Paper Citation Record · LEDGER

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks

As of 22 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 1 inbound Pith citation observation for arXiv:2412.20682.

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2412.20682 v1

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Outbound references

Observation c6080f1f-968c-4258-abc4-92ad33c29ca7 · outbound

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

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Learning transferable visual models from natural language supervision,

Reference 1

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Observation 24a9c0f8-f2fe-4376-a37e-59c0a3822ab3 · outbound

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

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Scaling up visual and vision-language representation learning with noisy text supervision,

Reference 2

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Observation 1496bf16-a508-4a7d-9e0c-b9a0d8b127c3 · outbound

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

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Sigmoid loss for language image pre-training,

Reference 3

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This paper cites Sgva-clip: Semantic- guided visual adapting of vision-language models for few-shot image classification,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Sgva-clip: Semantic- guided visual adapting of vision-language models for few-shot image classification,

Reference 4

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This paper cites Clip-vg: Self-paced curriculum adapting of clip for visual grounding,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Clip-vg: Self-paced curriculum adapting of clip for visual grounding,

Reference 5

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Observation 4f0ff8ac-de93-4d10-bae8-4f677414e50c · outbound

This paper cites Effective end-to-end vision language pre- training with semantic visual loss,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Effective end-to-end vision language pre- training with semantic visual loss,

Reference 6

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This paper cites Neural logic vision language explainer,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Neural logic vision language explainer,

Reference 7

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Observation 45f4ac16-7638-4079-96fe-a911d77bd1f4 · outbound

This paper cites Lovm: Language- only vision model selection,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Lovm: Language- only vision model selection,

Reference 8

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Observation ed98bd14-cf8f-4e55-a8c6-7b2f33da4b48 · outbound

This paper cites Bridge the Modality and Capability Gaps in Vision-Language Model Selection.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Bridge the Modality and Capability Gaps in Vision-Language Model Selection

Reference 9

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This paper cites Imagenet large scale visual recognition challenge,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Imagenet large scale visual recognition challenge,

Reference 10

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Observation 59c80df0-e92e-4a65-bf70-fb67a9d8e0d8 · outbound

This paper cites GPT-4 Technical Report.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks GPT-4 Technical Report

Reference 11

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This paper cites Leveraging unlabeled data to predict out-of-distribution performance,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Leveraging unlabeled data to predict out-of-distribution performance,

Reference 12

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This paper cites Are labels always necessary for classifier accuracy evaluation?.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Are labels always necessary for classifier accuracy evaluation?

Reference 13

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This paper cites Predicting out-of- distribution error with the projection norm,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Predicting out-of- distribution error with the projection norm,

Reference 14

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Observation c3384c91-811e-458a-8cee-ce8ae80128a6 · outbound

This paper cites Data determines distributional robustness in contrastive language image pre-training (clip),.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Data determines distributional robustness in contrastive language image pre-training (clip),

Reference 15

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This paper cites Does clip’s generalization performance mainly stem from high train- test similarity?.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Does clip’s generalization performance mainly stem from high train- test similarity?

Reference 16

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This paper cites A Survey on Evaluation of Out-of-Distribution Generalization.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks A Survey on Evaluation of Out-of-Distribution Generalization

Reference 17

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This paper cites Which Model to Transfer? A Survey on Transferability Estimation.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Which Model to Transfer? A Survey on Transferability Estimation

Reference 18

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This paper cites Rankme: Assessing the downstream performance of pretrained self-supervised representations by their rank,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Rankme: Assessing the downstream performance of pretrained self-supervised representations by their rank,

Reference 19

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This paper cites Identifying useful learnwares for heterogeneous label spaces,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Identifying useful learnwares for heterogeneous label spaces,

Reference 20

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This paper cites Etran: Energy-based transferability estimation,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Etran: Energy-based transferability estimation,

Reference 21

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This paper cites Predicting out-of-distribution error with confidence optimal transport,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Predicting out-of-distribution error with confidence optimal transport,

Reference 22

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This paper cites Data analysis and regression. a second course in statistics,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Data analysis and regression. a second course in statistics,

Reference 23

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This paper cites Tune it the right way: Unsupervised validation of domain adaptation via soft neighborhood density,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Tune it the right way: Unsupervised validation of domain adaptation via soft neighborhood density,

Reference 24

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This paper cites Covariate shift adap- tation by importance weighted cross validation.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Covariate shift adap- tation by importance weighted cross validation

Reference 25

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Towards accurate model selection in deep unsupervised domain adaptation,

Reference 26

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Stochastic gradient methods for dis- tributionally robust optimization with f-divergences,

Reference 27

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This paper cites Invariant Risk Minimization.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Invariant Risk Minimization

Reference 28

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Stable learning via sample reweighting,

Reference 29

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks A baseline for detecting misclassified and out-of-distribution examples in neural networks,

Reference 30

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks What does rotation prediction tell us about classifier accuracy under varying testing environments?

Reference 31

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Agreement-on-the- line: Predicting the performance of neural networks under distribution shift,

Reference 32

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,

Reference 33

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks I. mathematical contributions to the theory of evolu- tion.—vii. on the correlation of characters not quantitatively measur- able,

Reference 34

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Learning multiple layers of features from tiny images,

Reference 35

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Cats and dogs,

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Automated flower classification over a large number of classes,

Reference 37

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Reading digits in natural images with unsupervised feature learning,

Reference 38

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This paper cites Detection of traffic signs in real-world images: The german traffic sign detection benchmark,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Detection of traffic signs in real-world images: The german traffic sign detection benchmark,

Reference 39

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Describing textures in the wild,

Reference 40

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Yfcc100m: The new data in multimedia research,

Reference 41

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Sun database: Large-scale scene recognition from abbey to zoo,

Reference 42

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Gradient-based learning applied to document recognition,

Reference 43

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Challenges in representation learning: Facial expression recognition challenge,

Reference 44

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks On the importance of feature separability in predicting out-of-distribution error,

Reference 45

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Unsupervised representation learning by predicting image rotations,

Reference 46

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks The use of multiple measurements in taxonomic prob- lems,

Reference 47

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,

Reference 48

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Deep residual learning for image recognition,

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 50

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Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks A convnet for the 2020s,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:19:02.382172Z

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Observation 675996f6-57fd-4a99-ae23-b26f0b48b90a · outbound

This paper cites Laion-400m: Open dataset of clip-filtered 400 million image-text pairs,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Laion-400m: Open dataset of clip-filtered 400 million image-text pairs,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:19:02.372675Z

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Observation 82cc4b91-9e7e-4ad3-8f3c-c26e6c5d8102 · outbound

This paper cites AltCLIP: Altering the language encoder in CLIP for extended language capabilities,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks AltCLIP: Altering the language encoder in CLIP for extended language capabilities,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:19:02.362356Z

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Observation d7237b80-b802-4761-859c-448fcacc02af · outbound

This paper cites Groupvit: Semantic segmentation emerges from text supervision,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Groupvit: Semantic segmentation emerges from text supervision,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:19:02.350740Z

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Observation a3b48329-45f1-4ab2-856a-bec9f04e4f0e · outbound

This paper cites Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T23:19:02.235754Z

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Observation b63e8fcc-ea48-4330-8d0f-23b2ae3a5fef · outbound

This paper cites Demystifying CLIP Data.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Demystifying CLIP Data

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-10T23:19:02.239160Z

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Observation 8829e10d-c0a3-4c87-a026-2ede33fedede · outbound

This paper cites BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-10T23:19:02.242350Z

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Observation 60cb4d52-1c1d-4b93-8581-ff0c846425f1 · outbound

This paper cites Quilt-1M: One Million Image-Text Pairs for Histopathology.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Quilt-1M: One Million Image-Text Pairs for Histopathology

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-10T23:19:02.245892Z

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This paper cites BioCLIP: A vision foundation model for the tree of life,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks BioCLIP: A vision foundation model for the tree of life,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:19:02.341422Z

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This paper cites Gpt-4: Generative pre-trained transformer,.

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks Gpt-4: Generative pre-trained transformer,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:19:02.331857Z

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Pith citing papers

Observation 12f4859a-834e-4817-a2cf-bfb3385c5b4e · inbound

HugSelect: An Explainable Multi-Criteria Decision-Support Framework for foundation-model selection cites this paper.

HugSelect: An Explainable Multi-Criteria Decision-Support Framework for foundation-model selection Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-08-12T00:32:21.110763Z

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