Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T15:47:30.027583Z
Paper Citation Record · LEDGER
UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval
As of 15 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2412.10680.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
52 of 52 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation e7c20acc-6faf-406d-9ba6-b9a243c15c7c · outbound
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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Observation c86e320a-53d8-419d-8400-e58f530bd862 · outbound
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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Observation 377af791-0d3e-45af-b3cf-867a02808439 · outbound
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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Observation bd62b85a-3661-4dcc-ad5d-a749915a5507 · outbound
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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Source-reported events for the cited work
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Observation cfab177c-9657-47cf-8a03-00c44a2fdc7a · outbound
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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Observation 5e9788c6-51b5-441c-b445-3061402f1cbf · outbound
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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Observation 490d7aff-032c-4779-a200-298ee81d4ac9 · outbound
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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Observation 93a3e97c-cd02-45a4-aa96-58e869fc8a3b · outbound
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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Source-reported events for the cited work
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Observation 229a7498-2848-4734-8554-ddcbe02791d0 · outbound
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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Source-reported events for the cited work
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Observation aff4ac5b-6297-4adb-9c9a-cde781e65cee · outbound
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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Source-reported events for the cited work
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Observation 4f196da1-e340-4723-8935-93e7ad696ca7 · outbound
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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Source-reported events for the cited work
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Observation 942c3675-23d7-46b6-9fad-a5e1d65adb53 · outbound
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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Observation 8391679f-03bc-4a3e-90f1-0b74bef8662a · outbound
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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Observation c26ddd30-0178-4617-8437-d7b4ba8b7ad6 · outbound
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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Source-reported events for the cited work
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Observation bf8669c8-0e8e-4b6c-8031-19ecfff35aea · outbound
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
- Resolution
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Observation ca65de6f-5287-4b7a-b79e-9d68699c7955 · outbound
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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Observation a224e1a6-1f02-4462-b133-e2e632cf7d70 · outbound
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
- Resolution
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Source-reported events for the cited work
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Observation 13988ba2-8842-41ca-ab24-e62e8c094c75 · outbound
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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Observation 4d5fd7ae-18e7-4dad-a607-ac2274b345e5 · outbound
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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Observation 67ccb769-1768-4e38-ace6-1ce541c7e559 · outbound
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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Observation 9cab681e-3a91-4fb9-9604-8ba026d6f0e5 · outbound
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
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Observation 8c93638e-a690-47f6-91a3-f5094def5f66 · outbound
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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Observation 52d56236-2b59-4f70-bbab-e6440e584ac7 · outbound
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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Observation ee9eb51a-e7c5-47a8-bb74-5fcbcbbfd3b7 · outbound
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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Observation 8f8ce084-4963-4c06-a42b-4acbc9d80712 · outbound
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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Observation 3c4d17bd-5af2-44fd-9056-7ba131f0376e · outbound
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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Observation 9b465454-68cd-4fe2-a27b-212dd94e64f1 · outbound
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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Observation 9f6387b6-a9bf-448e-99c8-dfbdf7c02140 · outbound
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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Observation d4b698ba-035d-4e81-ad05-10a51fb62318 · outbound
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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Observation 0fc1bb8b-bed2-4b57-a62a-26ad2f8141f6 · outbound
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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Observation 3ca5babd-0e75-460e-bf33-edd39f7e7142 · outbound
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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Source-reported events for the cited work
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Observation 37a23471-ef22-43b6-992b-f69cea5e195b · outbound
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
- Resolution
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Source-reported events for the cited work
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Observation e75ab3bb-ab6d-4e1d-b136-a031205df59d · outbound
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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Source-reported events for the cited work
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Observation 3a19ab14-f4f7-4334-a1e2-bd7f73674757 · outbound
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
- Resolution
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Source-reported events for the cited work
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Observation fa7000d5-74ab-4805-bff3-ad647d01ca80 · outbound
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
Reference 38
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Source-reported events for the cited work
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Observation 2c3b03ca-c892-4d89-a427-7fc607d43db4 · outbound
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
- Resolution
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Source-reported events for the cited work
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Observation f9963754-31fe-4920-b53a-d08ef7ddd271 · outbound
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
- Resolution
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Source-reported events for the cited work
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Observation fe84fac4-91b9-4858-aa70-fb355f6bd293 · outbound
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
- Resolution
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Source-reported events for the cited work
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Observation 6b8e1403-f8ea-4c5a-979b-f7db6cfbbcf2 · outbound
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
- Resolution
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Source-reported events for the cited work
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Observation 725281c3-fbad-4123-8537-e874e16bb5dd · outbound
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
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Source-reported events for the cited work
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Observation 3e44bcd6-1cfa-47c0-b9a2-8624c89f36bf · outbound
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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Source-reported events for the cited work
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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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- raw_fallback, observed 2026-08-11T15:47:30.305360Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 5e9133de-e645-4298-a2fc-c2d71a4eb0f1 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T15:47:30.280229Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a0821ea1-f111-43af-974b-4da53c6c327a · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T15:47:30.260180Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 891a94b8-d415-4c6d-8ccf-e495c334aea1 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation e38a05f0-a20e-4061-8fd3-085ca8e66726 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 19a1227f-019e-40fb-a072-669294c18e85 · outbound
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T15:47:30.019008Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 21331f54-17a1-4bbc-ae19-da5906a76dc4 · outbound
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T15:47:30.027583Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.