Paper Citation Record · LEDGER

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification

As of 8 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2505.16149.

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

pith.paper-citation-record.v1
2505.16149 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:10:13.252061Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T20:58:59.346867Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-13T21:03:20.317945Z

Reference resolution

57 of 57 outbound references displayed

verified exact2
verified fuzzy26
unresolved29
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 48a3e18a-4e49-49f5-ae0b-0819e9da3985 · outbound

This paper cites Flamingo: a Visual Language Model for Few-Shot Learning.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Flamingo: a Visual Language Model for Few-Shot Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:06.972843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f064ddd-9342-4f7f-b4b6-bf9ce1bd984d · outbound

This paper cites Robust bi-tempered logistic loss based on Bregman divergences.Advances in Neural Information Processing Systems, 32, 2019.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Robust bi-tempered logistic loss based on Bregman divergences.Advances in Neural Information Processing Systems, 32, 2019

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.344936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6fcd7051-fdc4-4f55-ad25-778f795e80ff · outbound

This paper cites OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:07.127399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a525d9e-dee7-4dad-be7e-9c06f4445167 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:07.196272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 961a0667-4b37-4de6-a241-c14631fe194c · outbound

This paper cites Multi-label classification with partial annotations using class-aware selective loss.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Multi-label classification with partial annotations using class-aware selective loss

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:07.274254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e71ad4c-169a-4845-b791-042fda023895 · outbound

This paper cites Revolt: Collaborative crowdsourcing for labeling machine learning datasets.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Revolt: Collaborative crowdsourcing for labeling machine learning datasets

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.312410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9f06821e-0f4a-45b7-8cfb-3e51a74f7c49 · outbound

This paper cites Understanding and utilizing deep neural networks trained with noisy labels.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Understanding and utilizing deep neural networks trained with noisy labels

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.213540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6f375435-c8b1-4f53-a707-99b73eaf87fd · outbound

This paper cites Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:07.532981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ab8e373-0174-47fa-b025-f6bb3748e6ae · outbound

This paper cites Learning with instance-dependent label noise: A sample sieve approach.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Learning with instance-dependent label noise: A sample sieve approach

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:20.050759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cd5e300b-90c8-4a31-a339-514a3398aba3 · outbound

This paper cites InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:07.708958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdb43341-5072-45fb-a67f-b44b3686d445 · outbound

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

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Imagenet: A large- scale hierarchical image database

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:07.781082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bd93e03-d636-408f-b0ce-c2fd53e0501e · outbound

This paper cites Learning a deep convnet for multi-label classification with partial labels.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Learning a deep convnet for multi-label classification with partial labels

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:07.867329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba6c2ee2-d814-4d58-9616-e7bb6938a4f2 · outbound

This paper cites Training deep neural-networks using a noise adapta- tion layer.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Training deep neural-networks using a noise adapta- tion layer

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.845977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 99aaa568-009d-4fb6-aca0-38910f54b467 · outbound

This paper cites Caltech-256 object category dataset.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Caltech-256 object category dataset

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.795432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6f8f14ad-e6c5-4016-91cc-ca137689b0e3 · outbound

This paper cites Using trusted data to train deep networks on labels corrupted by severe noise.Advances in neural information processing systems, 31, 2018.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Using trusted data to train deep networks on labels corrupted by severe noise.Advances in neural information processing systems, 31, 2018

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:08.176558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e14c9111-f0ad-46c4-84b5-050e73c2279a · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Scaling up visual and vision-language representation learning with noisy text supervision

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.773148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5ee5d210-d361-499a-a95e-6714754ed563 · outbound

This paper cites Detecting and preventing confused labels in crowdsourced data.Proceedings of the VLDB Endowment, 13(12):2522–2535, 2020.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Detecting and preventing confused labels in crowdsourced data.Proceedings of the VLDB Endowment, 13(12):2522–2535, 2020

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.761199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9e581a3b-be4e-4344-bd85-82ad5b0d0ef6 · outbound

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

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Learning multiple layers of features from tiny images

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:08.510498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9228a774-a58a-4557-8d3b-bfe3d2b72162 · outbound

This paper cites Constrained Instance and Class Reweighting for Robust Learning under Label Noise.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Constrained Instance and Class Reweighting for Robust Learning under Label Noise

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:08.626706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3845250-8d70-4512-8ccb-b3b0dbfb8561 · outbound

This paper cites Gradient-based learning applied to document recognition.Proceedings of the IEEE, 86(11):2278–2324, 1998.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Gradient-based learning applied to document recognition.Proceedings of the IEEE, 86(11):2278–2324, 1998

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:08.731369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93a1c842-87b5-4a4c-aa02-4cb2260404a4 · outbound

This paper cites Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:08.846380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9564e4d9-de7d-4b9d-893d-ebfb7204a736 · outbound

This paper cites BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:08.955081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a8675f7-9c6e-4fb4-840a-faf73d8de1db · outbound

This paper cites Align before fuse: Vision and language representation learning with momentum distillation.Advances in neural information processing systems, 34:9694–9705, 2021.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Align before fuse: Vision and language representation learning with momentum distillation.Advances in neural information processing systems, 34:9694–9705, 2021

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:09.021317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f7f9a45-9155-4a8e-93dd-629543266a3c · outbound

This paper cites Visual Instruction Tuning.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Visual Instruction Tuning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:09.121439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9000fca-389d-4516-b2db-3f3a74aecfce · outbound

This paper cites Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:09.230185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e51ac529-6a5a-4a5d-9962-6b2fe153ac0e · outbound

This paper cites Human and ai perceptual differences in image classification errors.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Human and ai perceptual differences in image classification errors

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.716942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bf7f27be-965f-407e-b40d-562537f78e6c · outbound

This paper cites Classification with noisy labels by importance reweighting.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Classification with noisy labels by importance reweighting

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.703561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 34ab41c4-67d5-4b72-b9d2-761b4500d150 · outbound

This paper cites Peer loss functions: Learning from noisy labels without knowing noise rates.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Peer loss functions: Learning from noisy labels without knowing noise rates

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.689195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3adf3b09-f40f-4588-a053-07031cde3b63 · outbound

This paper cites Normalized loss functions for deep learning with noisy labels.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Normalized loss functions for deep learning with noisy labels

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.676484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8a11cefe-315e-4b77-9858-c95b198e69e1 · outbound

This paper cites Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing Labels.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing Labels

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:10:13.840314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 89187f11-ee59-42c1-bf94-e4bae83775b7 · outbound

This paper cites Learning with noisy labels.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Learning with noisy labels

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.556065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 28cbb655-d390-46f7-a484-66fea9370ff0 · outbound

This paper cites Confident learning: Estimating uncertainty in dataset labels.Journal of Artificial Intelligence Research, 70:1373–1411, 2021.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Confident learning: Estimating uncertainty in dataset labels.Journal of Artificial Intelligence Research, 70:1373–1411, 2021

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:09.989225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ddd2e76-91d3-43a4-9bc2-e9d4c495ebfa · outbound

This paper cites Northcutt, Anish Athalye, and Jonas Mueller.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Northcutt, Anish Athalye, and Jonas Mueller

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:19.076565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 44cd219f-29c8-47d5-bc64-9c0ee8395bae · outbound

This paper cites Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:10.178439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05353be6-f5c1-4cb9-be3e-5a211a843ae6 · outbound

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

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Learning transferable visual models from natural language supervision

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:10.271754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c2b9fe4-e065-4de5-a576-e95b1239c191 · outbound

This paper cites Learning from noisy labels by regularized estimation of annotator confusion.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Learning from noisy labels by regularized estimation of annotator confusion

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:10.397262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2d29e77-6dc6-4371-b4bc-40aecf0a1dc8 · outbound

This paper cites Policy learning using weak supervision.Advances in Neural Information Processing Systems, 34, 2021.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Policy learning using weak supervision.Advances in Neural Information Processing Systems, 34, 2021

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:18.943972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 88eda010-595e-485a-924a-af2f4f185ceb · outbound

This paper cites Symmetric cross entropy for robust learning with noisy labels.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Symmetric cross entropy for robust learning with noisy labels

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:18.825424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3e095e30-a355-46a7-9e01-f07b199fdda6 · outbound

This paper cites Combating noisy labels by agreement: A joint training method with co-regularization.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Combating noisy labels by agreement: A joint training method with co-regularization

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:18.596349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d7fdcfb5-84f6-41d5-a145-24212c7ecde7 · outbound

This paper cites Open-set label noise can improve robustness against inherent label noise.Advances in Neural Information Processing Systems, 34, 2021.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Open-set label noise can improve robustness against inherent label noise.Advances in Neural Information Processing Systems, 34, 2021

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:18.373746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d8c35e1d-5537-4855-a38f-fb810b12c63f · outbound

This paper cites To smooth or not? when label smoothing meets noisy labels.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification To smooth or not? when label smoothing meets noisy labels

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:11.097295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7369f625-704c-4374-9e58-c03f7e205b67 · outbound

This paper cites When Optimizing $f$-divergence is Robust with Label Noise.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification When Optimizing $f$-divergence is Robust with Label Noise

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:11.195032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dc4ecb3-c002-4e29-82b8-8af3d825ac32 · outbound

This paper cites Distributionally robust post-hoc classifiers under prior shifts.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Distributionally robust post-hoc classifiers under prior shifts

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:17.266126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fff03e6f-efb9-46e4-9917-c63d21c172e5 · outbound

This paper cites Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:11.464051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d653ab22-961d-4229-9e83-08337bb27bba · outbound

This paper cites To aggregate or not? learning with separate noisy labels.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification To aggregate or not? learning with separate noisy labels

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:15.383805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f8e74cf1-6b44-4751-9924-3ac1349b9188 · outbound

This paper cites Fairness Improves Learning from Noisily Labeled Long-Tailed Data.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Fairness Improves Learning from Noisily Labeled Long-Tailed Data

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:10:13.575149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c697762b-48db-4af5-9901-42f62a742f56 · outbound

This paper cites Vision- language models are strong noisy label detectors.Advances in Neural Information Processing Systems, 37:58154–58173, 2024.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Vision- language models are strong noisy label detectors.Advances in Neural Information Processing Systems, 37:58154–58173, 2024

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:15.219455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d8f9f345-fe27-45d3-a83d-0439145e011d · outbound

This paper cites iclip: Bridging image classification and contrastive language-image pre-training for visual recognition.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification iclip: Bridging image classification and contrastive language-image pre-training for visual recognition

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:14.965188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b2176df7-c402-4bce-838f-a6d5b1dccc79 · outbound

This paper cites Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:12.279954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfd973bd-80f8-46f0-929f-3f0b1bf6c901 · outbound

This paper cites Are anchor points really indispensable in label-noise learning?Advances in Neural Information Processing Systems, 32, 2019.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Are anchor points really indispensable in label-noise learning?Advances in Neural Information Processing Systems, 32, 2019

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:14.755349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6335e2a4-9f40-4714-829a-2ea7718e1101 · outbound

This paper cites FILIP: Fine-grained Interactive Language-Image Pre-Training.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification FILIP: Fine-grained Interactive Language-Image Pre-Training

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:12.558456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b523da12-5eab-408b-a5d4-b53a5275c552 · outbound

This paper cites How does disagreement help generalization against label corruption? InInternational Conference on Machine Learning, pages 7164–7173.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification How does disagreement help generalization against label corruption? InInternational Conference on Machine Learning, pages 7164–7173

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:14.523290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 60d5cab3-2639-4515-9a3d-aef77bfe0040 · outbound

This paper cites Lit: Zero-shot transfer with locked-image text tuning.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Lit: Zero-shot transfer with locked-image text tuning

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:12.697369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01dd9641-0e53-4b4a-ac50-2471b1ef8c2f · outbound

This paper cites Learning in imperfect environment: Multi-label classification with long-tailed distribution and partial labels.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Learning in imperfect environment: Multi-label classification with long-tailed distribution and partial labels

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:14.361480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 70761e1b-5107-4bcc-b438-6409042976df · outbound

This paper cites Conditional prompt learning for vision-language models.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Conditional prompt learning for vision-language models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:12.961360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f68fd159-20da-4ed9-9f3c-3b13c6306094 · outbound

This paper cites Clusterability as an alternative to anchor points when learning with noisy labels.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Clusterability as an alternative to anchor points when learning with noisy labels

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:10:14.149868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 72d5a0e0-35b0-49dd-8837-17c29dd6b31c · outbound

This paper cites Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:13.252061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 25fa9c8e-96e5-49bd-84e8-18e3e5f29531 · inbound

CAMEO: A Conditional and Quality-Aware Multi-Agent Image Editing Orchestrator cites this paper.

CAMEO: A Conditional and Quality-Aware Multi-Agent Image Editing Orchestrator When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-13T21:03:20.320358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.