Prior2Posterior: Model Prior Correction for Long-Tailed Learning | Paper Citation Record

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This paper cites Long-tailed recognition via weight balancing.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Long-tailed recognition via weight balancing

Reference 1

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Observation 528119b9-9857-4e6b-b404-274c169c7b29 · outbound

This paper cites Robust loss function for class imbalanced semantic seg- mentation and image classification.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Robust loss function for class imbalanced semantic seg- mentation and image classification

Reference 2

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Observation 019dfc82-f648-4154-a926-d8f089cdb527 · outbound

This paper cites A systematic study of the class imbalance problem in convo- lutional neural networks.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning A systematic study of the class imbalance problem in convo- lutional neural networks

Reference 3

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Observation 4611ef65-49df-451a-9c8f-227cd6fa6a5e · outbound

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

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Learning imbalanced datasets with label- distribution-aware margin loss

Reference 4

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Observation 387fc45e-a5d4-4c7d-b759-c3deea3e41a2 · outbound

This paper cites Area: Adaptive reweight- ing via effective area for long-tailed classification.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Area: Adaptive reweight- ing via effective area for long-tailed classification

Reference 5

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Observation c2dcbb3b-73a6-4076-bdf7-aedb85e5953e · outbound

This paper cites Reslt: Residual learning for long-tailed recogni- tion.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Reslt: Residual learning for long-tailed recogni- tion

Reference 6

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

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Class-balanced loss based on effective number of samples

Reference 7

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Observation 0e303561-1517-4b17-a18c-852d1a493a70 · outbound

This paper cites Global and local mixture consistency cumulative learning for long-tailed visual recognitions.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Global and local mixture consistency cumulative learning for long-tailed visual recognitions

Reference 8

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Observation da4b4132-6b35-4a63-be0c-14e45256b35b · outbound

This paper cites On calibration of modern neural networks.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning On calibration of modern neural networks

Reference 9

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Observation ee62d4b4-85f8-41fe-90ff-a531d1bccf7c · outbound

This paper cites Disentangling label dis- tribution for long-tailed visual recognition.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Disentangling label dis- tribution for long-tailed visual recognition

Reference 10

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Observation 7e18bf6f-b28c-4d12-a2d0-ee170cb7677a · outbound

This paper cites Learning deep representation for imbalanced classi- fication.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Learning deep representation for imbalanced classi- fication

Reference 11

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Observation 4f968e5f-1502-4467-b786-3d9679a7d3df · outbound

This paper cites Decoupling Representation and Classifier for Long-Tailed Recognition.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Decoupling Representation and Classifier for Long-Tailed Recognition

Reference 12

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Observation b8fcb5bf-5813-42bf-8ca5-780c5f4218db · outbound

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

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Learning multiple layers of features from tiny images

Reference 13

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This paper cites Neural network classification and prior class probabilities.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Neural network classification and prior class probabilities

Reference 14

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This paper cites Long-tailed visual recognition via gaussian clouded logit adjustment.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Long-tailed visual recognition via gaussian clouded logit adjustment

Reference 15

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Observation f32b9324-cf45-47d1-b2f0-a4b24e00857d · outbound

This paper cites Metasaug: Meta seman- tic augmentation for long-tailed visual recognition.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Metasaug: Meta seman- tic augmentation for long-tailed visual recognition

Reference 16

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Observation ad95231a-c290-45e2-aaa4-e4af2c7b44d0 · outbound

This paper cites Focal loss for dense object detection.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Focal loss for dense object detection

Reference 17

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Observation 70112c32-df75-481f-b3ae-e4b729bda8ed · outbound

This paper cites Inducing neural collapse in deep long- tailed learning.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Inducing neural collapse in deep long- tailed learning

Reference 18

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Observation 28f20d1f-b957-42a4-a550-af6faae5a866 · outbound

This paper cites Large-scale long-tailed recognition in an open world.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Large-scale long-tailed recognition in an open world

Reference 19

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Observation 14e5b90a-a70a-4558-9ca6-fef309d33486 · outbound

This paper cites Curvature-balanced feature manifold learning for long-tailed classification.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Curvature-balanced feature manifold learning for long-tailed classification

Reference 20

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This paper cites knn approach to unbalanced data distributions: a case study involving information extrac- tion.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning knn approach to unbalanced data distributions: a case study involving information extrac- tion

Reference 21

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This paper cites When does imbalanced data require more than cost-sensitive learning.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning When does imbalanced data require more than cost-sensitive learning

Reference 22

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Observation 9730ac3c-17cf-4d63-898f-cf0b242ef0eb · outbound

This paper cites Long-tail learning via logit adjustment.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Long-tail learning via logit adjustment

Reference 23

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Observation e3fcf2b0-fab4-4110-9989-9396f2e6a230 · outbound

This paper cites Decoupled Training for Long-Tailed Classification With Stochastic Representations.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Decoupled Training for Long-Tailed Classification With Stochastic Representations

Reference 24

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Observation 9f4ac7af-b6fc-419c-bd66-c115511b93e3 · outbound

This paper cites The majority can help the minority: Context-rich minority oversampling for long-tailed classifi- cation.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning The majority can help the minority: Context-rich minority oversampling for long-tailed classifi- cation

Reference 25

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Observation 8157fea0-e826-4d38-9ae7-07922db0b842 · outbound

This paper cites Feature directions matter: Long-tailed learning via rotated balanced represen- tation.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Feature directions matter: Long-tailed learning via rotated balanced represen- tation

Reference 26

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Observation e800ac07-638f-448b-9ec1-708316fee2ba · outbound

This paper cites Optimal transport for long-tailed recognition with learnable cost matrix.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Optimal transport for long-tailed recognition with learnable cost matrix

Reference 27

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Observation 5d1ab547-6562-442e-bc74-df3d62f083bf · outbound

This paper cites Escaping saddle points for effective generalization on class- imbalanced data.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Escaping saddle points for effective generalization on class- imbalanced data

Reference 28

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Observation 38aa92e4-3658-487b-a708-9f1e6432a12e · outbound

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

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Balanced meta-softmax for long-tailed visual recogni- tion

Reference 29

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Observation 8af51d58-c63a-4b83-b84a-1557e5b3ee4b · outbound

This paper cites Neural net- work classifiers estimate bayesian a posteriori probabilities.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Neural net- work classifiers estimate bayesian a posteriori probabilities

Reference 30

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Observation f2ab269e-e8c9-4377-86b1-c7a605d69216 · outbound

This paper cites Distributional robustness loss for long-tail learning.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Distributional robustness loss for long-tail learning

Reference 31

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This paper cites Relative entropic optimal transport: a (prior-aware) match- ing perspective to (unbalanced) classification.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Relative entropic optimal transport: a (prior-aware) match- ing perspective to (unbalanced) classification

Reference 32

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This paper cites Meta-weight-net: Learn- ing an explicit mapping for sample weighting.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Meta-weight-net: Learn- ing an explicit mapping for sample weighting

Reference 33

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This paper cites Rethinking the inception ar- chitecture for computer vision.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Rethinking the inception ar- chitecture for computer vision

Reference 34

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Observation 36fc4394-0885-4746-a677-5042d80ce773 · outbound

This paper cites Long- tailed classification by keeping the good and removing the bad momentum causal effect.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Long- tailed classification by keeping the good and removing the bad momentum causal effect

Reference 35

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Observation ecf18e03-3996-4acd-93c4-ea6569aeab1a · outbound

This paper cites Improving tail-class representation with centroid contrastive learning.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Improving tail-class representation with centroid contrastive learning

Reference 36

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Observation 84b76aec-c90f-4bbc-b237-c6adfe950ee8 · outbound

This paper cites The inaturalist species classification and de- tection dataset.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning The inaturalist species classification and de- tection dataset

Reference 37

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Observation 4e6b9c6d-df83-4188-bece-166be0a78937 · outbound

This paper cites Solar: Sinkhorn la- bel refinery for imbalanced partial-label learning.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Solar: Sinkhorn la- bel refinery for imbalanced partial-label learning

Reference 38

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Observation 5e304cec-7466-47da-bcfc-f8a531d22c84 · outbound

This paper cites Rsg: A simple but effective mod- ule for learning imbalanced datasets.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Rsg: A simple but effective mod- ule for learning imbalanced datasets

Reference 39

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Observation db8dc2b0-ffb0-4223-b15f-c59746e30c0d · outbound

This paper cites Seesaw loss for long- tailed instance segmentation.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Seesaw loss for long- tailed instance segmentation

Reference 40

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Observation ed0b6077-ac5e-427b-a018-b86ffdf3e53f · outbound

This paper cites Adaptive class suppression loss for long-tail object detection.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Adaptive class suppression loss for long-tail object detection

Reference 41

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Observation ea967dec-2ac3-45fe-b15c-d3057cf158a3 · outbound

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

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Long-tailed Recognition by Routing Diverse Distribution-Aware Experts

Reference 42

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Observation 9b9f3d07-3d7f-4080-adb1-99da4324c3ac · outbound

This paper cites Margin calibration for long-tailed visual recognition.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Margin calibration for long-tailed visual recognition

Reference 43

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Observation 5bb118a9-2c16-4357-ac69-d62127d40600 · outbound

This paper cites Learn- ing to model the tail.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Learn- ing to model the tail

Reference 44

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Observation 28d3e8aa-8b41-4af9-829c-6ed19978b9f4 · outbound

This paper cites Distribution alignment: A unified framework for long-tail visual recognition.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Distribution alignment: A unified framework for long-tail visual recognition

Reference 45

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Observation 2dc63996-dd41-477f-b89b-5d35d6c517b7 · outbound

This paper cites Im- proving calibration for long-tailed recognition.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Im- proving calibration for long-tailed recognition

Reference 46

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Observation bc6c19d4-d3ab-4509-acde-6c7917ca6bcd · outbound

This paper cites Class-conditional sharpness-aware mini- mization for deep long-tailed recognition.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Class-conditional sharpness-aware mini- mization for deep long-tailed recognition

Reference 47

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Observation a0b36242-bf12-4f1e-a98d-a67cfa3df101 · outbound

This paper cites Balanced contrastive learning for long-tailed visual recognition.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning Balanced contrastive learning for long-tailed visual recognition

Reference 48

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Observation 201183fb-3efd-4645-8cd0-6b74e660ff70 · outbound

This paper cites In Stage 2, for both classifier and feature tuning cases, we train the models for 4000 iterations.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning In Stage 2, for both classifier and feature tuning cases, we train the models for 4000 iterations

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:35:06.126528Z

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Observation 3c09dcdc-841f-4ad6-af0a-119f4c4ddeb9 · outbound

This paper cites For the sake of complete- ness we provide a mathematical justification using the ap- proach of moment matching.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning For the sake of complete- ness we provide a mathematical justification using the ap- proach of moment matching

Reference 50

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Observation 2199313a-0070-4791-8973-f07844136752 · outbound

This paper cites From the figure it is clearly observed that model bias is quite different from empirical bias estimated using class frequen- cies.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning From the figure it is clearly observed that model bias is quite different from empirical bias estimated using class frequen- cies

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:35:06.101076Z

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Observation 35b20472-538a-4d23-a2bf-7b9476b49b1a · outbound

This paper cites We note from the table that proposed approach achieves highest overall accuracy while shot-wise accuracies are not affected much.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning We note from the table that proposed approach achieves highest overall accuracy while shot-wise accuracies are not affected much

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:35:06.087147Z

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Observation 06c1a375-4a90-4297-8481-84127b7228c4 · outbound

This paper cites 8 we compare model performance for test-time shifted distributions with additional baselines and a few more distribution shifts.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning 8 we compare model performance for test-time shifted distributions with additional baselines and a few more distribution shifts

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:35:06.074330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation da4b393e-2178-4dd6-92f4-654105a27feb · outbound

This paper cites The performance on iNaturalist18 for Stage 1 baseline (CE) and Stage 2 (CL and FT) are shown.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning The performance on iNaturalist18 for Stage 1 baseline (CE) and Stage 2 (CL and FT) are shown

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:35:06.060334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8257f381-0dab-469d-be15-ba685e7e903d · outbound

This paper cites The manually specified smooth- ing guidance matrix Q can be seen as a generic representa- tion for the effective prior.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning The manually specified smooth- ing guidance matrix Q can be seen as a generic representa- tion for the effective prior

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:35:06.047519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6ef7e385-535f-48a5-9062-bc38645d0485 · outbound

This paper cites The block diagram illustrates the different stages involved in the process starting from bias accumulation in traditional training to bias removal us- ing the proposed method.

Prior2Posterior: Model Prior Correction for Long-Tailed Learning The block diagram illustrates the different stages involved in the process starting from bias accumulation in traditional training to bias removal us- ing the proposed method

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:35:06.033615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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