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Paper Citation Record · LEDGER

Compositional Attribute Imbalance in Vision Datasets

As of 18 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2506.14418.

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

pith.paper-citation-record.v1
2506.14418 v1

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:57:49.474730Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

63 of 63 outbound references displayed

  • verified exact0
  • verified fuzzy36
  • unresolved26
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3ce66dfa-6ade-4ddb-b39f-568c084f4b9f · outbound

This paper cites Food-101--mining discriminative components with random forests.

Compositional Attribute Imbalance in Vision Datasets Food-101--mining discriminative components with random forests

Reference 1

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Unavailable: canonical work link unavailable.

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Observation dd61f789-553d-4fbd-ada0-2f993b7c457f · outbound

This paper cites Ace: Ally complementary experts for solving long-tailed recognition in one-shot.

Compositional Attribute Imbalance in Vision Datasets Ace: Ally complementary experts for solving long-tailed recognition in one-shot

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation d8202990-f534-4b50-aa59-6733ac59ba70 · outbound

This paper cites Learning imbalanced datasets with label-distribution-aware margin loss.

Compositional Attribute Imbalance in Vision Datasets Learning imbalanced datasets with label-distribution-aware margin loss

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 88a88f07-9c55-4158-8f18-66f5441c10c3 · outbound

This paper cites V., Bowyer, K.

Compositional Attribute Imbalance in Vision Datasets V., Bowyer, K

Reference 4

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Observation fe6553b7-8f9f-4153-856e-1ee7fc8c85a3 · outbound

This paper cites Feature space augmentation for long-tailed data.

Compositional Attribute Imbalance in Vision Datasets Feature space augmentation for long-tailed data

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 41312ade-5037-4b5e-9eda-3470fa88a272 · outbound

This paper cites Describing textures in the wild.

Compositional Attribute Imbalance in Vision Datasets Describing textures in the wild

Reference 6

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Observation 9002df26-4206-4744-bcfc-c3aba6a19bd1 · outbound

This paper cites Parametric contrastive learning.

Compositional Attribute Imbalance in Vision Datasets Parametric contrastive learning

Reference 7

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Observation 6df278b1-a9a9-45ff-8532-a7db1ee03fbf · outbound

This paper cites Large scale fine-grained categorization and domain-specific transfer learning.

Compositional Attribute Imbalance in Vision Datasets Large scale fine-grained categorization and domain-specific transfer learning

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.217038Z digest=sha256:e1c08102adbbf99195111225e07a7bf53117bd319be621651d031f0319ed079f

Observation 43d6eac6-9f6d-4baf-a918-b639b47bc630 · outbound

This paper cites Class-balanced loss based on effective number of samples.

Compositional Attribute Imbalance in Vision Datasets Class-balanced loss based on effective number of samples

Reference 9

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Observation ecd4a92c-dd71-427f-bf32-db1e8c64e288 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Compositional Attribute Imbalance in Vision Datasets Imagenet: A large-scale hierarchical image database

Reference 10

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Observation d7d08380-b524-4204-8f81-c034d8202397 · outbound

This paper cites Class rectification hard mining for imbalanced deep learning.

Compositional Attribute Imbalance in Vision Datasets Class rectification hard mining for imbalanced deep learning

Reference 11

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation e069d35e-4415-4d21-9530-0be54bc262a7 · outbound

This paper cites H., Williams, K., Corke, F.

Compositional Attribute Imbalance in Vision Datasets H., Williams, K., Corke, F

Reference 12

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 967d1623-93b0-42ec-b35b-0a9b98c9e875 · outbound

This paper cites The foundations of cost-sensitive learning.

Compositional Attribute Imbalance in Vision Datasets The foundations of cost-sensitive learning

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 526e6651-7fca-4ea5-a2d9-ae7b67dc1e75 · outbound

This paper cites A multiple resampling method for learning from imbalanced data sets.

Compositional Attribute Imbalance in Vision Datasets A multiple resampling method for learning from imbalanced data sets

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 59f0f3d3-4f03-48e5-ab02-0f10ba2eb9c0 · outbound

This paper cites Learning to segment the tail.

Compositional Attribute Imbalance in Vision Datasets Learning to segment the tail

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 73f16d5c-0619-46d9-b7ed-4af6103902c7 · outbound

This paper cites C., and Tang, X.

Compositional Attribute Imbalance in Vision Datasets C., and Tang, X

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 840cbc67-341b-4a53-992b-5d63d5cd2160 · outbound

This paper cites Z., Mahmood, A., and Nandakumar, K.

Compositional Attribute Imbalance in Vision Datasets Z., Mahmood, A., and Nandakumar, K

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation d0ca6e3a-9d39-4627-8497-8b6d17e282f6 · outbound

This paper cites Exploring balanced feature spaces for representation learning.

Compositional Attribute Imbalance in Vision Datasets Exploring balanced feature spaces for representation learning

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 52ebf5bd-fa96-4349-8c8e-5a953b642f7f · outbound

This paper cites Novel dataset for fine-grained image categorization: Stanford dogs.

Compositional Attribute Imbalance in Vision Datasets Novel dataset for fine-grained image categorization: Stanford dogs

Reference 19

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 6e244fac-f815-4233-97bf-b116cf1f72d2 · outbound

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

Compositional Attribute Imbalance in Vision Datasets 3d object representations for fine-grained categorization

Reference 20

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Observation 439214d1-3654-41ad-80b1-c8cf568af71d · outbound

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

Compositional Attribute Imbalance in Vision Datasets Learning multiple layers of features from tiny images

Reference 21

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Observation 786e0185-1e67-48e5-913b-e8dc54948869 · outbound

This paper cites Focal loss for dense object detection.

Compositional Attribute Imbalance in Vision Datasets Focal loss for dense object detection

Reference 22

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source=arxiv_source observed=2026-08-15T19:57:49.282642Z digest=sha256:61bea45638f28f04a3cc267226c7580be218b875ad2663a0531b76049077024b

Observation e9d4c03f-83c0-47b0-be4e-e131f0312957 · outbound

This paper cites Gistnet: a geometric structure transfer network for long-tailed recognition.

Compositional Attribute Imbalance in Vision Datasets Gistnet: a geometric structure transfer network for long-tailed recognition

Reference 23

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.287194Z digest=sha256:91ef9da6f6ad79fb692c0a136572d04c28b4cfde35d306834ab93223830abbc0

Observation dc17dea3-7259-413d-ab9f-19cd566b7523 · outbound

This paper cites Deep representation learning on long-tailed data: A learnable embedding augmentation perspective.

Compositional Attribute Imbalance in Vision Datasets Deep representation learning on long-tailed data: A learnable embedding augmentation perspective

Reference 24

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raw_fallback, observed 2026-08-15T19:57:50.164857Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 32e032c5-7503-4499-b3b0-2bb226e57bc2 · outbound

This paper cites Deepfashion: Powering robust clothes recognition and retrieval with rich annotations.

Compositional Attribute Imbalance in Vision Datasets Deepfashion: Powering robust clothes recognition and retrieval with rich annotations

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation a01166f3-314c-4240-b739-3a68de7f94a5 · outbound

This paper cites Handling Inter-class and Intra-class Imbalance in Class-imbalanced Learning.

Compositional Attribute Imbalance in Vision Datasets Handling Inter-class and Intra-class Imbalance in Class-imbalanced Learning

Reference 26

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Observation 41b741d0-5092-4e45-99fa-887a110fe007 · outbound

This paper cites Delving into semantic scale imbalance.

Compositional Attribute Imbalance in Vision Datasets Delving into semantic scale imbalance

Reference 27

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verified fuzzy
raw_fallback, observed 2026-08-15T19:57:50.132834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation b8f85e69-bd52-498d-a4ee-2723e368f771 · outbound

This paper cites Geometric prior guided feature representation learning for long-tailed classification.

Compositional Attribute Imbalance in Vision Datasets Geometric prior guided feature representation learning for long-tailed classification

Reference 28

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raw_fallback, observed 2026-08-15T19:57:50.116988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 5843501a-f216-482b-a4c7-9ca26f77c4d0 · outbound

This paper cites Feature distribution representation learning based on knowledge transfer for long-tailed classification.

Compositional Attribute Imbalance in Vision Datasets Feature distribution representation learning based on knowledge transfer for long-tailed classification

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.315201Z digest=sha256:b0327e1f2b983e556e2b6e5c5d986ee8ad51cc3039efa9c5c7983d28e4bc715f

Observation d1a2d58c-4e9c-4f81-8088-90e593b90fed · outbound

This paper cites Predicting and enhancing the fairness of dnns with the curvature of perceptual manifolds.

Compositional Attribute Imbalance in Vision Datasets Predicting and enhancing the fairness of dnns with the curvature of perceptual manifolds

Reference 30

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raw_fallback, observed 2026-08-15T19:57:50.100407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation cac10cef-4538-4249-bda1-135d7d7f8583 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

Compositional Attribute Imbalance in Vision Datasets Fine-Grained Visual Classification of Aircraft

Reference 31

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no resolver link, observed 2026-08-15T19:57:49.323843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:57:49.323843Z digest=sha256:80b30073b13a57d8c5b7b98f232473e3a9e2aa9234a4871d572036330ea71c66

Observation 004b3037-dccc-4924-b16d-b9ea46f12e39 · outbound

This paper cites and Zisserman, A.

Compositional Attribute Imbalance in Vision Datasets and Zisserman, A

Reference 32

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no resolver link, observed 2026-08-15T19:57:49.328723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:57:49.328723Z digest=sha256:35e29e8b382a7eb90c17cb7fd54c40ed88921b140f80dcef744ab39fd07af3da

Observation e79059a7-61a9-47f6-a80a-798f0e6915c3 · outbound

This paper cites Factors in finetuning deep model for object detection with long-tail distribution.

Compositional Attribute Imbalance in Vision Datasets Factors in finetuning deep model for object detection with long-tail distribution

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:57:50.075188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.332941Z digest=sha256:d13d8fa5e60fcee4b1d44ab3c45da777ec0c0e8894758133b24bfbc8669cf715

Observation 57abc3f7-fc37-48eb-a471-8e7f9713c09d · outbound

This paper cites an unresolved cited work.

Compositional Attribute Imbalance in Vision Datasets Unresolved cited work

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.337501Z digest=sha256:1dcaf137165c7e6f83a6d98397008ed0f81bbc955b753d44bd31c0f072b375e5

Observation d8d9c62b-c61d-445a-b62a-9bde948f8eb2 · outbound

This paper cites M., Vedaldi, A., Zisserman, A., and Jawahar, C.

Compositional Attribute Imbalance in Vision Datasets M., Vedaldi, A., Zisserman, A., and Jawahar, C

Reference 35

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no resolver link, observed 2026-08-15T19:57:49.342220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:57:49.342220Z digest=sha256:d6e830649bd887802ee9bcbc329fd98510d26cab0193c5fb3f408a1226bf337f

Observation d91af0dd-45be-445c-8c30-9dd0b608fd09 · outbound

This paper cites Learning to predict visual attributes in the wild.

Compositional Attribute Imbalance in Vision Datasets Learning to predict visual attributes in the wild

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-15T19:57:50.034856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.346465Z digest=sha256:881ca354f6baa8456aaadd1011e045bed358c6077718f6bf9a5ae97e82701125

Observation e7bd3aa4-e997-47c8-b00c-00d5a5c637df · outbound

This paper cites A., and Gao, X.

Compositional Attribute Imbalance in Vision Datasets A., and Gao, X

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:57:50.019337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.351035Z digest=sha256:1533e52eede5c0267160a51128d0cd74579e481b6d1a7319a8c07c9a77650d17

Observation 5e0c80fa-5abf-4df0-82c1-5cf2b229c998 · outbound

This paper cites W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.

Compositional Attribute Imbalance in Vision Datasets W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-15T19:57:49.355388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:57:49.355388Z digest=sha256:f9f005b31ea42b00fff9240e734474f4b161add46a8a98a9f5a484c000bb85e6

Observation ddd9af99-84cd-42d9-80ce-b36332551e08 · outbound

This paper cites Balanced meta-softmax for long-tailed visual recognition.

Compositional Attribute Imbalance in Vision Datasets Balanced meta-softmax for long-tailed visual recognition

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-15T19:57:49.359826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:57:49.359826Z digest=sha256:728c182b42820e05cc4c8b80585de36d11a883a0bef6a5230967c9d8fa220b97

Observation 1502a839-353b-4d38-b575-44dd8077425d · outbound

This paper cites Class-wise difficulty-balanced loss for solving class-imbalance.

Compositional Attribute Imbalance in Vision Datasets Class-wise difficulty-balanced loss for solving class-imbalance

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:57:49.983707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.364144Z digest=sha256:9828dfe1f8c2c0a55fd51190f6f1631df6147d4cbb7732d4676aeafd768418af

Observation 1065e3e0-8bd1-44a7-bc46-e9050fd59d09 · outbound

This paper cites Class-difficulty based methods for long-tailed visual recognition.

Compositional Attribute Imbalance in Vision Datasets Class-difficulty based methods for long-tailed visual recognition

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:57:49.968222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.368645Z digest=sha256:62d9248ef151475b06b316846bf89cb3b20c7c34a77e36a8c69959e991ad7284

Observation 84980ba9-125f-49d7-8e94-01eefb22581d · outbound

This paper cites Equalization loss for long-tailed object recognition.

Compositional Attribute Imbalance in Vision Datasets Equalization loss for long-tailed object recognition

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:57:49.952699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.376180Z digest=sha256:0f757ca547486ce675ab7ea8e7002862f883595ae68344a01b9b6c8cfb3bb08e

Observation abe12c74-2966-4fac-8747-683b879e6330 · outbound

This paper cites Invariant feature learning for generalized long-tailed classification.

Compositional Attribute Imbalance in Vision Datasets Invariant feature learning for generalized long-tailed classification

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:57:49.937188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.380953Z digest=sha256:db6276b7f3c23a31c1f27bf54a64c7949cea4260b73d0e5bc05a87f9ae5a96d9

Observation 4e556d77-cd53-44ad-a88d-715f39ee73fb · outbound

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

Compositional Attribute Imbalance in Vision Datasets The caltech-ucsd birds-200-2011 dataset

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-15T19:57:49.385531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:57:49.385531Z digest=sha256:aad0375112393aa373c46ba889941279db200882e3ad809eab7fc6aab7f72d30

Observation 84b105ca-5de8-465a-9365-52c7752a48d3 · outbound

This paper cites The devil is in classification: A simple framework for long-tail instance segmentation.

Compositional Attribute Imbalance in Vision Datasets The devil is in classification: A simple framework for long-tail instance segmentation

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:57:49.910484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.389941Z digest=sha256:f4b1d5f830c1fa39a887a4dd38efc7f27ef99de9dc3a0497b3d96f99a9cb5fd4

Observation 0f8862c1-d048-438f-b00b-cd0d7ceddae8 · outbound

This paper cites Long-tailed Recognition by Routing Diverse Distribution-Aware Experts.

Compositional Attribute Imbalance in Vision Datasets Long-tailed Recognition by Routing Diverse Distribution-Aware Experts

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-15T19:57:49.394395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:57:49.394395Z digest=sha256:5cba9fed6130cf4bc9d1abb794f1a2b5c7081ce5eabeea41330220f97841e10a

Observation f71bdf4c-7528-40dc-9eca-2c10e67a9caa · outbound

This paper cites A., Oliva, A., and Torralba, A.

Compositional Attribute Imbalance in Vision Datasets A., Oliva, A., and Torralba, A

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-15T19:57:49.399151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:57:49.399151Z digest=sha256:14613cdb93701af0d2eede16c76fc085f07e54641ac266c8e7d6239a45ca6f3d

Observation 7ef08bdc-0e28-45e7-a615-1d2f109bcbda · outbound

This paper cites Defect spectrum: a granular look of large-scale defect datasets with rich semantics.

Compositional Attribute Imbalance in Vision Datasets Defect spectrum: a granular look of large-scale defect datasets with rich semantics

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:57:49.883288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.403795Z digest=sha256:083370cdabc6e81ae12b11e6d8a0dc9f55e6f7cf7b7a59218015a8d185a0c7b4

Observation 0f710b47-c47c-4795-8e71-b01d13cd2372 · outbound

This paper cites and Xu, Z.

Compositional Attribute Imbalance in Vision Datasets and Xu, Z

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:57:49.867598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.408213Z digest=sha256:4daf99dbeb81fc66551aa219670c11b9904c5338a46bab2b6e4f7582adbb96f1

Observation f8a18c5a-e15d-4b0e-bf63-12955d10eb83 · outbound

This paper cites Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning.

Compositional Attribute Imbalance in Vision Datasets Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-15T19:57:49.413116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:57:49.413116Z digest=sha256:0542e1f6314a691db4b0e309386bf021f09290dbd57a91ce4cbdf5c6be08d170

Observation 24fbcc1e-32d0-4c4c-8708-a34b006e5317 · outbound

This paper cites Feature transfer learning for face recognition with under-represented data.

Compositional Attribute Imbalance in Vision Datasets Feature transfer learning for face recognition with under-represented data

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:57:49.849542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.418027Z digest=sha256:0d31475339d29cf451428b885be6202f07c6ce8d42d550bb762f5f70209bdbf2

Observation 8f7f9c45-62ac-4d64-867f-e179f8e03d94 · outbound

This paper cites an unresolved cited work.

Compositional Attribute Imbalance in Vision Datasets Unresolved cited work

Reference 52

Resolution
unresolved
raw_fallback, observed 2026-08-15T19:57:49.833917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.423312Z digest=sha256:39f9f7330010ebd22350da1f321576f9ce61b47fa6656ce8a64466e89dd01d6c

Observation c4c73c28-9773-4c6e-8180-09db100724d6 · outbound

This paper cites Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition.

Compositional Attribute Imbalance in Vision Datasets Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-15T19:57:49.427745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:57:49.427745Z digest=sha256:0133aa4d8a0093cca4df2cb73928b9c3eb4ac02365572acb5d2d13092bfd5d19

Observation 267c0bda-58c9-4cdb-9a5f-7d84cf9ab521 · outbound

This paper cites Deep Long-Tailed Learning: A Survey.

Compositional Attribute Imbalance in Vision Datasets Deep Long-Tailed Learning: A Survey

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-15T19:57:49.432524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:57:49.432524Z digest=sha256:3c3d4ecf20292427b8b965da516de3b0611a54860fc43903bbd278401823ede5

Observation 43da577b-10bc-450a-a248-9c70a3e8af8d · outbound

This paper cites Concept-guided prompt learning for generalization in vision-language models.

Compositional Attribute Imbalance in Vision Datasets Concept-guided prompt learning for generalization in vision-language models

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:57:49.818712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.437531Z digest=sha256:eb43e1be4379c02485f0641f986a52c31e0ffe8b869f1dc37278a9f1179e8989

Observation b522eb3c-0c5e-4edb-a450-0799ac92d3a9 · outbound

This paper cites and Pfister, T.

Compositional Attribute Imbalance in Vision Datasets and Pfister, T

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:57:49.803402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.442078Z digest=sha256:42f9454789cbb69d11528174dd5d45a6b3bd668431109520538e4e8b5ee1d31c

Observation 299d335b-5a38-4bfd-8c62-7a7100ae7c5e · outbound

This paper cites A large-scale attribute dataset for zero-shot learning.

Compositional Attribute Imbalance in Vision Datasets A large-scale attribute dataset for zero-shot learning

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:57:49.788172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.446795Z digest=sha256:debeb84c6ee330762889868dc29579662960e1d0bc8cd00951fba6f916a0c1e9

Observation 6b47ee14-b9e3-4efd-8e53-c3f2533a126d · outbound

This paper cites C., Tan, M., and Huang, J.

Compositional Attribute Imbalance in Vision Datasets C., Tan, M., and Huang, J

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-15T19:57:49.451548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:57:49.451548Z digest=sha256:8b72649ae5c7d571609fd64e87fb30418588e0a006ec369cb23e45c9bae3d864

Observation 7a2dfc16-58e6-4e45-b171-3cf0e8c0c461 · outbound

This paper cites Improving calibration for long-tailed recognition.

Compositional Attribute Imbalance in Vision Datasets Improving calibration for long-tailed recognition

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:57:49.763886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.456079Z digest=sha256:06b24f30fbe606b243bffd24ae132676175a20feb4bcb3ac064fe3a97b437c08

Observation 9f44469d-ea8c-4a60-a139-735ea9cbcc8b · outbound

This paper cites Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition.

Compositional Attribute Imbalance in Vision Datasets Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:57:49.748665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.460839Z digest=sha256:8732f117f91aa912212b057eedf420dc7b1e28fc181e91fc2b688e627678af94

Observation d7bbb2fe-5b76-485d-8279-097200f624d8 · outbound

This paper cites Global-local framework for medical image segmentation with intra-class imbalance problem.

Compositional Attribute Imbalance in Vision Datasets Global-local framework for medical image segmentation with intra-class imbalance problem

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T19:57:49.733052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T19:57:49.465542Z digest=sha256:e63049ad7edffcfc978260fab7eafe15260cfaf7294c50265058f81b838f170a

Observation e928990d-e6a5-45ac-86b2-39e6a518dd68 · outbound

This paper cites and Liu, X.-Y.

Compositional Attribute Imbalance in Vision Datasets and Liu, X.-Y

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-15T19:57:49.470222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:57:49.470222Z digest=sha256:67811e482667e6085d4cc1ab2ef4ec2744dd7c0068a36fa9b4e8686db137103f

Observation c256067f-3bc9-47f6-99a4-a3c154962bba · outbound

This paper cites write newline.

Compositional Attribute Imbalance in Vision Datasets write newline

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-15T19:57:49.474730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:57:49.474730Z digest=sha256:a3da823df9d694e99640f342da1372cf5df4728ad1f9a474a1f7fc5295aceaaf

Pith citing papers

No inbound Pith citation observations are available.