UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval | Paper Citation Record

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This paper cites Contrastive learning of semantic concepts for open-set cross-domain retrieval.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Contrastive learning of semantic concepts for open-set cross-domain retrieval

Reference 1

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Observation 806a06bf-7f82-4d88-bffd-f246727241e9 · outbound

This paper cites Handling class-imbalance for improved zero-shot domain generaliza- tion.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Handling class-imbalance for improved zero-shot domain generaliza- tion

Reference 2

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This paper cites General- izing from several related classification tasks to a new unla- beled sample.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval General- izing from several related classification tasks to a new unla- beled sample

Reference 3

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This paper cites Stylip: Multi-scale style- conditioned prompt learning for clip-based domain general- ization.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Stylip: Multi-scale style- conditioned prompt learning for clip-based domain general- ization

Reference 4

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This paper cites A review on multi- modal zero-shot learning.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval A review on multi- modal zero-shot learning

Reference 5

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This paper cites Uniter: Universal image-text representation learning.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Uniter: Universal image-text representation learning

Reference 6

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This paper cites Video ecommerce: Towards online video advertising.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Video ecommerce: Towards online video advertising

Reference 7

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This paper cites Video ecommerce++: Toward large scale online video adver- tising.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Video ecommerce++: Toward large scale online video adver- tising

Reference 8

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This paper cites Video2shop: Exact matching clothes in videos to online shopping images.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Video2shop: Exact matching clothes in videos to online shopping images

Reference 9

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This paper cites On the selection of anchors and targets for video hyperlink- ing.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval On the selection of anchors and targets for video hyperlink- ing

Reference 10

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This paper cites An evaluation of descriptors for large-scale image retrieval from sketched feature lines.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval An evaluation of descriptors for large-scale image retrieval from sketched feature lines

Reference 11

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This paper cites Pros: Prompting-to-simulate generalized knowledge for universal cross-domain retrieval.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Pros: Prompting-to-simulate generalized knowledge for universal cross-domain retrieval

Reference 12

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This paper cites Domain adaptation via prompt learning.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Domain adaptation via prompt learning

Reference 13

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This paper cites Improv- ing diversity with adversarially learned transformations for domain generalization.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Improv- ing diversity with adversarially learned transformations for domain generalization

Reference 14

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This paper cites Mixup as locally linear out-of-manifold regularization.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Mixup as locally linear out-of-manifold regularization

Reference 15

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Observation 6b5fe7f3-cd61-4205-b0a0-9798f1661ef5 · outbound

This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Momentum contrast for unsupervised visual rep- resentation learning

Reference 16

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This paper cites Vi- sual prompt tuning.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Vi- sual prompt tuning

Reference 17

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This paper cites Bert: Pre-training of deep bidirectional trans- formers for language understanding.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Bert: Pre-training of deep bidirectional trans- formers for language understanding

Reference 18

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This paper cites Maple: Multi-modal prompt learning.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Maple: Multi-modal prompt learning

Reference 19

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This paper cites Self-regulating prompts: Foundational model adaptation without forgetting.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Self-regulating prompts: Foundational model adaptation without forgetting

Reference 20

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This paper cites Interpretability be- yond feature attribution: Quantitative testing with concept activation vectors (tcav).

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Interpretability be- yond feature attribution: Quantitative testing with concept activation vectors (tcav)

Reference 21

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This paper cites The power of scale for parameter-efficient prompt tuning.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval The power of scale for parameter-efficient prompt tuning

Reference 22

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

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:47:30.901170Z

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This paper cites Align before fuse: Vision and language representation learn- ing with momentum distillation.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Align before fuse: Vision and language representation learn- ing with momentum distillation

Reference 24

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This paper cites Oscar: Object-semantics aligned pre-training for vision-language tasks.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Oscar: Object-semantics aligned pre-training for vision-language tasks

Reference 25

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This paper cites Prefix-tuning: Optimizing continuous prompts for generation.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Prefix-tuning: Optimizing continuous prompts for generation

Reference 26

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This paper cites Deep sketch hashing: Fast free-hand sketch-based im- age retrieval.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Deep sketch hashing: Fast free-hand sketch-based im- age retrieval

Reference 27

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This paper cites Cocoa: Context-conditional adaptation for recognizing unseen classes in unseen do- mains.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Cocoa: Context-conditional adaptation for recognizing unseen classes in unseen do- mains

Reference 28

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This paper cites Seic: Semantic embed- ding with intermediate classes for zero-shot domain general- ization.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Seic: Semantic embed- ding with intermediate classes for zero-shot domain general- ization

Reference 29

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This paper cites Vireo@ trecvid 2017: Video-to-text, ad-hoc video search and video hyper- linking.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Vireo@ trecvid 2017: Video-to-text, ad-hoc video search and video hyper- linking

Reference 30

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This paper cites Towards Calibrated Robust Fine-Tuning of Vision-Language Models.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Towards Calibrated Robust Fine-Tuning of Vision-Language Models

Reference 31

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This paper cites Robust Adaptation of Foundation Models with Black-Box Visual Prompting.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Robust Adaptation of Foundation Models with Black-Box Visual Prompting

Reference 32

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This paper cites Universal cross-domain retrieval: Generalizing across classes and do- mains.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Universal cross-domain retrieval: Generalizing across classes and do- mains

Reference 33

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This paper cites Clipping: Distilling clip-based models with a student base for video- language retrieval.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Clipping: Distilling clip-based models with a student base for video- language retrieval

Reference 34

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This paper cites Moment matching for multi-source domain adaptation.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Moment matching for multi-source domain adaptation

Reference 35

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This paper cites Language models as knowledge bases? In EMNLP, pages 2463–2473, 2019.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Language models as knowledge bases? In EMNLP, pages 2463–2473, 2019

Reference 36

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This paper cites Learn- ing transferable visual models from natural language super- vision.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Learn- ing transferable visual models from natural language super- vision

Reference 37

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This paper cites Visual semantic segmentation based on few/zero-shot learning: An overview.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Visual semantic segmentation based on few/zero-shot learning: An overview

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This paper cites Clip for all things zero-shot sketch-based image retrieval, fine- grained or not.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Clip for all things zero-shot sketch-based image retrieval, fine- grained or not

Reference 39

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This paper cites The sketchy database: learning to retrieve badly drawn bunnies.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval The sketchy database: learning to retrieve badly drawn bunnies

Reference 40

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This paper cites Structure-aware semantic-aligned network for universal cross-domain retrieval.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Structure-aware semantic-aligned network for universal cross-domain retrieval

Reference 41

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This paper cites Generalizing to unseen domains: A survey on domain generalization.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Generalizing to unseen domains: A survey on domain generalization

Reference 42

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This paper cites Image as a foreign language: Beit pretraining for vision and vision- language tasks.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Image as a foreign language: Beit pretraining for vision and vision- language tasks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:47:30.361529Z

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This paper cites Ra-clip: Retrieval augmented contrastive language-image pre-training.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Ra-clip: Retrieval augmented contrastive language-image pre-training

Reference 44

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Observation fac74cf3-5cce-4b79-8be1-b857f6592ca8 · outbound

This paper cites Towards zero-shot learning: A brief review and an attention-based embedding network.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Towards zero-shot learning: A brief review and an attention-based embedding network

Reference 45

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This paper cites A fourier-based framework for domain generaliza- tion.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval A fourier-based framework for domain generaliza- tion

Reference 46

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This paper cites Mixup without hesitation.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Mixup without hesitation

Reference 47

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Observation 57f2b9bc-8de2-498a-8ae1-09e7ba215c5d · outbound

This paper cites Coca: Contrastive captioners are image-text foundation models.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Coca: Contrastive captioners are image-text foundation models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:47:30.238522Z

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This paper cites mixup: Beyond empirical risk minimiza- tion.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval mixup: Beyond empirical risk minimiza- tion

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:47:30.210814Z

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This paper cites Domain generalization: A survey.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Domain generalization: A survey

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:47:30.179985Z

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

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Conditional prompt learning for vision-language models

Reference 51

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This paper cites Learning to prompt for vision-language models.

UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Learning to prompt for vision-language models

Reference 52

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