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Paper Citation Record · LEDGER

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning

As of 9 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2607.22072.

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

pith.paper-citation-record.v1
2607.22072 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T05:58:30.183175Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation bbd53633-a946-4827-932b-98c7213751cc · outbound

This paper cites Learning to prompt for continual learning,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Learning to prompt for continual learning,

Reference 1

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Observation 20b9466c-6f77-40cd-9451-4f55cc49d82c · outbound

This paper cites Few-shot class-incremental learning,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Few-shot class-incremental learning,

Reference 2

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Observation 75e3c72e-557e-45c6-9f92-62f5e796ffb5 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Distilling the Knowledge in a Neural Network

Reference 3

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Observation d11aa723-809e-420b-b917-cf67fc2e85d8 · outbound

This paper cites Few-shot in- cremental learning with continually evolved classifiers,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Few-shot in- cremental learning with continually evolved classifiers,

Reference 4

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Observation 983316d7-2447-48e5-af4d-8299fcd2e0e8 · outbound

This paper cites Few- shot class-incremental learning via training-free prototype calibration,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Few- shot class-incremental learning via training-free prototype calibration,

Reference 5

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Observation 38ae5a25-c57c-418c-87ff-00e3b308af59 · outbound

This paper cites Compositional few- shot class-incremental learning,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Compositional few- shot class-incremental learning,

Reference 6

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Observation 0c9484e8-bdae-46a8-bd92-5de0198e9e22 · outbound

This paper cites Learning a unified classifier incrementally via rebalancing,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Learning a unified classifier incrementally via rebalancing,

Reference 7

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Observation 4019fd1d-098b-485a-99c3-e2909db46e9a · outbound

This paper cites Margin-based few-shot class- incremental learning with class-level overfitting mitigation,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Margin-based few-shot class- incremental learning with class-level overfitting mitigation,

Reference 8

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Observation 7b370c26-eed2-4310-8de4-5ac79bd89045 · outbound

This paper cites Catastrophic forgetting in connectionist networks,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Catastrophic forgetting in connectionist networks,

Reference 9

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Observation 22f43fb2-d199-437f-ae49-09ad61a8b822 · outbound

This paper cites Self-promoted prototype refinement for few-shot class-incremental learning,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Self-promoted prototype refinement for few-shot class-incremental learning,

Reference 10

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Observation c8354d44-8b91-46be-9b5a-67a5090168a7 · outbound

This paper cites Delve into Base-Novel Confusion: Redundancy Exploration for Few-Shot Class-Incremental Learning.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Delve into Base-Novel Confusion: Redundancy Exploration for Few-Shot Class-Incremental Learning

Reference 11

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Observation 8e72da2c-2498-4d85-b1a3-87930b070bba · outbound

This paper cites Few-shot class-incremental learning via generative co-memory regularization,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Few-shot class-incremental learning via generative co-memory regularization,

Reference 12

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Observation 38025982-3fe9-4486-bf53-39511fcac1cc · outbound

This paper cites Pki: Prior knowledge-infused neural network for few-shot class-incremental learn- ing,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Pki: Prior knowledge-infused neural network for few-shot class-incremental learn- ing,

Reference 13

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Observation b062e621-6489-4ee3-84ce-3bd5f6ba0dd1 · outbound

This paper cites Cd2: constrained dataset distillation for few-shot class-incremental learning,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Cd2: constrained dataset distillation for few-shot class-incremental learning,

Reference 14

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Observation 6cddcb09-7d3c-4595-ae5a-3ebd4755edfa · outbound

This paper cites Divide and conquer: Static-dynamic collaboration for few-shot class-incremental learning,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Divide and conquer: Static-dynamic collaboration for few-shot class-incremental learning,

Reference 15

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Observation 4a3f6e18-2f26-456d-923e-e6758c855afb · outbound

This paper cites Attraction diminishing and distributing for few-shot class-incremental learning,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Attraction diminishing and distributing for few-shot class-incremental learning,

Reference 16

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Observation 46407b80-209d-471f-b3ea-0ce50df0203f · outbound

This paper cites Language-inspired relation transfer for few-shot class-incremental learning,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Language-inspired relation transfer for few-shot class-incremental learning,

Reference 17

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Observation 6d697a0b-ce59-4075-a800-84ec3ed52b3c · outbound

This paper cites Recognition-by-components: a theory of human image understanding.,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Recognition-by-components: a theory of human image understanding.,

Reference 18

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Observation f0a03385-f539-45b2-9937-5694f42f5fe8 · outbound

This paper cites Context-based and diversity-driven specificity in compositional zero-shot learning,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Context-based and diversity-driven specificity in compositional zero-shot learning,

Reference 19

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Observation e61df8ce-0566-4481-bcc0-c487e8981b99 · outbound

This paper cites Prompting language- informed distribution for compositional zero-shot learning,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Prompting language- informed distribution for compositional zero-shot learning,

Reference 20

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Observation be22d472-dc37-4878-b8e5-03660cbe5cf4 · outbound

This paper cites Decompose novel into known: Part concept learning for 3d novel class discovery,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Decompose novel into known: Part concept learning for 3d novel class discovery,

Reference 21

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Observation 14c5e80e-9e97-4922-8d1b-df535b9aecb8 · outbound

This paper cites Disentangled representation learning,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Disentangled representation learning,

Reference 22

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Observation 23a9267c-d716-4584-bc24-c9e352b650be · outbound

This paper cites Layer-wise representation fusion for compositional generaliza- tion,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Layer-wise representation fusion for compositional generaliza- tion,

Reference 23

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Observation 3d5b1163-4c82-45e0-9660-42f245e78471 · outbound

This paper cites Learning Clustering-based Prototypes for Compositional Zero-shot Learning.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Learning Clustering-based Prototypes for Compositional Zero-shot Learning

Reference 24

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Observation 4bd9a9f5-c35e-4a71-b6e9-d7b037167c82 · outbound

This paper cites Parts of recognition,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Parts of recognition,

Reference 25

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Observation 1c2a3244-a9cb-4070-8ce2-cf1a43135e61 · outbound

This paper cites Learning deep features for discriminative localization,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Learning deep features for discriminative localization,

Reference 26

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Observation 378f7425-db4b-4be4-889a-46ea6d81236c · outbound

This paper cites Compo- sitional few-shot recognition with primitive discovery and enhancing,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Compo- sitional few-shot recognition with primitive discovery and enhancing,

Reference 27

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Observation b2e87f78-7c2a-47ef-8e59-f983796546e8 · outbound

This paper cites Basic objects in natural categories,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Basic objects in natural categories,

Reference 28

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Observation ea7b8b87-4524-449a-82ac-20de37f7ff15 · outbound

This paper cites Learning orthogonal prototypes for generalized few-shot semantic segmentation,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Learning orthogonal prototypes for generalized few-shot semantic segmentation,

Reference 29

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Observation 74c55090-380f-4c1e-bc28-e2bfec36a706 · outbound

This paper cites Learning deep representations by mutual information estimation and maximization.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Learning deep representations by mutual information estimation and maximization

Reference 30

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Observation 40d40e89-b9d3-4c8a-a812-db11fd20d47a · outbound

This paper cites Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learning.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learning

Reference 31

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Observation a009687b-ae0b-40ec-8029-f34eea1d8cbc · outbound

This paper cites Few- shot class-incremental learning by sampling multi-phase tasks,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Few- shot class-incremental learning by sampling multi-phase tasks,

Reference 32

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Observation 7092562c-644f-4eac-9c17-4ed98e106028 · outbound

This paper cites Metafscil: a meta-learning approach for few-shot class incremental learning,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Metafscil: a meta-learning approach for few-shot class incremental learning,

Reference 33

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Observation 48d58f02-85af-4939-bec7-273a24c29a44 · outbound

This paper cites For- ward compatible few-shot class-incremental learning,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning For- ward compatible few-shot class-incremental learning,

Reference 34

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Observation 26e8f9ab-04a3-4bb3-8ba9-9f9fecec70a2 · outbound

This paper cites Few- shot class-incremental learning via entropy-regularized data-free replay,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Few- shot class-incremental learning via entropy-regularized data-free replay,

Reference 35

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Observation fb924901-70a3-4e4f-b3c7-6c83b3924ee3 · outbound

This paper cites Few-shot class- incremental learning from an open-set perspective,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Few-shot class- incremental learning from an open-set perspective,

Reference 36

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Observation 5d78d02e-72d6-4078-953c-dc7adb150d1d · outbound

This paper cites Rethinking Few-shot Class-incremental Learning: Learning from Yourself.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Rethinking Few-shot Class-incremental Learning: Learning from Yourself

Reference 37

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Observation 6f5643a7-8f16-41cd-9236-861f5b97d749 · outbound

This paper cites Pre-trained vision and language transformers are few-shot incremental learners,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Pre-trained vision and language transformers are few-shot incremental learners,

Reference 38

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Observation f0d31fd2-0959-4b14-9b22-c1338e32fe2b · outbound

This paper cites Prototype-guided memory replay for continual learning,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Prototype-guided memory replay for continual learning,

Reference 39

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source=pdf_text observed=2026-08-01T05:58:29.612610Z digest=sha256:bfff165a44d41773da7481fd121106a5786fbcfbfd8e955129360e58aa6f8c4f

Observation b5e692db-d8fd-4ee4-bcd8-53aff015d266 · outbound

This paper cites Learning multiple layers of features from tiny images,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Learning multiple layers of features from tiny images,

Reference 40

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source=pdf_text observed=2026-08-01T05:58:29.778188Z digest=sha256:07568c58aea26979dea0bd9a46544129853374961d50103600145a13064f6918

Observation 00568d29-accf-499c-8fcf-2ecf8ec4020f · outbound

This paper cites Imagenet large scale visual recognition challenge,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Imagenet large scale visual recognition challenge,

Reference 41

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Observation cb881f96-5752-4040-83f5-cbc61cb6f8ab · outbound

This paper cites The caltech-ucsd birds-200-2011 dataset,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning The caltech-ucsd birds-200-2011 dataset,

Reference 42

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Observation d56b9073-59a3-410f-86d2-040062363b29 · outbound

This paper cites Visualizing data using t-sne.,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Visualizing data using t-sne.,

Reference 43

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source=pdf_text observed=2026-08-01T05:58:30.094014Z digest=sha256:5d04065ee493477ac4c756b5b05048711dd67c0374f1783f4b4e6109e2cc2f3e

Observation 2e812d2b-f3ab-4aed-bff1-9fb6d05b0378 · outbound

This paper cites Learning with fantasy: Semantic-aware virtual contrastive constraint for few-shot class- incremental learning,.

Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning Learning with fantasy: Semantic-aware virtual contrastive constraint for few-shot class- incremental learning,

Reference 44

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source=pdf_text observed=2026-08-01T05:58:30.183175Z digest=sha256:fe54cff9ed86b748891cb3e4f9041e216f80f1144510aefafd9c679f050d0fde

Pith citing papers

No inbound Pith citation observations are available.