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Paper Citation Record · LEDGER

Dual-Path Stable Soft Prompt Generation for Domain Generalization

As of 8 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2505.18770.

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

pith.paper-citation-record.v1
2505.18770 v1

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:29:37.864637Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

60 of 60 outbound references displayed

  • verified exact0
  • verified fuzzy36
  • unresolved24
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b3851154-9f71-41e6-83cb-0158e732df25 · outbound

This paper cites Domain general- ization: A survey,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Domain general- ization: A survey,

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 70e0585a-67a1-44ae-86c4-b06f27bbb695 · outbound

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

Dual-Path Stable Soft Prompt Generation for Domain Generalization Generalizing to unseen domains: A survey on domain generalization,

Reference 2

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no resolver link, observed 2026-08-07T14:29:33.910869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation d40e93fc-8a01-4279-b479-e76298f6326c · outbound

This paper cites Generalizing to unseen domains via adversarial data augmentation,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Generalizing to unseen domains via adversarial data augmentation,

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 8ebde99e-3e21-49e4-8b0c-fa3744d0da1f · outbound

This paper cites A simple feature augmentation for domain generalization,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization A simple feature augmentation for domain generalization,

Reference 4

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raw_fallback, observed 2026-08-07T14:29:45.289633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 0b4c6ed9-20d9-4c71-aa54-5eb6b7a00b82 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Dual-Path Stable Soft Prompt Generation for Domain Generalization mixup: Beyond Empirical Risk Minimization

Reference 5

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no resolver link, observed 2026-08-07T14:29:34.119648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:34.119648Z digest=sha256:8a70bfd6be92feba636a5d0bd90519d27c4f2e8fc48294e1a4953f138420a068

Observation 45796ff8-77ea-4ff6-a43d-bbab9d69d534 · outbound

This paper cites Domain generalization via invariant feature representation,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Domain generalization via invariant feature representation,

Reference 6

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no resolver link, observed 2026-08-07T14:29:34.176320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:34.176320Z digest=sha256:ef142868388e58e4cf076cad04b2f4aea5e173ea87cfee5b0b2c55a1481e552e

Observation f5527f58-f98c-4922-be51-4aea68bb22db · outbound

This paper cites Domain generalization with small data,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Domain generalization with small data,

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:34.236645Z digest=sha256:b7bb9b4e9035634f971d7f205b8112444ca79830ee15e25817a8994066c7fb28

Observation 62450aa6-a4d6-4962-8fd6-4cfa2a01b571 · outbound

This paper cites Ensemble of averages: Improving model selection and boosting performance in domain gener- alization,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Ensemble of averages: Improving model selection and boosting performance in domain gener- alization,

Reference 8

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raw_fallback, observed 2026-08-07T14:29:45.000553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:34.321533Z digest=sha256:077a346ac05adc32ba441149bc082749475954997403757881256211d159ea65

Observation 014ed25d-6a98-4e45-b282-5186b8805875 · outbound

This paper cites Domain adaptation via prompt learning,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Domain adaptation via prompt learning,

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:34.387196Z digest=sha256:cca17187668770b983057fe453038ec0d0624bdf8dcfa26461a930fece184b12

Observation 589d9d6e-ccaf-4ae0-a8e1-0f7ce88d5416 · outbound

This paper cites Prompt-based distribution alignment for unsupervised domain adaptation,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Prompt-based distribution alignment for unsupervised domain adaptation,

Reference 10

Resolution
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raw_fallback, observed 2026-08-07T14:29:44.776215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:34.449537Z digest=sha256:cfd583056f4d754c32f44d1200de7e42223b2e9935ff74ba2f4fd970c7cccfa6

Observation b6814aa9-faa1-42d2-9153-9b59880164ab · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Learning transferable visual models from natural language supervision,

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:34.529806Z digest=sha256:32a3c9ee61c5d8a08e12870e3bd7a51c08a3dee19d703587f63c514811d4d170

Observation a6bfda7c-b6a6-43af-b431-39241ffe3198 · outbound

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

Dual-Path Stable Soft Prompt Generation for Domain Generalization Scaling up visual and vision-language representation learning with noisy text supervision,

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:34.595412Z digest=sha256:6f119ae6c0cf1cc9177bd628ec07b3866d97b13c668ff0122f987b03a8e70f66

Observation c3d0ef34-3216-4b39-941c-0ba3fd46420a · outbound

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

Dual-Path Stable Soft Prompt Generation for Domain Generalization Learning to prompt for vision- language models,

Reference 13

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no resolver link, observed 2026-08-07T14:29:34.654802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:34.654802Z digest=sha256:b567d7219f333afa2eb649ae7af47e26830e1cdc4f4b92bc5b5e02c2296fe9ea

Observation ed9c31f9-b857-487a-bd06-383e5ae4aa40 · outbound

This paper cites Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal Perspective.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal Perspective

Reference 14

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Unavailable: canonical work link unavailable.

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Observation 1162aaaf-69cf-4784-a5b2-c7d69785c281 · outbound

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

Dual-Path Stable Soft Prompt Generation for Domain Generalization Maple: Multi-modal prompt learning,

Reference 15

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no resolver link, observed 2026-08-07T14:29:34.818344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:34.818344Z digest=sha256:f49993917ad55b34630d701768c23d04852cc86dfb8e40e370d96a52bbf6ab6b

Observation a6c1cd7c-cf63-4375-877c-15ce25dc43cc · outbound

This paper cites Domain prompt learning for efficiently adapting clip to unseen domains,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Domain prompt learning for efficiently adapting clip to unseen domains,

Reference 16

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:34.871550Z digest=sha256:e387488218cc3c0acdf4600417198a308bcd045813d9f5ac13577342f0125141

Observation 67015d0c-2f63-4a0f-b509-7280fee28930 · outbound

This paper cites Soft prompt generation for domain generalization,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Soft prompt generation for domain generalization,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:44.290230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:34.915462Z digest=sha256:aba9e3616d90df87cba3c43b13419a4fec24d71c6fb1f6843582438a4ee3c35a

Observation 3cce10dc-01a0-4816-bac6-00f5a138d361 · outbound

This paper cites Cbda: Contrastive-based data augmentation for domain generalization,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Cbda: Contrastive-based data augmentation for domain generalization,

Reference 18

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raw_fallback, observed 2026-08-07T14:29:44.023637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 8963d02f-ecb5-4c39-a37d-c2c127c3cda8 · outbound

This paper cites Mixup-induced domain extrapolation for domain generalization,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Mixup-induced domain extrapolation for domain generalization,

Reference 19

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:35.010249Z digest=sha256:966fb8d7c5ff49b777753bb2bd6463a29f264a9925eb20e2398c29401957aac0

Observation d2be7144-e616-40d5-ae9e-31fbf02a76c3 · outbound

This paper cites Domain generalization with adversarial feature learning,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Domain generalization with adversarial feature learning,

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:35.097705Z digest=sha256:607524e750ff16f3acd901ceaacdf8a7d4bfaf7138443520afeac99f5552436a

Observation 2662f800-ed2b-4711-b841-ff506dc84607 · outbound

This paper cites Domain generalization via inter- domain alignment and intra-domain expansion,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Domain generalization via inter- domain alignment and intra-domain expansion,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:43.582537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:35.229501Z digest=sha256:f570605a41a609c77b193646f82066861e3a192a9695a970aac00f70b5e23465

Observation 3f28dec5-874d-426a-8ac0-f06b0b9a7a1a · outbound

This paper cites Domain-adversarial training of neural networks,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Domain-adversarial training of neural networks,

Reference 22

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raw_fallback, observed 2026-08-07T14:29:43.403055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:35.317081Z digest=sha256:cb40f508a7f2b784d18bc1ab8084b2f62808f51f0490ec5e7084ae434bfc78c1

Observation 264ee814-5816-4452-a30b-ea8a4381656f · outbound

This paper cites Deep domain generalization via conditional invariant adversarial networks,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Deep domain generalization via conditional invariant adversarial networks,

Reference 23

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:35.397636Z digest=sha256:a6687be6ebdde9d2e9cccd64250acc3dc9de90b8443954d7f9a60b27cf222df0

Observation 9e9ab3af-f83f-4ddf-99a0-9ae6e891852c · outbound

This paper cites Invariant Risk Minimization.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Invariant Risk Minimization

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:35.510562Z digest=sha256:4d171d2d9f26b71d95ff3025bd947e6208d907d00561890217b9cbfc899595f3

Observation 67ed05cb-2f49-4064-9ed7-5b64ff7cf9f4 · outbound

This paper cites Invariant information bottleneck for domain generalization,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Invariant information bottleneck for domain generalization,

Reference 25

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raw_fallback, observed 2026-08-07T14:29:43.168446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:35.541680Z digest=sha256:616864c25d221bc8da6b0ec66cc639120b7c06c0a6044f6ce6b99416878c9900

Observation b1002d5f-5e56-4c18-8af8-ebb685d44906 · outbound

This paper cites Exploiting domain- specific features to enhance domain generalization,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Exploiting domain- specific features to enhance domain generalization,

Reference 26

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verified fuzzy
raw_fallback, observed 2026-08-07T14:29:43.028298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:35.587561Z digest=sha256:f2e01852c8dac4c1017713eec6304c6113ae1db901b7bb6ea816db2fb4b8cbdd

Observation d0e3e15b-c2d5-4e04-b5f4-c36e6b0e4f58 · outbound

This paper cites Simple: Specialized model-sample matching for domain generalization,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Simple: Specialized model-sample matching for domain generalization,

Reference 27

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verified fuzzy
raw_fallback, observed 2026-08-07T14:29:42.880589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:35.654499Z digest=sha256:3ff39ece5bdfdbee19c237aaf9ac8dfc317fe5fee158d89b777a8f84c75b1b20

Observation b311f995-66a6-4b37-8264-eef2673fd261 · outbound

This paper cites Mixstyle neural networks for domain generalization and adaptation,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Mixstyle neural networks for domain generalization and adaptation,

Reference 28

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raw_fallback, observed 2026-08-07T14:29:42.727176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:35.734207Z digest=sha256:065236774d5e74455743469a158602954488deb80dd73afc9b6e08cb9e569d9c

Observation 27fa6310-ffba-4e4a-adeb-12bccb55f247 · outbound

This paper cites Knowledge distillation-based domain-invariant representation learning for domain generalization,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Knowledge distillation-based domain-invariant representation learning for domain generalization,

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-07T14:29:42.579352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:35.803843Z digest=sha256:67cd4520a302b8c3cd012f0b1ef97450fea9b17a2f164822a56b89816879e2a3

Observation c60fa027-2372-49c9-a3af-67ea01007622 · outbound

This paper cites Boosting domain generalization by domain-aware knowledge distillation,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Boosting domain generalization by domain-aware knowledge distillation,

Reference 30

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verified fuzzy
raw_fallback, observed 2026-08-07T14:29:42.415967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:35.876875Z digest=sha256:409867d842ec9321b133b53e4cf36a75d478cb9d779151f098fddd994fcb96c6

Observation 11d0b151-1984-4748-bf91-92d0ca270ec8 · outbound

This paper cites Learning to generalize: Meta-learning for domain generalization,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Learning to generalize: Meta-learning for domain generalization,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:42.284486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:35.928730Z digest=sha256:3fa38dc4148c0060703d538a7ae00eba2ce12cbe85e37385db3d3d014f3ef7ff

Observation b4b31a4f-916e-4c94-9440-969f57c8ab21 · outbound

This paper cites Discriminative adversarial do- main generalization with meta-learning based cross-domain validation,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Discriminative adversarial do- main generalization with meta-learning based cross-domain validation,

Reference 32

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verified fuzzy
raw_fallback, observed 2026-08-07T14:29:42.133741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:35.984747Z digest=sha256:76cc0a56d6ec4b80d1905b60523eb9abe0c47006f3ad5a9108ff01ea0211c6d7

Observation b727a7d7-d7cd-44dc-9fbc-13d5778fa290 · outbound

This paper cites Learning common and specific visual prompts for domain generalization,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Learning common and specific visual prompts for domain generalization,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:42.033559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:36.029752Z digest=sha256:d0efd1b949d3dde9d9e8ea582d354c9c33bcbe09552362fb950d06d1cf426131

Observation 92661be1-a9c5-4389-b3cf-2a391db1eca8 · outbound

This paper cites PromptTA: Prompt-driven Text Adapter for Source-free Domain Generalization.

Dual-Path Stable Soft Prompt Generation for Domain Generalization PromptTA: Prompt-driven Text Adapter for Source-free Domain Generalization

Reference 34

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no resolver link, observed 2026-08-07T14:29:36.084962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:36.084962Z digest=sha256:bcf1c8ee8b2d292db47780270f5a5389d2191fc87484b1ebb45194fe084bba9d

Observation 402067f9-56c6-425e-8305-57256cf06bb0 · outbound

This paper cites Consistent prompt learning for vision-language models,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Consistent prompt learning for vision-language models,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:41.914169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:36.150071Z digest=sha256:aaf60579c2da08f48cec5975e161b62ffb82b6b5ce53752f93feb1faa34c5a3d

Observation 57fe4ef1-290b-48eb-b516-900a66ee8451 · outbound

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

Dual-Path Stable Soft Prompt Generation for Domain Generalization Tip-adapter: Training-free adaption of clip for few-shot classification,

Reference 36

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no resolver link, observed 2026-08-07T14:29:36.198006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:36.198006Z digest=sha256:69c215884ae0c64bf3a6cba861891703d0e48fca171dcdd075bb9aecd114373a

Observation bc71646b-af73-4c92-8255-808d6afff780 · outbound

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

Dual-Path Stable Soft Prompt Generation for Domain Generalization Clip-adapter: Better vision-language models with feature adapters,

Reference 37

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unresolved
no resolver link, observed 2026-08-07T14:29:36.245217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:36.245217Z digest=sha256:83da53b36379ac402bd37cac2529d9d62d4e0f217282bca294fa785510009adb

Observation d06fd343-9f38-4a4c-8a62-2c5243f90f5d · outbound

This paper cites Clipceil: Domain generalization through clip via channel refinement and image-text alignment,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Clipceil: Domain generalization through clip via channel refinement and image-text alignment,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:41.742392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:36.305833Z digest=sha256:7ebf466a9eb30739a5328f0f72fcd28d10cb09511f9b242189ef4626ae8d15b2

Observation 8f04a2b0-f00c-4543-8082-584f6db37e48 · outbound

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

Dual-Path Stable Soft Prompt Generation for Domain Generalization Stylip: Multi-scale style-conditioned prompt learning for clip-based domain generalization,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:41.587481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:36.362284Z digest=sha256:1952f66fe9f80d326c4b0d5cc9653b94af2e497b51097c980186a48b59998c89

Observation c2f67598-09f9-4d58-8157-a1c2aa8d7a7b · outbound

This paper cites Disentangled prompt representation for domain generalization,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Disentangled prompt representation for domain generalization,

Reference 40

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unresolved
no resolver link, observed 2026-08-07T14:29:36.415810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:36.415810Z digest=sha256:e4c2d0606996436519c08a2529e343ea65686b477b75349904f8d7d272b64cb8

Observation 63602186-1200-47f8-8bb2-0d78a2a91b43 · outbound

This paper cites Ensembling disentangled domain-specific prompts for domain generalization,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Ensembling disentangled domain-specific prompts for domain generalization,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:41.418089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:36.462367Z digest=sha256:b568c365aad34181748c674c49d18add56636c256b8cf2793cb5bb6acbc2593b

Observation b2b063b9-13d3-4317-aaac-ee27aece9558 · outbound

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

Dual-Path Stable Soft Prompt Generation for Domain Generalization Conditional prompt learning for vision-language models,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:41.248625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:36.518194Z digest=sha256:ecf89b279a621226027cc639e7dba21af72b2becec10be1898cf9be3e46da444

Observation 6e650738-86b3-4834-a8e7-4f4e6ca203bf · outbound

This paper cites Nlnl: Negative learning for noisy labels,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Nlnl: Negative learning for noisy labels,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:41.048691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:36.571271Z digest=sha256:0246502817e1958c4bc0b362ece75c48f04f65da90c45ca10ee0f56a5a91abf6

Observation e36f5134-c974-473c-9176-6e7a322150a4 · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization A simple framework for contrastive learning of visual representations,

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T14:29:36.642608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:36.642608Z digest=sha256:62ffe69d535d3cd5188925419d486d8c1d87db76d0dd400396af2d254d5e8f28

Observation 84f9cfaa-c486-40be-a15f-a500cca54554 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Momentum contrast for unsupervised visual representation learning,

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T14:29:36.697833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:36.697833Z digest=sha256:2e795132a9ef177207cd4e0525fd74b073bb2ac2607ccc3da4e84419cfcc24af

Observation 7c47ab84-aac7-40f6-9a1c-e773b6df4433 · outbound

This paper cites Learning open set network with discriminative reciprocal points,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Learning open set network with discriminative reciprocal points,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:40.785482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:36.745893Z digest=sha256:39adbce22e91035eeb5a6a6e5a4693a65f02f82422344688ed7ffcf056776521

Observation 65ec4c2d-a69f-4aac-87d1-efda6e3739e9 · outbound

This paper cites Argue: Attribute-guided prompt tuning for vision-language models,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Argue: Attribute-guided prompt tuning for vision-language models,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:40.495025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:36.791741Z digest=sha256:f965548bc9750c1308639d6d7b451d1eaf50b41f62144f5f8a033e3b52e63471

Observation 800772a8-3699-47da-9d44-1d8b28ed4823 · outbound

This paper cites Clipn for zero-shot ood detection: Teaching clip to say no,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Clipn for zero-shot ood detection: Teaching clip to say no,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:40.184328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:36.844673Z digest=sha256:7aea962dbc0d6d229720118d3e2519d3b843125a41b68a11b78e96c859f9e538

Observation bd94fe83-80d2-44da-92e5-d7475b8fbd27 · outbound

This paper cites Learning transferable negative prompts for out-of-distribution detection,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Learning transferable negative prompts for out-of-distribution detection,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:39.837650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:36.890688Z digest=sha256:0ed897bff459f7f2590f5f22a3e7c66eba896b34cf054be209e91dbd36307eb1

Observation 6057958c-6f45-41e2-bc1c-c66339487998 · outbound

This paper cites Semi- supervised learning with pseudo-negative labels for image classifica- tion,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Semi- supervised learning with pseudo-negative labels for image classifica- tion,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:39.566086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:36.939319Z digest=sha256:ecc240cca93dd57defa4c0cde34c6fed29e070e1da1fa6c0418c0866f6262d04

Observation ec55a492-0108-4e57-bd5c-8104d9ae7735 · outbound

This paper cites Vision-language models are strong noisy label detectors,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Vision-language models are strong noisy label detectors,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:39.380084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:36.974547Z digest=sha256:359d668f8f28658baa706453f00a2b12c54b9e017a663302475f7eb06a60c0a8

Observation c9a7fd13-a13d-4981-b28a-259de1df9702 · outbound

This paper cites Deeper, broader and artier domain generalization,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Deeper, broader and artier domain generalization,

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-07T14:29:37.042472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:37.042472Z digest=sha256:7a69babe93f44a068fbe84a3aeb1d47c38240d60fedbc50161cbd87ded75f932

Observation 2d950008-1c65-4f08-8e85-3fc3a153f7bc · outbound

This paper cites Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:39.175878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:37.105946Z digest=sha256:3c536c53a07a66f6ae5b0531538c460cd0902c3bf6dd021868a5789292e9302b

Observation 03a4fd0c-153c-4e9d-a7d4-feec6e32f702 · outbound

This paper cites Deep hashing network for unsupervised domain adaptation,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Deep hashing network for unsupervised domain adaptation,

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-07T14:29:37.182255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:37.182255Z digest=sha256:a66f9fe954ad89fa17627fe893a1effa82c233277e7bdccb712af6bf59e22683

Observation 05614b97-23fd-4ad6-9e07-01f1531ba793 · outbound

This paper cites Recognition in terra incognita,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Recognition in terra incognita,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:38.941197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:37.345940Z digest=sha256:ae219076a23cebc37c6f86bbdc3b096a7e77d1361f7e31e1f3d91e60d4713c35

Observation 1257b819-bf59-4c84-8be9-4c5b1cbe1b93 · outbound

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

Dual-Path Stable Soft Prompt Generation for Domain Generalization Moment matching for multi-source domain adaptation,

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-07T14:29:37.409295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:37.409295Z digest=sha256:a6479e939e2d920df053f689175956eee6bde1e0cbb44c61fe931fcd30521770

Observation fd6ac35b-7812-4cdd-8c20-dbe9ee594062 · outbound

This paper cites In search of lost domain generalization,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization In search of lost domain generalization,

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:38.677906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:37.483638Z digest=sha256:e8374bdd48020bac38d430a8baf7edf51c6c7d276a5f9ecc3d8c4986e9ba5166

Observation 5826920c-f232-4b52-a8bb-3bd4afbf0c31 · outbound

This paper cites Swad: Domain generalization by seeking flat minima,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Swad: Domain generalization by seeking flat minima,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:29:38.322825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:29:37.544679Z digest=sha256:18d0682cbbe28794cf4901d70cd8c4f681d985799b8a63bbe37b6493249635e7

Observation b2f9bdee-b5ef-488f-a24b-645e8e0715c4 · outbound

This paper cites Exploring Visual Prompts for Adapting Large-Scale Models.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Exploring Visual Prompts for Adapting Large-Scale Models

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-07T14:29:37.658386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:37.658386Z digest=sha256:b94c694f37bec9135acdbb674668e9ed6c172c69b7b7f2b25cf22b02a630f661

Observation 9d9006d5-5e40-44cd-bd74-25dd1dceaa55 · outbound

This paper cites Visual prompt tuning,.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Visual prompt tuning,

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-07T14:29:37.864637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:37.864637Z digest=sha256:9085d79c12416220373788b0b815e585538ef914bd7e667df11f358a88fa57b2

Pith citing papers

No inbound Pith citation observations are available.