Paper Citation Record · LEDGER

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning

As of 10 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2507.21786.

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

pith.paper-citation-record.v1
2507.21786 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T12:26:42.570770Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

38 of 38 outbound references displayed

verified exact0
verified fuzzy22
unresolved16
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4bb4c621-5720-4bd1-928d-067e2500ac78 · outbound

This paper cites GPT-4 Technical Report.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning GPT-4 Technical Report

Reference 1

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Source-reported events for the cited work

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Observation 48081863-8d15-442b-a460-1d93f341b3d6 · outbound

This paper cites Flamingo: a visual language model for few-shot learning.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Flamingo: a visual language model for few-shot learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T12:26:42.394583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee2b204a-3602-4924-88f2-7c5ea2730331 · outbound

This paper cites Food-101–mining discriminative components with random forests.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Food-101–mining discriminative components with random forests

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:43.142188Z

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Observation 155cdea8-5fbc-4b08-8ee3-0411dd52601a · outbound

This paper cites Lan- guage models are few-shot learners.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Lan- guage models are few-shot learners

Reference 4

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Source-reported events for the cited work

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Observation ca5870cd-ef62-4863-98c6-070100981a38 · outbound

This paper cites Describing textures in the wild.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Describing textures in the wild

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:43.114317Z

Source-reported events for the cited work

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Observation 054b9468-a565-4ac9-b986-262509d6b5fd · outbound

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

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Imagenet: A large-scale hierarchical image database

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:43.098290Z

Source-reported events for the cited work

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Observation ebe38a1e-0f71-468f-982d-d83eba93a627 · outbound

This paper cites Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories

Reference 7

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Source-reported events for the cited work

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Observation 035bec05-c1d5-4559-82cf-88f20e5a853b · outbound

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

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Clip-adapter: Better vision-language models with feature adapters

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:43.072403Z

Source-reported events for the cited work

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Observation 4c391b29-8e4f-4c86-b444-0440cb733d8b · outbound

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

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:43.055707Z

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Observation fc7ab030-e928-47de-9859-fe6ac44c44c1 · outbound

This paper cites The many faces of robust- ness: A critical analysis of out-of-distribution generalization.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning The many faces of robust- ness: A critical analysis of out-of-distribution generalization

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:43.039614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation eb2e5560-b380-4b14-98e1-4f6b7a3cd74e · outbound

This paper cites Natural adversarial examples.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Natural adversarial examples

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:43.023679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f1b5895c-f4a9-4521-ba96-2e644ae35af8 · outbound

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

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 12

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Source-reported events for the cited work

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Observation d19d1ab2-dddd-4606-aa54-3f1b97683d23 · outbound

This paper cites 10 Maple: Multi-modal prompt learning.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning 10 Maple: Multi-modal prompt learning

Reference 13

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Source-reported events for the cited work

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Observation 7fd9b75b-1565-428a-b259-d1f5388a1453 · outbound

This paper cites Aapl: Adding attributes to prompt learning for vision-language models.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Aapl: Adding attributes to prompt learning for vision-language models

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:42.980834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b10b8c91-bd5e-4f30-a5b9-d67424192ca0 · outbound

This paper cites 3d object representations for fine-grained categorization.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning 3d object representations for fine-grained categorization

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:42.964838Z

Source-reported events for the cited work

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Observation e2518c4c-361c-4581-967b-88ad89588b67 · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 16

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Source-reported events for the cited work

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Observation 614035f4-314e-44ab-9947-86d5c38d6720 · outbound

This paper cites DeepSeek-V3 Technical Report.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning DeepSeek-V3 Technical Report

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T12:26:42.468362Z

Source-reported events for the cited work

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Observation e109f634-8ae6-4656-9ed3-b801afed1dcd · outbound

This paper cites Visual instruction tuning, 2023.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Visual instruction tuning, 2023

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T12:26:42.473201Z

Source-reported events for the cited work

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Observation f31258c7-7f58-4783-9976-79c53335d150 · outbound

This paper cites Learning customized visual models with retrieval-augmented knowledge.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Learning customized visual models with retrieval-augmented knowledge

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:42.927486Z

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Observation fc9e8ddf-6cf1-4a51-9d30-a87ebe0a0c89 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Fine-Grained Visual Classification of Aircraft

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T12:26:42.482852Z

Source-reported events for the cited work

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Observation e28fac58-5575-461c-85ae-8f79857868c8 · outbound

This paper cites Slip: Self-supervision meets language-image pre- training.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Slip: Self-supervision meets language-image pre- training

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:42.911750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e4a95d4b-2a0e-4f71-800d-7ed1cb9c7183 · outbound

This paper cites Automated flower classification over a large number of classes.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Automated flower classification over a large number of classes

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:42.896216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6d76d56d-5001-4e91-a63b-0c333f707c1f · outbound

This paper cites Cats and dogs.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Cats and dogs

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:42.880419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4e501c30-0ec1-4fe8-a027-3d67f5431546 · outbound

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

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Learn- ing transferable visual models from natural language super- vision

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:42.865820Z

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Observation f7e89038-99d2-487c-aeaf-0f0c5d192583 · outbound

This paper cites Do imagenet classifiers generalize to im- agenet? In International Conference on Machine Learning, pages 5389–5400.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Do imagenet classifiers generalize to im- agenet? In International Conference on Machine Learning, pages 5389–5400

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:42.849673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b37f5f3f-3fed-457f-8ded-fd444f65dabc · outbound

This paper cites Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference

Reference 26

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Source-reported events for the cited work

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Observation c4a48f8a-4fb7-4c7f-956d-eeb98c5299ac · outbound

This paper cites K-lite: Learning transferable visual models with external knowledge.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning K-lite: Learning transferable visual models with external knowledge

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:42.832026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0317e271-d1b6-42fa-884f-b93e0531af70 · outbound

This paper cites Meta-adapter: An online few-shot learner for vision-language model.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Meta-adapter: An online few-shot learner for vision-language model

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:42.815403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1042a666-b738-4abd-9f9d-ec338ba2e1b3 · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 29

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Source-reported events for the cited work

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Observation a615628e-0149-4b70-8ee4-d2d562b667ef · outbound

This paper cites Learning robust global representations by penalizing local predictive power.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Learning robust global representations by penalizing local predictive power

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:42.799389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2f2678a7-90c0-4b57-84e3-fd60bd4f980d · outbound

This paper cites Sun database: Large-scale scene recognition from abbey to zoo.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Sun database: Large-scale scene recognition from abbey to zoo

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:42.783260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4b0d00e9-5fde-4354-8869-cc87f588b341 · outbound

This paper cites Unified contrastive learning in image-text-label space.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Unified contrastive learning in image-text-label space

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:42.767576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ec452736-a0b4-4d0b-a8fd-219b3123c9a2 · outbound

This paper cites Visual- language prompt tuning with knowledge-guided context op- timization.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Visual- language prompt tuning with knowledge-guided context op- timization

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T12:26:42.544922Z

Source-reported events for the cited work

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Observation 1f619dc8-8c22-41ad-9e55-a49108b73bb6 · outbound

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

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning FILIP: Fine-grained Interactive Language-Image Pre-Training

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T12:26:42.549669Z

Source-reported events for the cited work

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Observation e18a62d8-6ec2-4bc9-81ee-76005114de11 · outbound

This paper cites Florence: A New Foundation Model for Computer Vision.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Florence: A New Foundation Model for Computer Vision

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T12:26:42.554660Z

Source-reported events for the cited work

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Observation 3691ca97-e2a3-401f-8e57-b9cda3f76cf8 · outbound

This paper cites Conditional prompt learning for vision-language mod- els.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Conditional prompt learning for vision-language mod- els

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T12:26:42.560388Z

Source-reported events for the cited work

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Observation 826f539c-a59e-439b-8c7e-493182ed759f · outbound

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

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Learning to prompt for vision-language models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T12:26:42.565622Z

Source-reported events for the cited work

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Observation be1bb5dd-ecd3-49e8-ade6-44b2dd826307 · outbound

This paper cites Prompt-aligned gradient for prompt tuning.

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning Prompt-aligned gradient for prompt tuning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:26:42.720374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.