incremental learning of convolutional neural networks,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Incremental learning of convolutional neural networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.681551Z

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Observation 5b3a1939-97f4-4e5d-ba09-e2e4eb2f2a1a · outbound

This paper cites Convolutional neural network based sentiment analysis using adaboost combination,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Convolutional neural network based sentiment analysis using adaboost combination,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.670861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0477e34a-c4e6-4331-8fee-c18272696fbc · outbound

This paper cites Robust object rep- resentation by boosting-like deep learning architecture,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Robust object rep- resentation by boosting-like deep learning architecture,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.659860Z

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Observation a03e0b7b-1689-44a9-b292-e712f2522a68 · outbound

This paper cites Incremental boosting convolutional neural network for facial action unit recognition,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Incremental boosting convolutional neural network for facial action unit recognition,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.649002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 370c5551-314b-45df-abd7-497ec9be2b36 · outbound

This paper cites Dropout: a simple way to prevent neural networks from overﬁt- ting,.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Dropout: a simple way to prevent neural networks from overﬁt- ting,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:57:57.637911Z

Source-reported events for the cited work

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Observation 85b20977-a1fa-4e71-abaa-a87f22907ac9 · outbound

This paper cites ADADELTA: An Adaptive Learning Rate Method.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks ADADELTA: An Adaptive Learning Rate Method

Reference 28

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Observation cd81ecf8-2836-4d04-9be3-d25619dcd937 · outbound

This paper cites Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest.

AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest

Reference 29

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Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.