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Paper Citation Record · LEDGER

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network

As of 8 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2506.21905.

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

pith.paper-citation-record.v1
2506.21905 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:23:21.525042Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

25 of 25 outbound references displayed

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  • verified fuzzy13
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6fa228b1-0022-4f2b-87b7-e6c21643debe · outbound

This paper cites Fet-fgvc: Feature-enhanced transformer for fine-grained visual classification.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network Fet-fgvc: Feature-enhanced transformer for fine-grained visual classification

Reference 1

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation d1ec8f9c-7b2c-41e7-b447-71257a5ed9eb · outbound

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

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2

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Observation 2f2fb156-0b94-47b5-821c-c13aa3f4b54a · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 3

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Observation 0284387e-b816-4f1e-b3cf-843fdd6e2bd5 · outbound

This paper cites AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning

Reference 4

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verified exact
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Source-reported events for the cited work

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Observation bf89845e-d380-4629-b676-0d7418fd2524 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 5

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Observation 4e52a722-155b-465b-a7d3-f393ac4ef5cd · outbound

This paper cites Denoising dif- fusion probabilistic models.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network Denoising dif- fusion probabilistic models

Reference 6

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Observation bb584484-b708-44ed-b615-3307374d354f · outbound

This paper cites Part- stacked cnn for fine-grained visual categorization.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network Part- stacked cnn for fine-grained visual categorization

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation f35d1f07-98f6-4c0c-b325-8083e9307184 · outbound

This paper cites Low-rank bilinear pool- ing for fine-grained classification.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network Low-rank bilinear pool- ing for fine-grained classification

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 64486962-2a63-4031-8299-53eece715d20 · outbound

This paper cites Vmamba: Visual state space model.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network Vmamba: Visual state space model

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 9d30f78d-ed00-4417-8200-22adfa2fa1da · outbound

This paper cites Swin transformer v2: Scaling up capacity and resolution.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network Swin transformer v2: Scaling up capacity and resolution

Reference 10

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation ba2b2d0c-bc77-48fc-8707-b02107125667 · outbound

This paper cites Fine-grained adversarial semi- supervised learning.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network Fine-grained adversarial semi- supervised learning

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 62487539-d482-4e39-b026-db81da223b86 · outbound

This paper cites An Overview of Deep Semi-Supervised Learning.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network An Overview of Deep Semi-Supervised Learning

Reference 12

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Observation 15da6b8f-993f-47a2-8fd2-96b9ab9decb5 · outbound

This paper cites A survey of recent advances in cnn-based fine-grained visual categorization.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network A survey of recent advances in cnn-based fine-grained visual categorization

Reference 13

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation d5cb84c8-ad3a-46c5-8fe2-656ac0a980fe · outbound

This paper cites In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning

Reference 14

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Observation be24bc45-e11c-485f-b209-be0535f4a876 · outbound

This paper cites Fixmatch: Simplifying semi-supervised learning with consistency and confidence.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network Fixmatch: Simplifying semi-supervised learning with consistency and confidence

Reference 15

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation e02b9677-9f33-4727-bc5b-aff6fce90f01 · outbound

This paper cites Find it if you can: end-to-end adversar- ial erasing for weakly-supervised semantic segmentation.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network Find it if you can: end-to-end adversar- ial erasing for weakly-supervised semantic segmentation

Reference 16

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation ee0c393e-a0f7-498f-bf03-4e90f619c42b · outbound

This paper cites Learn- ing a discriminative filter bank within a cnn for fine-grained recognition.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network Learn- ing a discriminative filter bank within a cnn for fine-grained recognition

Reference 17

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 72f98a1c-d31a-4f7f-b33d-874e22a671a0 · outbound

This paper cites FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning

Reference 18

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Observation 832d5ab4-d78f-475d-babe-3184a43eec09 · outbound

This paper cites Dynamic Vision Mamba.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network Dynamic Vision Mamba

Reference 19

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Observation 5ce5e657-655b-409a-8a2a-ed7ba06bbffe · outbound

This paper cites End-to- end semi-supervised object detection with soft teacher.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network End-to- end semi-supervised object detection with soft teacher

Reference 20

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Observation ceb5afe1-121e-4ab7-8c86-3d6657628556 · outbound

This paper cites DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation

Reference 21

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Observation 47ec848b-10d6-49c2-b07b-ac0d42fc13cb · outbound

This paper cites Learning to navigate for fine-grained clas- sification.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network Learning to navigate for fine-grained clas- sification

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 375e3910-8987-4fa9-a530-adc9181dff35 · outbound

This paper cites A 1d-inception-resnet based global detection model for thin- skinned multifruit spectral quantitative analysis.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network A 1d-inception-resnet based global detection model for thin- skinned multifruit spectral quantitative analysis

Reference 23

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation bde01393-b053-4749-be9b-6a0c8bfa4077 · outbound

This paper cites Flexmatch: Boosting semi-supervised learning with curricu- lum pseudo labeling.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network Flexmatch: Boosting semi-supervised learning with curricu- lum pseudo labeling

Reference 24

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 652b78dd-8bd2-4c72-bd3d-ef90eb2fac9b · outbound

This paper cites Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model

Reference 25

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Pith citing papers

No inbound Pith citation observations are available.