Paper Citation Record · LEDGER

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models

As of 14 August 2026, this Paper Citation Record lists 98 of 98 outbound references and 4 inbound Pith citation observations for arXiv:2506.02557.

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

pith.paper-citation-record.v1
2506.02557 v1

Coverage vector

measured 98 of 98 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:26:14.795852Z

measured 102 of 102 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T22:07:35.021986Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T22:09:07.132811Z

Reference resolution

98 of 98 outbound references displayed

verified exact4
verified fuzzy35
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4d16e6ee-d894-4bda-8dcc-7acfeb66ae2e · outbound

This paper cites Tallyqa: Answering complex counting questions.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Tallyqa: Answering complex counting questions

Reference 1

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Observation 9ac6405e-c393-4fe7-be5e-44e9302a72b6 · outbound

This paper cites Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 2

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Observation 5b8e08a7-39c5-472a-bf0b-f4305a43ef72 · outbound

This paper cites Multi-label cluster discrimination for visual representation learning.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Multi-label cluster discrimination for visual representation learning

Reference 3

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Observation dd2aea2c-c9b9-4bd1-894b-5ac1d6191e03 · outbound

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

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models

Reference 4

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Observation ae69c505-1c92-4049-8542-abc0a16de6f6 · outbound

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

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 5

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Observation 93b3c931-1656-477b-91ee-e9678554b1ea · outbound

This paper cites BE it: BERT pre-training of image transformers.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models BE it: BERT pre-training of image transformers

Reference 6

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Observation 15631b84-da0c-4158-890f-8af4e5d2f0cf · outbound

This paper cites Demystifying MMD GANs.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Demystifying MMD GANs

Reference 7

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Observation 687fedb5-d726-43a1-a2cd-2cb12ad73329 · outbound

This paper cites Domain prompt learning with quaternion networks.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Domain prompt learning with quaternion networks

Reference 8

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Observation 18af799d-c07f-4a26-8766-b41a6ea96b33 · outbound

This paper cites Emerging properties in self-supervised vision transformers.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Emerging properties in self-supervised vision transformers

Reference 9

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Observation 4ae0c78d-9be3-4ee3-84a6-54fc288609ed · outbound

This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 10

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Observation 5bc761d1-b06c-4caa-a25b-ce9284b1b416 · outbound

This paper cites Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks

Reference 11

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Observation 9aa90695-3be6-400e-943d-09083641276e · outbound

This paper cites Remote sensing image scene classification: Benchmark and state of the art.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Remote sensing image scene classification: Benchmark and state of the art

Reference 12

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Observation 2bc4a2da-efac-488c-a0ec-b5892fa02b4a · outbound

This paper cites Describing textures in the wild.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Describing textures in the wild

Reference 13

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Observation 01f684b7-2458-4e0c-844a-5c245287180d · outbound

This paper cites Locality alignment improves vision-language models.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Locality alignment improves vision-language models

Reference 14

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Observation 9ea5f798-9654-40d3-b615-2e300fe98c8e · outbound

This paper cites Vision transformers need registers.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Vision transformers need registers

Reference 15

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Observation 76f1de1f-3955-4271-956b-0e71f1f8478a · outbound

This paper cites FairerCLIP: Debiasing CLIP's Zero-Shot Predictions using Functions in RKHSs.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models FairerCLIP: Debiasing CLIP's Zero-Shot Predictions using Functions in RKHSs

Reference 16

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Observation 29001f20-7d10-4a44-aa2e-3a212bc2928b · outbound

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

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Imagenet: A large-scale hierarchical image database

Reference 17

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Observation 0f19e8af-fdf4-4222-b513-e6808e566af5 · outbound

This paper cites Data Filtering Networks.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Data Filtering Networks

Reference 18

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Observation e1768fd8-cc1c-4a4b-8fb5-cac422a01f1c · outbound

This paper cites Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories

Reference 19

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Observation f27885c4-874f-4f66-80cd-ca21c6c67bfc · outbound

This paper cites The Vendi Score: A Diversity Evaluation Metric for Machine Learning.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models The Vendi Score: A Diversity Evaluation Metric for Machine Learning

Reference 20

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This paper cites Y., Ilharco, G., Fang, A., Hayase, J., Smyrnis, G., Nguyen, T., Marten, R., Wortsman, M., Ghosh, D., Zhang, J., et al.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Y., Ilharco, G., Fang, A., Hayase, J., Smyrnis, G., Nguyen, T., Marten, R., Wortsman, M., Ghosh, D., Zhang, J., et al

Reference 21

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This paper cites Clip-adapter: Better vision-language models with feature adapters.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Clip-adapter: Better vision-language models with feature adapters

Reference 22

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Observation 01bb1b83-88d7-447e-a6f6-e6d5055328d6 · outbound

This paper cites 3dsam-adapter: Holistic adaptation of sam from 2d to 3d for promptable tumor segmentation.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models 3dsam-adapter: Holistic adaptation of sam from 2d to 3d for promptable tumor segmentation

Reference 23

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Boosting the visual interpretability of clip via adversarial fine-tuning

Reference 24

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This paper cites J., Erhan, D., Carrier, P.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models J., Erhan, D., Carrier, P

Reference 25

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This paper cites Making the v in vqa matter: Elevating the role of image understanding in visual question answering.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Making the v in vqa matter: Elevating the role of image understanding in visual question answering

Reference 26

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This paper cites Recovering low-rank matrices from few coefficients in any basis.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Recovering low-rank matrices from few coefficients in any basis

Reference 27

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Llava-uhd: an lmm perceiving any aspect ratio and high-resolution images

Reference 28

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models J., Guo, A., Lin, C., Grauman, K., Luo, J., and Bigham, J

Reference 29

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models and Ozay, M

Reference 30

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This paper cites Masked autoencoders are scalable vision learners.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Masked autoencoders are scalable vision learners

Reference 31

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This paper cites Introducing eurosat: A novel dataset and deep learning benchmark for land use and land cover classification.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Introducing eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 32

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Natural adversarial examples

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Probability inequalities for sums of bounded random variables

Reference 34

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Unresolved cited work

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models J., yelong shen, Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models T., and Farnia, F

Reference 37

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models T., and Farnia, F

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Clevr: A diagnostic dataset for compositional language and elementary visual reasoning

Reference 40

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This paper cites What's ''up'' with vision-language models? investigating their struggle with spatial reasoning.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models What's ''up'' with vision-language models? investigating their struggle with spatial reasoning

Reference 41

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This paper cites Studiogan: A taxonomy and benchmark of gans for image synthesis.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Studiogan: A taxonomy and benchmark of gans for image synthesis

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Referitgame: Referring to objects in photographs of natural scenes

Reference 43

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models A diagram is worth a dozen images

Reference 44

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models The hateful memes challenge: Detecting hate speech in multimodal memes

Reference 45

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Unresolved cited work

Reference 46

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Learning multiple layers of features from tiny images

Reference 47

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Clip benchmark: Clip-like model evaluation, 2022

Reference 48

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 49

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Transfer learning in computer vision tasks: Remember where you come from

Reference 50

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Evaluating object hallucination in large vision-language models

Reference 51

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Unresolved cited work

Reference 52

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Visual spatial reasoning

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Unresolved cited work

Reference 54

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Decoupled Weight Decay Regularization

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This paper cites Understanding Zero-Shot Adversarial Robustness for Large-Scale Models.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 56

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Ok-vqa: A visual question answering benchmark requiring external knowledge

Reference 57

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Y., et al

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Dinov2: Learning robust visual features without supervision

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This paper cites Do Vendi Scores Converge with Finite Samples? Truncated Vendi Score for Finite-Sample Convergence Guarantees.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Do Vendi Scores Converge with Finite Samples? Truncated Vendi Score for Finite-Sample Convergence Guarantees

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This paper cites Scendi Score: Prompt-Aware Diversity Evaluation via Schur Complement of CLIP Embeddings.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Scendi Score: Prompt-Aware Diversity Evaluation via Schur Complement of CLIP Embeddings

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This paper cites Towards a scalable reference-free evaluation of generative models.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Towards a scalable reference-free evaluation of generative models

Reference 62

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This paper cites M., Vedaldi, A., Zisserman, A., and Jawahar, C.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models M., Vedaldi, A., Zisserman, A., and Jawahar, C

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This paper cites W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

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This paper cites Am-radio: Agglomerative vision foundation model reduce all domains into one.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Am-radio: Agglomerative vision foundation model reduce all domains into one

Reference 65

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This paper cites Be More Diverse than the Most Diverse: Optimal Mixtures of Generative Models via Mixture-UCB Bandit Algorithms.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Be More Diverse than the Most Diverse: Optimal Mixtures of Generative Models via Mixture-UCB Bandit Algorithms

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This paper cites Improved zero-shot classification by adapting vlms with text descriptions.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Improved zero-shot classification by adapting vlms with text descriptions

Reference 67

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This paper cites CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement

Reference 68

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models MoME: Mixture of Multimodal Experts for Generalist Multimodal Large Language Models

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Finetuning Text-to-Image Diffusion Models for Fairness

Reference 71

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders

Reference 72

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Towards vqa models that can read

Reference 73

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Unresolved cited work

Reference 74

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models L., Taylor, E., and Loaiza-Ganem, G

Reference 75

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 76

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Winoground: Probing vision and language models for visio-linguistic compositionality

Reference 77

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This paper cites Eyes wide shut? exploring the visual shortcomings of multimodal llms.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Eyes wide shut? exploring the visual shortcomings of multimodal llms

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models S., Linmans, J., Winkens, J., Cohen, T., and Welling, M

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Clip the gap: A single domain generalization approach for object detection

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models S., Steiner, A

Reference 81

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Unresolved cited work

Reference 82

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This paper cites Diffusion feedback helps clip see better.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Diffusion feedback helps clip see better

Reference 83

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This paper cites CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction

Reference 84

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Demystifying CLIP data

Reference 85

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Explicit inductive bias for transfer learning with convolutional networks

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This paper cites From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions

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This paper cites C., and Berg, T.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models C., and Berg, T

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:26:15.570210Z

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This paper cites Convolutions die hard: Open-vocabulary segmentation with single frozen convolutional clip.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Convolutions die hard: Open-vocabulary segmentation with single frozen convolutional clip

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Observation 215a7a43-1c7f-4c53-bc74-17924b037985 · outbound

This paper cites When and why vision-language models behave like bags-of-words, and what to do about it? In International Conference on Learning Representations, 2023.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models When and why vision-language models behave like bags-of-words, and what to do about it? In International Conference on Learning Representations, 2023

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Observation 2cb6e895-9f87-4c8d-8c4d-69b22d6ba8ec · outbound

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

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Sigmoid loss for language image pre-training

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This paper cites Unveiling Differences in Generative Models: A Scalable Differential Clustering Approach.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Unveiling Differences in Generative Models: A Scalable Differential Clustering Approach

Reference 92

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This paper cites An Interpretable Evaluation of Entropy-based Novelty of Generative Models.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models An Interpretable Evaluation of Entropy-based Novelty of Generative Models

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Observation dc3159f4-8721-495b-b9dd-4cb65b7c68ef · outbound

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

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Tip-adapter: Training-free adaption of clip for few-shot classification

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:26:15.510047Z

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Observation bd3c76e1-ac43-4256-a6c3-cb6b3b7ec446 · outbound

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models H., Zhou, L., Dai, X., Yuan, L., Li, Y., et al

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models C., and Liu, Z

Reference 96

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This paper cites Rethinking Centered Kernel Alignment in Knowledge Distillation.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Rethinking Centered Kernel Alignment in Knowledge Distillation

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This paper cites write newline.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models write newline

Reference 98

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Pith citing papers

Observation d62736ac-a819-41bb-a7c7-eb0abf546778 · inbound

UniCon: Unified Framework for Efficient Contrastive Alignment via Kernels cites this paper.

UniCon: Unified Framework for Efficient Contrastive Alignment via Kernels Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models

Reference 3

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Observation a3fff1ec-2d20-4ddc-ad54-cc96cf462af6 · inbound

Latent Denoising Improves Visual Alignment in Large Multimodal Models cites this paper.

Latent Denoising Improves Visual Alignment in Large Multimodal Models Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models

Reference 26

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A$_3$B$_2$: Adaptive Asymmetric Adapter for Alleviating Branch Bias in Vision-Language Image Classification with Few-Shot Learning cites this paper.

A$_3$B$_2$: Adaptive Asymmetric Adapter for Alleviating Branch Bias in Vision-Language Image Classification with Few-Shot Learning Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models

Reference 11

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Observation 885178fa-8e7d-4394-a495-a60a3a927002 · inbound

A$_3$B$_2$: Adaptive Asymmetric Adapter for Alleviating Branch Bias in Vision-Language Image Classification with Few-Shot Learning cites this paper.

Reference 11

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