Paper Citation Record · LEDGER

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation

As of 8 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2506.21237.

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

pith.paper-citation-record.v1
2506.21237 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:35:35.940992Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:13:52.989408Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact0
verified fuzzy11
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 504cdc2a-6f73-4c39-bab2-b853249a70f8 · outbound

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

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Flamingo: a visual language model for few-shot learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:33.694188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00e7e192-c457-4dbf-bb58-3e8720326a8e · outbound

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

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Food-101–mining discriminative components with random forests

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:33.800893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb26be40-4e42-42ff-a711-1dea2369d3ca · outbound

This paper cites Describing textures in the wild.

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Describing textures in the wild

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:33.841030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 181398a6-6ab1-4fae-9f40-35bb1af922e1 · outbound

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

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Imagenet: A large-scale hierarchical image database

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:33.892433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0517585d-cfee-4f83-9d1d-c302ac428496 · outbound

This paper cites Learning to prompt for open-vocabulary ob- ject detection with vision-language model.

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Learning to prompt for open-vocabulary ob- ject detection with vision-language model

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:35:37.883867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a13b66e8-2572-4f51-8269-e3ce6f3562aa · outbound

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

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:35:37.755968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a5f9b41e-5d6e-4ae0-bef3-ad6adacc3e47 · outbound

This paper cites Open-vocabulary Object Detection via Vision and Language Knowledge Distillation.

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Open-vocabulary Object Detection via Vision and Language Knowledge Distillation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:34.138097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba8180a4-b704-41e0-8a37-6cff1a8fbeba · outbound

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

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:34.213263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab69f3e6-8d5c-4d05-89da-37e7d9d6bba8 · outbound

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

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation The many faces of robust- ness: A critical analysis of out-of-distribution generalization

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:34.240102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9f44a4e-9638-4bb9-b711-319e835b4875 · outbound

This paper cites Natural adversarial examples.

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Natural adversarial examples

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:34.293252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3188e0b-e8b1-440b-bd6c-7b3d9607dd99 · outbound

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

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:34.442103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b11039b-f11e-4539-ad6e-795866f6e5fe · outbound

This paper cites Vi- sual prompt tuning.

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Vi- sual prompt tuning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:35:37.593504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8fcb7215-61bf-4f0b-a398-6b626f97bd98 · outbound

This paper cites A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models.

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:34.634008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5307a984-b6c2-4930-8a00-6aafb6312314 · outbound

This paper cites Nystr ¨om m-hilbert- schmidt independence criterion, 2023.

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Nystr ¨om m-hilbert- schmidt independence criterion, 2023

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:35:37.410050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ba164d62-06b7-44ff-8ab9-c81258115130 · outbound

This paper cites Maple: Multi-modal prompt learning.

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Maple: Multi-modal prompt learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:34.755861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc0be6ad-d4d7-42e5-86e2-c9539bcc9dff · outbound

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

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation 3d object representations for fine-grained categorization

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:35:37.234737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation adb75eef-a141-4b8c-ae9e-f539ab47b1f2 · outbound

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

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:34.867280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28c66107-6424-4f00-9afa-7484491f7c9f · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Fine-Grained Visual Classification of Aircraft

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:34.926793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dd7b640-e79a-43cf-bae1-67a1bf2d08dc · outbound

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

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Automated flower classification over a large number of classes

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:35:37.043191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation afe714f8-02b2-4d7d-9f9c-8f852a85d3f6 · outbound

This paper cites Cats and dogs.

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Cats and dogs

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:35:36.882986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 593a9dca-da67-4906-8a01-6b261a4db736 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Learning transferable visual models from natural language supervi- sion

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:35.130753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2255e6f0-2296-4396-899e-a689c69d6e1e · outbound

This paper cites Do imagenet classifiers generalize to im- agenet? In International conference on machine learning , pages 5389–5400.

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Do imagenet classifiers generalize to im- agenet? In International conference on machine learning , pages 5389–5400

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:35.231579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b73e2fa-603f-439a-838b-bbe17fdfefe9 · outbound

This paper cites Clipood: Generalizing clip to out-of-distributions.

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Clipood: Generalizing clip to out-of-distributions

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:35:36.703967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 76061164-3508-4072-a0a1-4c89d9156f7b · outbound

This paper cites Flava: A foundational language and vision alignment model.

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Flava: A foundational language and vision alignment model

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:35:36.522730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cefecf0a-e21e-4912-ad90-a765a4db1728 · outbound

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

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:35.471644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aafb5d0e-b99d-4364-9c0f-28588c63a9ed · outbound

This paper cites Learning robust global representations by penalizing local predictive power.Advances in Neural Information Pro- cessing Systems, 32, 2019.

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Learning robust global representations by penalizing local predictive power.Advances in Neural Information Pro- cessing Systems, 32, 2019

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:35.545800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d261e325-acf2-4983-bb3f-95c8b0bdff35 · outbound

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

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Sun database: Large-scale scene recognition from abbey to zoo

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:35.596968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd289e98-48b9-4f8f-b1fe-ea080735d5b5 · outbound

This paper cites Cpt: Colorful prompt tuning for pre-trained vision-language models.

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Cpt: Colorful prompt tuning for pre-trained vision-language models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:35.668674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0d0c0cd-c69f-42eb-9339-2e4d68afa344 · outbound

This paper cites Amend to alignment: De- coupled prompt tuning for mitigating spurious correlation in vision-language models.

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Amend to alignment: De- coupled prompt tuning for mitigating spurious correlation in vision-language models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:35:36.329537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8f2a481a-7f8e-4c12-b202-519c983735fa · outbound

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

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Conditional prompt learning for vision-language mod- els

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:35.794278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c97876f-8c63-4329-82f3-0f9c70278afa · outbound

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

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation Learning to prompt for vision-language models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T22:35:35.851454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eebf31b7-eafc-4b0c-95ab-a721ac079771 · outbound

This paper cites a photo of a [CLASS].

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation a photo of a [CLASS]

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:35:36.150603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation 3ac69a89-2b67-4ddc-87df-f7039fc74251 · inbound

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift cites this paper.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:52.989408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.