Paper Citation Record · LEDGER

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions

As of 8 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2505.23031.

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

pith.paper-citation-record.v1
2505.23031 v1

Coverage vector

measured 33 of 33 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-07T12:59:24.523270Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

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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

33 of 33 outbound references displayed

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External citation measurements

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Outbound references

Observation 7fe6993c-1b85-4fb5-95f8-c7d0fa2e2b84 · outbound

This paper cites Co-training for demographic classification using deep learning from la- bel proportions.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Co-training for demographic classification using deep learning from la- bel proportions

Reference 1

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Observation 588be4d3-0758-405f-8f14-d04ec9afe3b5 · outbound

This paper cites Mixbag: Bag-level data augmentation for learning from label proportions.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Mixbag: Bag-level data augmentation for learning from label proportions

Reference 2

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Observation 7e574922-38c4-4dda-99e8-4f946316f08b · outbound

This paper cites A fast iterative shrinkage- thresholding algorithm for linear inverse problems.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions A fast iterative shrinkage- thresholding algorithm for linear inverse problems

Reference 3

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Observation f9553a35-472c-4e36-b764-10f78b129b29 · outbound

This paper cites Easy learning from label proportions.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Easy learning from label proportions

Reference 4

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Observation aba36a8f-92bb-4807-b35a-c4d132aa88ae · outbound

This paper cites Your” flamingo” is my” bird”: Fine-grained, or not.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Your” flamingo” is my” bird”: Fine-grained, or not

Reference 5

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Observation 3540d86c-1336-4386-9726-68c7262115e0 · outbound

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

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Fet-fgvc: Feature-enhanced transformer for fine-grained visual classification

Reference 6

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Observation d62f46c3-ed8b-4911-b2f9-92c94114da16 · outbound

This paper cites An iterative thresholding algorithm for linear inverse prob- lems with a sparsity constraint.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions An iterative thresholding algorithm for linear inverse prob- lems with a sparsity constraint

Reference 7

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Observation 232f86eb-7221-41ca-bc92-cdabf93d73a7 · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 8

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Observation a6be08c8-b704-4b19-a7cb-e72fdb606252 · outbound

This paper cites Fine-grained visual classification via progressive multi-granularity train- ing of jigsaw patches.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Fine-grained visual classification via progressive multi-granularity train- ing of jigsaw patches

Reference 9

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Observation c4605734-a5d8-47a4-a087-9aea0a3a914b · outbound

This paper cites Fair comparison: Quantifying variance in re- sults for fine-grained visual categorization.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Fair comparison: Quantifying variance in re- sults for fine-grained visual categorization

Reference 10

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Observation e73d838d-3970-4fb8-9c87-441ea67cb382 · outbound

This paper cites Transfg: A trans- former architecture for fine-grained recognition.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Transfg: A trans- former architecture for fine-grained recognition

Reference 11

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Observation fde583e3-d869-4a04-a957-9c47f73ed6fa · outbound

This paper cites Granularity-aware distillation and structure modeling region proposal network for fine-grained image classifica- tion.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Granularity-aware distillation and structure modeling region proposal network for fine-grained image classifica- tion

Reference 12

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Observation 191c8421-c77e-49b0-bb86-6d1242f6ad83 · outbound

This paper cites 3d object representations for fine-grained categorization.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions 3d object representations for fine-grained categorization

Reference 13

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Observation 12e048b9-f78e-47bb-8201-c95bb1414b9c · outbound

This paper cites Temporal Ensembling for Semi-Supervised Learning.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Temporal Ensembling for Semi-Supervised Learning

Reference 14

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Observation 1a9d1e63-9361-40b5-8086-3c0c41ce9b47 · outbound

This paper cites Temporal ensembling for semi- supervised learning.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Temporal ensembling for semi- supervised learning

Reference 15

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Observation 0cb2e33e-0048-4c93-8911-f425d3b0d3fe · outbound

This paper cites Llp-gan: a gan-based algorithm for learning from label proportions.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Llp-gan: a gan-based algorithm for learning from label proportions

Reference 16

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Observation 5f18f697-b899-40bd-a141-141e10668e75 · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 17

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This paper cites Fine-Grained Visual Classification of Aircraft.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Fine-Grained Visual Classification of Aircraft

Reference 18

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Observation 4697ffca-0ee0-4a07-945e-0d8c54e30bd3 · outbound

This paper cites From softmax to sparsemax: A sparse model of attention and multi-label clas- sification.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions From softmax to sparsemax: A sparse model of attention and multi-label clas- sification

Reference 19

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Observation 89961863-d8f5-4ab6-8f52-fb6bc658b232 · outbound

This paper cites Ssfe-net: Self-supervised feature enhancement for ultra-fine-grained few-shot class incremental learning.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Ssfe-net: Self-supervised feature enhancement for ultra-fine-grained few-shot class incremental learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:25.970446Z

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Observation 78400918-19fa-42f6-94c0-ac1224a52454 · outbound

This paper cites SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification

Reference 21

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Observation a8dd0b40-75b0-43ba-8e6d-03462f630804 · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Learn- ing transferable visual models from natural language super- vision

Reference 22

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This paper cites Fine-grained recognition: Multi-granularity labels and category similarity matrix.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Fine-grained recognition: Multi-granularity labels and category similarity matrix

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:25.834936Z

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Observation 8031acc3-39c2-495d-aaf6-4eb10fb12016 · outbound

This paper cites Interweaving insights: high-order feature interaction for fine-grained vi- sual recognition.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Interweaving insights: high-order feature interaction for fine-grained vi- sual recognition

Reference 24

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Observation de28ee19-6c74-42ea-9ad3-2a2145f6bb08 · outbound

This paper cites Learning from label proportions with consistency regularization.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Learning from label proportions with consistency regularization

Reference 25

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Observation df92a9cc-646a-4d1f-9606-4d90873e2aea · outbound

This paper cites Visualiz- ing data using t-sne.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Visualiz- ing data using t-sne

Reference 26

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

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions The caltech-ucsd birds-200-2011 dataset

Reference 27

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This paper cites Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers

Reference 28

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This paper cites Mask-cnn: Localizing parts and selecting descriptors for fine-grained bird species categorization.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Mask-cnn: Localizing parts and selecting descriptors for fine-grained bird species categorization

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:25.395966Z

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Observation e6f7ecf4-2f81-45e2-bdf0-42ad703f71bc · outbound

This paper cites Context-Semantic Quality Awareness Network for Fine-Grained Visual Categorization.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Context-Semantic Quality Awareness Network for Fine-Grained Visual Categorization

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:59:24.732020Z

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Observation 0c5e4db6-a7b7-4186-ad6f-c1b193f4e6de · outbound

This paper cites \proptosvm for learning with label proportions.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions \proptosvm for learning with label proportions

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:25.279027Z

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Observation 14d9fd1e-7604-44f9-86c2-1ffb4c7dea77 · outbound

This paper cites On Learning from Label Proportions.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions On Learning from Label Proportions

Reference 32

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Observation 89f7bdb3-fcb5-4edf-a4a5-58d1ec4fe7ae · outbound

This paper cites Learning from label proportions by learning with label noise.

Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions Learning from label proportions by learning with label noise

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:59:25.184185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

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