Paper Citation Record · LEDGER

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques

As of 17 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2501.13756.

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

pith.paper-citation-record.v1
2501.13756 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:41:36.902886Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

44 of 44 outbound references displayed

verified exact0
verified fuzzy34
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dd649b3d-1c11-469e-af1b-899744931a91 · outbound

This paper cites Balanced product of calibrated ex- perts for long-tailed recognition.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Balanced product of calibrated ex- perts for long-tailed recognition

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.576233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a31e5342-4fd1-406e-ac94-b5590913ca1c · outbound

This paper cites Long-tailed recognition via weight balancing.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Long-tailed recognition via weight balancing

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.560838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0417afba-43a3-4833-b7a6-caf62b4185b2 · outbound

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

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Learning imbalanced datasets with label- distribution-aware margin loss

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.546687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4c62d486-9690-473a-8072-648ee608a116 · outbound

This paper cites Parametric contrastive learning.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Parametric contrastive learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.530611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f1dba0d5-d765-4909-b6f3-cb49f6aea89a · outbound

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

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Reslt: Residual learning for long-tailed recogni- tion

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.514171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 63d3ab52-b17c-4e90-96be-187e05b07018 · outbound

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

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Class-balanced loss based on effective number of samples

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:36.728714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a778624-d983-46ea-85e9-5f20a34108df · outbound

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

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Imagenet: A large-scale hierarchical image database

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:36.734236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67f16371-4c35-4c77-9f6d-6b7f7a680d12 · outbound

This paper cites No one left behind: Improving the worst categories in long-tailed learning.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques No one left behind: Improving the worst categories in long-tailed learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.479197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d3f16303-a0d9-4399-a01c-e0984014037c · outbound

This paper cites Genetic algorithm-based hyperparameter optimiza- tion of deep learning models for pm2.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Genetic algorithm-based hyperparameter optimiza- tion of deep learning models for pm2

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.463256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5680e5ef-b1c1-4e8e-aa3b-a343c1150383 · outbound

This paper cites Deep residual learning for image recognition.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Deep residual learning for image recognition

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.446835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0011ba19-5432-4e0c-bc4c-86dea4712e74 · outbound

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

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Disentangling label dis- tribution for long-tailed visual recognition

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.431864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 65377f93-1f41-4e37-9c88-3ef81c121758 · outbound

This paper cites Learning deep representation for imbalanced classifi- cation.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Learning deep representation for imbalanced classifi- cation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.416092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e1ede674-c9bd-4a4e-aad1-9992da54e367 · outbound

This paper cites Deep imbalanced learning for face recognition and attribute prediction.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Deep imbalanced learning for face recognition and attribute prediction

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.400776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 84ffd1bc-f4da-43af-bf5e-310e68a48a29 · outbound

This paper cites Long-tailed visual recognition via self-heterogeneous integration with knowledge excavation.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Long-tailed visual recognition via self-heterogeneous integration with knowledge excavation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.385315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c17017d9-d898-412e-ac14-e12325cd43f2 · outbound

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

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Decoupling Representation and Classifier for Long-Tailed Recognition

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:36.772774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e658732-1af9-4034-86fd-15633245e0be · outbound

This paper cites Supervised contrastive learning.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Supervised contrastive learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:36.776706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e611cfa-a766-4059-bf22-64b8c66a40ce · outbound

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

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Learning multiple layers of features from tiny images

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:36.781184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d84d725-2751-4302-9d34-3293f4023dd6 · outbound

This paper cites Adaptive hierarchical representation learn- ing for long-tailed object detection.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Adaptive hierarchical representation learn- ing for long-tailed object detection

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.349744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 38f0d12e-d335-4256-9dcb-e4583c4b2bb5 · outbound

This paper cites Trustworthy long-tailed classification.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Trustworthy long-tailed classification

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.333856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7e500876-6a10-4f2c-b7c4-a1389354587a · outbound

This paper cites Nested collaborative learning for long-tailed visual recognition.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Nested collaborative learning for long-tailed visual recognition

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:36.795167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6e77b7e-c8fe-4d66-b11b-a9e0439f9659 · outbound

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

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Long-tailed visual recognition via gaussian clouded logit adjustment

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.308427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c5fada20-d86a-48d9-8e01-ba32312ef55c · outbound

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

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Metasaug: Meta semantic augmentation for long-tailed visual recognition

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.293071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 014b8b39-c25d-40c6-97d7-400c5954a4bc · outbound

This paper cites Targeted su- pervised contrastive learning for long-tailed recognition.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Targeted su- pervised contrastive learning for long-tailed recognition

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.276333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 92f1e5d3-233c-444b-ac16-54779ba723d7 · outbound

This paper cites Focal loss for dense object detection.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Focal loss for dense object detection

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.259492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3e517bee-7c8c-4158-80f1-e7ccf61cfbdd · outbound

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

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Large-scale long-tailed recognition in an open world

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.245271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 475eee1c-50e1-4a07-b936-ecba86ef060a · outbound

This paper cites Open long-tailed recognition in a dynamic world.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Open long-tailed recognition in a dynamic world

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.231036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation eb36b840-1ed6-436f-b19e-48fb0abae7b2 · outbound

This paper cites Long-tail learning via logit adjustment.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Long-tail learning via logit adjustment

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:36.826956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6189973-34e6-44c8-9073-ba4c889de7ae · outbound

This paper cites Hyperparameter optimization for convolutional neural networks with genetic algorithms and bayesian optimization.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Hyperparameter optimization for convolutional neural networks with genetic algorithms and bayesian optimization

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.216180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 79be6982-0d42-4467-a60d-29d003029cb8 · outbound

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

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Distributional robustness loss for long-tail learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.201528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 26ef4069-dd11-4289-823b-b01bc7d41d3a · outbound

This paper cites Multi-task learning as multi-objective optimization.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Multi-task learning as multi-objective optimization

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:36.840898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1c50550-871a-4588-9bdf-ce799a0b0b20 · outbound

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

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Long- tailed classification by keeping the good and removing the bad momentum causal effect

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.175149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6b8de70e-fe06-4254-99e5-fa7eb69608d1 · outbound

This paper cites Matching networks for one shot learning.Ad- vances in neural information processing systems , 29, 2016.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Matching networks for one shot learning.Ad- vances in neural information processing systems , 29, 2016

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.157828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9a92c866-8e91-405d-83bf-c9001446bad6 · outbound

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

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Rsg: A simple but effective mod- ule for learning imbalanced datasets

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.142070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation de98da58-d4c2-4a92-96f2-79d71352a852 · outbound

This paper cites Contrastive learning based hybrid networks for long- tailed image classification.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Contrastive learning based hybrid networks for long- tailed image classification

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.125819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3770f748-234c-459c-9a08-f2361164775f · outbound

This paper cites C2am loss: Chas- ing a better decision boundary for long-tail object detection.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques C2am loss: Chas- ing a better decision boundary for long-tail object detection

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.109982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 431b4bf7-71e7-46eb-a836-2597edfd179c · outbound

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

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Long-tailed Recognition by Routing Diverse Distribution-Aware Experts

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:36.867122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb9e0fda-06be-4418-979e-c973ac4b706a · outbound

This paper cites Balancing logit variation for long-tailed semantic segmentation.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Balancing logit variation for long-tailed semantic segmentation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.094410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 67717f78-1320-4fed-b822-7ebdf3ef9753 · outbound

This paper cites Aggregated residual transformations for deep neural networks.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Aggregated residual transformations for deep neural networks

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T15:41:36.876060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 284a8ad8-ce46-435a-8f89-f01093c3e556 · outbound

This paper cites Decoupled con- trastive learning.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Decoupled con- trastive learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.069000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 32d39186-cfaa-4b61-985b-c63bd1293f3d · outbound

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

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Distribution alignment: A unified frame- work for long-tail visual recognition

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.053530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d14d992c-5403-4398-ad31-a3eede9e26b8 · outbound

This paper cites Test-agnostic long-tailed recognition by test-time aggregat- ing diverse experts with self-supervision.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Test-agnostic long-tailed recognition by test-time aggregat- ing diverse experts with self-supervision

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.038353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a7fc0ae3-aa04-44fa-a1b0-ceabb370f288 · outbound

This paper cites Deep long-tailed learning: A survey.

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Deep long-tailed learning: A survey

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.021942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2ca6a98a-e6c3-4623-ae8f-596e3a0da2da · outbound

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

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Bbn: Bilateral-branch network with cumulative learn- ing for long-tailed visual recognition

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:37.005965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ad2327b6-bdfa-42c1-b546-c586b165b33f · outbound

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

Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques Balanced contrastive learn- ing for long-tailed visual recognition

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:41:36.989614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.