Paper Citation Record · LEDGER

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification

As of 7 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2507.11845.

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

pith.paper-citation-record.v1
2507.11845 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:05:07.496407Z

measured 34 of 34 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

34 of 34 outbound references displayed

verified exact4
verified fuzzy20
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 15de8740-e00f-4550-8308-0255cdd29891 · outbound

This paper cites Few-shot open-set recognition of hyperspectral images,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Few-shot open-set recognition of hyperspectral images,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:11.939894Z

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

Observation 24c6d3fe-7565-4376-b8ab-e2ac9bef4f73 · outbound

This paper cites Toward generalized few-shot open- set object detection,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Toward generalized few-shot open- set object detection,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:11.837055Z

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Observation 1bca1979-6d79-45ac-8c72-ddeca90feb00 · outbound

This paper cites Rd-openmax: Rethinking openmax for robust realistic open-set recognition,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Rd-openmax: Rethinking openmax for robust realistic open-set recognition,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:11.693724Z

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Observation 7ffde4e7-0342-4491-a525-67e90dc15b6f · outbound

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

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Few-shot class-incremental learning from an open-set perspective,

Reference 4

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Observation ff7ecad3-f105-4fb8-b5ad-92df6f388edb · outbound

This paper cites Boosting few-shot open-set recognition with multi-relation margin loss.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Boosting few-shot open-set recognition with multi-relation margin loss

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:11.549329Z

Source-reported events for the cited work

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Observation 0a8f7daf-dc3f-425e-8992-f13f43842c3f · outbound

This paper cites Feature-semantic augmentation network for few-shot open-set recognition,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Feature-semantic augmentation network for few-shot open-set recognition,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:11.387680Z

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

Observation ecc42751-8005-471d-847a-02078555beaf · outbound

This paper cites Learning to prompt for vision- language models,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Learning to prompt for vision- language models,

Reference 7

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Observation 04b0f407-a0ff-40e9-b007-700e23c9c667 · outbound

This paper cites Conditional prompt learning for vision-language models,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Conditional prompt learning for vision-language models,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:11.223324Z

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Observation 1a60b985-2743-490d-bf98-06a3ce2b3c45 · outbound

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

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Pre-trained vision and language transformers are few-shot incremental learners,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:11.065743Z

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.

Observation b6c183aa-897c-49ea-b74e-0bf0bc2607f9 · outbound

This paper cites Self-regulating prompts: Foundational model adaptation without forgetting,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Self-regulating prompts: Foundational model adaptation without forgetting,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:10.907851Z

Source-reported events for the cited work

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Observation a21d9b64-5581-445f-8acf-3369f4da23e3 · outbound

This paper cites Maple: Multi-modal prompt learning,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Maple: Multi-modal prompt learning,

Reference 11

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Source-reported events for the cited work

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Observation 5790eab8-f955-4aa2-9d76-892c572ae9e3 · outbound

This paper cites Learning to prompt knowledge transfer for open-world continual learning,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Learning to prompt knowledge transfer for open-world continual learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:10.759712Z

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.

Observation b6eef014-42da-4b5b-b824-18967b109007 · outbound

This paper cites A survey on few- shot class-incremental learning,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification A survey on few- shot class-incremental learning,

Reference 13

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Observation 724f8781-ca80-4ad1-9a9c-c90314d1b48e · outbound

This paper cites Morgan: Meta-learning- based few-shot open-set recognition via generative adversarial network,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Morgan: Meta-learning- based few-shot open-set recognition via generative adversarial network,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:10.576936Z

Source-reported events for the cited work

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Observation b90092d5-bd34-4059-9bdb-7e3ed4ee7ae1 · outbound

This paper cites Enhance image classification via inter-class image mixup with diffusion model,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Enhance image classification via inter-class image mixup with diffusion model,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:10.370743Z

Source-reported events for the cited work

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Observation 4ba53e40-72cb-4a71-a4ff-6c77a61ca5dd · outbound

This paper cites Collaborative consortium of foundation models for open-world few-shot learning,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Collaborative consortium of foundation models for open-world few-shot learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:10.186817Z

Source-reported events for the cited work

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Observation c6068b7d-179d-4089-9e96-afc4efd3b61c · outbound

This paper cites Joint feature generation and open-set prototype learning for generalized zero-shot open-set classification,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Joint feature generation and open-set prototype learning for generalized zero-shot open-set classification,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:10.009760Z

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.

Observation 14ffddc7-ae3d-4935-b1cb-6cd3148d5422 · outbound

This paper cites BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers

Reference 18

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Source-reported events for the cited work

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Observation a0ead85f-c226-4920-8254-c51dae8f7428 · outbound

This paper cites CRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD Detection.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification CRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD Detection

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:05:08.362860Z

Source-reported events for the cited work

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Observation d54a76de-aee0-4ec4-b79d-470b9ad69894 · outbound

This paper cites An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment Classification.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment Classification

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:05:08.146253Z

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.

Observation fd9573e2-e417-4b3a-a84e-eb517cd7c95c · outbound

This paper cites Instance-Conditioned GAN Data Augmentation for Representation Learning.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Instance-Conditioned GAN Data Augmentation for Representation Learning

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:05:07.956595Z

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.

Observation c70915ac-a093-4783-9685-80cd5a39c9ce · outbound

This paper cites Meta-learning with latent embedding optimization,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Meta-learning with latent embedding optimization,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:09.807162Z

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.

Observation 58eaae22-d3c6-48ac-9dea-7606d9ad101e · outbound

This paper cites Deit iii: Revenge of the vit,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Deit iii: Revenge of the vit,

Reference 23

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Source-reported events for the cited work

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Observation 7c48e8c7-1d86-4ed5-a764-2925ea3aa7a1 · outbound

This paper cites Masked au- toencoders are scalable vision learners,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Masked au- toencoders are scalable vision learners,

Reference 24

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Observation 497cdcde-a204-4e54-a3e6-ec016ca38794 · outbound

This paper cites Few-shot open-set recognition of hyperspectral images with outlier calibration network,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Few-shot open-set recognition of hyperspectral images with outlier calibration network,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:09.615547Z

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.

Observation 05a79c62-ae6d-4397-88ed-c1293b936d0e · outbound

This paper cites Boosting few-shot open-set recognition with multi-relation margin loss,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Boosting few-shot open-set recognition with multi-relation margin loss,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:09.432009Z

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.

Observation 8468f092-160b-4651-8001-4789c018945f · outbound

This paper cites Few-shot open-set recognition by trans- formation consistency,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Few-shot open-set recognition by trans- formation consistency,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:09.234079Z

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.

Observation 244f6dbb-4c9d-4222-ab5d-f0fb64b9bc26 · outbound

This paper cites Recon- struction guided meta-learning for few shot open set recognition,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Recon- struction guided meta-learning for few shot open set recognition,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:09.001536Z

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.

Observation 72819482-8861-4c16-b319-f1e5aeacbca5 · outbound

This paper cites Glocal energy-based learning for few-shot open-set recognition,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Glocal energy-based learning for few-shot open-set recognition,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:08.803426Z

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.

Observation 216175ef-3119-40ca-83b6-368b187b4fee · outbound

This paper cites Learning transferable visual models from natural language supervision,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Learning transferable visual models from natural language supervision,

Reference 30

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Observation f653cd70-d0d1-4204-bb5a-ce18f0cc0a51 · outbound

This paper cites Cross-silo prototypical calibration for federated learning with non-iid data,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Cross-silo prototypical calibration for federated learning with non-iid data,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:08.561682Z

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.

Observation 9eb6fc84-ee0a-4644-9875-aafb50a5e208 · outbound

This paper cites Cross- training with multi-view knowledge fusion for heterogenous federated learning,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Cross- training with multi-view knowledge fusion for heterogenous federated learning,

Reference 32

Resolution: verified exact; raw_fallback, observed 2026-08-06T17:05:07.773341Z

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.

Observation 50d6ccb2-37e4-4244-9c22-e02241f86435 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization,.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 33

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Source-reported events for the cited work

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Observation ccad7282-0de3-4ffa-9754-bf3c228d1b4d · outbound

This paper cites Visualizing data using t-sne.

ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification Visualizing data using t-sne

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:07.496407Z

Source-reported events for the cited work

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Pith citing papers

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