Tuning the Right Foundation Models is What you Need for Partial Label Learning | Paper Citation Record

Observation 2c5147ef-c8d7-4864-8503-2046215f8d10 · outbound

This paper cites Disambiguation-free partial label learning.IEEE Transactions on Knowledge and Data Engineering, 29(10):2155–2167, 2017.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Disambiguation-free partial label learning.IEEE Transactions on Knowledge and Data Engineering, 29(10):2155–2167, 2017

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.269694Z

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Observation 001d9767-6b98-4f85-a7ea-35c1fb627256 · outbound

This paper cites Disambiguated attention embedding for multi-instance partial-label learning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Disambiguated attention embedding for multi-instance partial-label learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.259573Z

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Observation 7fdca1ef-4956-436f-bc3e-cb57587fde39 · outbound

This paper cites Network cooperation with progressive disambiguation for partial label learning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Network cooperation with progressive disambiguation for partial label learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.248871Z

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Observation a29dc55d-c5cf-4db3-a769-b83b34c39d16 · outbound

This paper cites Structured prediction with partial labelling through the infimum loss.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Structured prediction with partial labelling through the infimum loss

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.238812Z

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Observation ef559812-05b0-43e2-8ac3-2fcf8b738e22 · outbound

This paper cites Learning from partial labels.Journal of Machine Learning Research, 12(42):1501–1536, 2011.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Learning from partial labels.Journal of Machine Learning Research, 12(42):1501–1536, 2011

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.229746Z

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Observation 80588de6-3167-43ea-b01e-0f4d53a5ae80 · outbound

This paper cites Classification with partial labels.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Classification with partial labels

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.220380Z

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Observation 63a84c4f-2082-4493-aa07-b8e81ce7a5f1 · outbound

This paper cites Multi-instance partial-label learning with margin adjustment.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Multi-instance partial-label learning with margin adjustment

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.210858Z

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Observation 4b124359-7c6d-453c-81a8-03c114f945f4 · outbound

This paper cites Candidate pseudolabel learning: Enhancing vision-language models by prompt tuning with unlabeled data.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Candidate pseudolabel learning: Enhancing vision-language models by prompt tuning with unlabeled data

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.199643Z

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Observation d9f7252a-2d85-484c-9ea9-4085d55732c1 · outbound

This paper cites Patel, and Rama Chellappa.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Patel, and Rama Chellappa

Reference 9

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T10:32:13.678759Z

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Observation 6be750fc-9282-4000-aa43-de39bac4ee54 · outbound

This paper cites Learning from candidate labeling sets.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Learning from candidate labeling sets

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.189410Z

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Observation 05d414cf-6966-4bcc-93a8-dc73e42d3b93 · outbound

This paper cites Dietterich.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Dietterich

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.179299Z

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Observation 075121e5-28b4-4e3a-b888-b3edfa6d1a24 · outbound

This paper cites A confidence-based partial label learning model for crowd-annotated named entity recognition.

Tuning the Right Foundation Models is What you Need for Partial Label Learning A confidence-based partial label learning model for crowd-annotated named entity recognition

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.169577Z

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Observation 351c0812-cddb-42ba-859b-79702fdc4666 · outbound

This paper cites Learning from ambiguously labeled examples.Intelligent Data Analysis, 10(5):419–439, 2006.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Learning from ambiguously labeled examples.Intelligent Data Analysis, 10(5):419–439, 2006

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.160319Z

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Observation 8aed5e38-b009-40da-8388-10d439c9f972 · outbound

This paper cites Learning with multiple labels.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Learning with multiple labels

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.150965Z

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Observation 34b49c45-1795-47d3-87f4-fbb713baf702 · outbound

This paper cites Maximum margin partial label learning.Machine Learning, 106(4):573–593, 2017.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Maximum margin partial label learning.Machine Learning, 106(4):573–593, 2017

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.141050Z

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Observation f223d9ee-185f-48cc-bed8-06a47d85eeaa · outbound

This paper cites Progressive identification of true labels for partial-label learning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Progressive identification of true labels for partial-label learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.131272Z

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Observation 024a5bcb-de88-42f4-a441-fa9fee564096 · outbound

This paper cites Revisiting consistency regularization for deep partial label learning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Revisiting consistency regularization for deep partial label learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.119850Z

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Observation c7710a7a-31f7-408b-8c6f-6cbf7f65d753 · outbound

This paper cites SoLar: Sinkhorn label refinery for imbalanced partial-label learning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning SoLar: Sinkhorn label refinery for imbalanced partial-label learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.109177Z

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Observation 96b9daaf-76b3-482d-9d00-6901fa5ed4dd · outbound

This paper cites Ambiguity-induced contrastive learning for instance- dependent partial label learning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Ambiguity-induced contrastive learning for instance- dependent partial label learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.099039Z

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Observation 5fd7bab0-adb6-479e-b348-4767e7705ff0 · outbound

This paper cites Deep residual learning for image recognition.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Deep residual learning for image recognition

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.088311Z

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Observation 63c1eadd-91b9-443a-ab39-8e19cf22ad8f · outbound

This paper cites Provably consistent partial-label learning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Provably consistent partial-label learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.078336Z

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Observation 02d8b843-ce5d-4769-8efa-398a18d210b1 · outbound

This paper cites Leveraged weighted loss for partial label learning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Leveraged weighted loss for partial label learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.067829Z

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Observation eb1618cb-95b0-4fe7-868e-3b06df0c78f1 · outbound

This paper cites Exploiting class activation value for partial-label learning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Exploiting class activation value for partial-label learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.057825Z

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Observation 8ca1fee1-6ac0-46c5-8d96-ee9784a0d545 · outbound

This paper cites On the robustness of average losses for partial-label learning.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(5):2569–2583, 2023.

Tuning the Right Foundation Models is What you Need for Partial Label Learning On the robustness of average losses for partial-label learning.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(5):2569–2583, 2023

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.048081Z

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Observation b991207e-3119-4a69-96ad-b0d9b42f0a55 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Momentum contrast for unsupervised visual representation learning

Reference 25

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Observation a50fccd5-3c66-4ecf-9be7-076170e335d3 · outbound

This paper cites Pico: Contrastive label disambiguation for partial label learning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Pico: Contrastive label disambiguation for partial label learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.031939Z

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Observation c9c4ca98-3139-418b-a640-074b0da70a21 · outbound

This paper cites Fixmatch: Simplifying semi-supervised learning with consistency and confidence.Advances in Neural Information Processing Systems, 33:596–608, 2020.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Fixmatch: Simplifying semi-supervised learning with consistency and confidence.Advances in Neural Information Processing Systems, 33:596–608, 2020

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.022502Z

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Observation b5716ce1-d193-4cba-bab5-241202cd2056 · outbound

This paper cites Towards effective visual representations for partial-label learning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Towards effective visual representations for partial-label learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.012208Z

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Observation b8547a4f-e02c-423b-85be-91c6a432d801 · outbound

This paper cites Crosel: Cross selection of confident pseudo labels for partial-label learning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Crosel: Cross selection of confident pseudo labels for partial-label learning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:14.001552Z

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Observation 4b90e79f-db28-4ee7-a3e0-795a62858411 · outbound

This paper cites Towards mitigating the class-imbalance problem for partial label learning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Towards mitigating the class-imbalance problem for partial label learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.990841Z

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Observation 9ce2df9b-7277-48c3-a173-bf252f0b3e54 · outbound

This paper cites A partial label metric learning algorithm for class imbalanced data.

Tuning the Right Foundation Models is What you Need for Partial Label Learning A partial label metric learning algorithm for class imbalanced data

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.979967Z

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Observation 035e3e37-135a-4ed3-9b53-291592a77469 · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Sinkhorn distances: Lightspeed computation of optimal transport

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.970056Z

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Observation eeeb6eac-9c9e-498a-8edc-ca9f35252205 · outbound

This paper cites Long-Tailed Partial Label Learning via Dynamic Rebalancing.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Long-Tailed Partial Label Learning via Dynamic Rebalancing

Reference 33

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Observation bc4de5f0-85be-4b90-bb6a-f8d193ae3a9e · outbound

This paper cites Long-tail learning via logit adjustment.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Long-tail learning via logit adjustment

Reference 34

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Observation 65740b26-3df2-4a00-a9b6-9f77050707f3 · outbound

This paper cites Long-tailed partial label learning by head classifier and tail classifier cooperation.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Long-tailed partial label learning by head classifier and tail classifier cooperation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.959252Z

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Observation 10fd75a5-8807-45ee-86d6-790ad0d43068 · outbound

This paper cites Instance-dependent partial label learning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Instance-dependent partial label learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.949632Z

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Observation ab970c7e-53c4-4a8a-9bfa-a751e3b6129d · outbound

This paper cites Progressive purification for instance-dependent partial label learning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Progressive purification for instance-dependent partial label learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.940100Z

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Observation afc3cc4a-a88a-4570-8b58-72111a4d1476 · outbound

This paper cites Decompositional generation process for instance-dependent partial label learning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Decompositional generation process for instance-dependent partial label learning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.929840Z

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Observation 0daf2d77-f304-482a-b150-561d0e826a40 · outbound

This paper cites Distilling reliable knowledge for instance- dependent partial label learning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Distilling reliable knowledge for instance- dependent partial label learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.919993Z

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Observation 00a56986-b9d3-4d45-8733-7124a7a214fb · outbound

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

Tuning the Right Foundation Models is What you Need for Partial Label Learning Learning transferable visual models from natural language supervision

Reference 40

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Observation 56e71bdc-66cb-49ec-9bb0-99d9fd31b720 · outbound

This paper cites Learning to prompt for vision-language models.International Journal of Computer Vision, 130(9):2337–2348, 2022.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Learning to prompt for vision-language models.International Journal of Computer Vision, 130(9):2337–2348, 2022

Reference 41

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Observation 8d1f7c65-173b-42b3-84ef-2cf2ee660e14 · outbound

This paper cites Tip-adapter: Training-free adaption of clip for few-shot classification.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Tip-adapter: Training-free adaption of clip for few-shot classification

Reference 42

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Observation be8f6034-31ad-4226-bc76-e72714319c4b · outbound

This paper cites Long-tail learning with foundation model: Heavy fine-tuning hurts.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Long-tail learning with foundation model: Heavy fine-tuning hurts

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.891140Z

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Observation b613bd71-ec10-48d9-904a-6fbc48958547 · outbound

This paper cites Visual prompt tuning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Visual prompt tuning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.881038Z

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Observation e4cba089-87cc-403a-9cc7-220a200a47f8 · outbound

This paper cites Learning multiple layers of features from tiny images.Master’s thesis, University of Tront, 2009.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Learning multiple layers of features from tiny images.Master’s thesis, University of Tront, 2009

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.871310Z

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Observation 6f16c6da-fa52-4b83-872e-692a9f37ed52 · outbound

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

Tuning the Right Foundation Models is What you Need for Partial Label Learning Large-scale long-tailed recognition in an open world

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.860006Z

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Observation 2fcadad5-6d94-441c-b63c-e1719fdfa2e5 · outbound

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

Tuning the Right Foundation Models is What you Need for Partial Label Learning Learning imbalanced datasets with label-distribution-aware margin loss

Reference 47

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Observation 8be9b25a-5a7d-4194-b521-78978e2e54d8 · outbound

This paper cites Fine-grained image analysis with deep learning: A survey.IEEE transactions on pattern analysis and machine intelligence, 44(12):8927–8948, 2021.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Fine-grained image analysis with deep learning: A survey.IEEE transactions on pattern analysis and machine intelligence, 44(12):8927–8948, 2021

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T10:32:11.456629Z

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Observation be786613-3e5a-4dd4-ad93-ce999b55dbd0 · outbound

This paper cites A survey on deep learning-based fine-grained object classification and semantic segmentation.International Journal of Automation and Computing, 14(2): 119–135, 2017.

Tuning the Right Foundation Models is What you Need for Partial Label Learning A survey on deep learning-based fine-grained object classification and semantic segmentation.International Journal of Automation and Computing, 14(2): 119–135, 2017

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.837016Z

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Observation 60959633-d678-4bc6-9126-b10e887eda11 · outbound

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Tuning the Right Foundation Models is What you Need for Partial Label Learning Unresolved cited work

Reference 50

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Observation 20ae019c-c4e4-4fdb-a906-2ca0ac5d4ff4 · outbound

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

Tuning the Right Foundation Models is What you Need for Partial Label Learning 3d object representations for fine-grained categorization

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T10:32:11.867717Z

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Observation e8b3f8d3-fcaf-4df2-b80a-ec8b8d5b7780 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Fine-Grained Visual Classification of Aircraft

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T10:32:11.968073Z

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Observation e20c95bd-52df-484e-987b-5a27c02be503 · outbound

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

Tuning the Right Foundation Models is What you Need for Partial Label Learning Novel dataset for fine-grained image categorization : Stanford dogs

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.814064Z

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This paper cites Wide residual networks.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Wide residual networks

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.803564Z

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This paper cites Demystifying clip data.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Demystifying clip data

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.793108Z

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Observation ccaac6ea-d4c0-4327-b37c-b1ce6b17562f · outbound

This paper cites Sigmoid loss for language image pre-training.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Sigmoid loss for language image pre-training

Reference 56

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

Tuning the Right Foundation Models is What you Need for Partial Label Learning An image is worth 16x16 words: Transformers for image recognition at scale

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.777616Z

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Observation 088afa10-fbb9-4f9e-8380-873c280d43ad · outbound

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

Tuning the Right Foundation Models is What you Need for Partial Label Learning Imagenet: A large-scale hierarchical image database

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.767046Z

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Observation b5b94130-1d4a-42dd-b9bc-bfb3287b3dc7 · outbound

This paper cites Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T10:32:12.901992Z

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Observation 9eec1116-c91c-4fd3-9a83-6ca52dea09eb · outbound

This paper cites Parameter-efficient transfer learning for NLP.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Parameter-efficient transfer learning for NLP

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.750605Z

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Observation 69d3b352-080e-41f5-bcc3-90607589be8f · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

Tuning the Right Foundation Models is What you Need for Partial Label Learning LoRA: Low-rank adaptation of large language models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T10:32:13.106314Z

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Observation e47f0026-688b-4a04-a3e6-3681bf825ff6 · outbound

This paper cites Adapt- former: Adapting vision transformers for scalable visual recognition.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Adapt- former: Adapting vision transformers for scalable visual recognition

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.732758Z

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Observation 823f84df-0c2d-4ded-ae59-beba68da64a6 · outbound

This paper cites Mixed Precision Training.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Mixed Precision Training

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T10:32:13.236236Z

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Observation 40d0d1f9-5a26-4f9e-827e-d70540cbbfd3 · outbound

This paper cites Randaugment: Practical automated data augmentation with a reduced search space.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Randaugment: Practical automated data augmentation with a reduced search space

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.721946Z

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Observation ef14413e-3989-43f9-b391-a6002aec3ef5 · outbound

This paper cites mixup: Beyond empirical risk minimization.

Tuning the Right Foundation Models is What you Need for Partial Label Learning mixup: Beyond empirical risk minimization

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T10:32:13.363613Z

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Observation c5f38372-386f-4f16-985c-831d539a9459 · outbound

This paper cites Cutmix: Regularization strategy to train strong classifiers with localizable features.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Cutmix: Regularization strategy to train strong classifiers with localizable features

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T10:32:13.477374Z

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Observation 3b565ad2-af8e-41e5-8a37-dc18dff90b20 · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning.

Tuning the Right Foundation Models is What you Need for Partial Label Learning Reproducible scaling laws for contrastive language-image learning

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T10:32:13.516072Z

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Observation dad78b80-1a57-4ff1-8555-59fbbcedfd22 · outbound

This paper cites collapse.

Tuning the Right Foundation Models is What you Need for Partial Label Learning collapse

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:13.691830Z

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