Differentiable Prompt Learning for Vision Language Models | Paper Citation Record

Observation c4c61fb1-3944-4f7a-9e49-f56b0c6bdef9 · outbound

This paper cites Hierarchical optimization: An introduc- tion.

Differentiable Prompt Learning for Vision Language Models Hierarchical optimization: An introduc- tion

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.403682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cd7a17d0-b3f3-4034-a946-18e1460f239e · outbound

This paper cites , 2018; Xu et al.

Differentiable Prompt Learning for Vision Language Models , 2018; Xu et al

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:52.644302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0d132700-eaf8-4679-a462-ee90eb18dfe4 · outbound

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

Differentiable Prompt Learning for Vision Language Models Food-101–mining discriminative com- ponents with random forests

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.356429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 74294b79-571a-4174-ba34-c2f2b1a7595c · outbound

This paper cites However, the DPL method be- comes less competitive when the number of shots is very limited (e.g.

Differentiable Prompt Learning for Vision Language Models However, the DPL method be- comes less competitive when the number of shots is very limited (e.g

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:52.627918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4b17581a-d3ae-412f-83a9-caf1ccf8f851 · outbound

This paper cites Understanding and improving visual prompting: A label-mapping perspec- tive.

Differentiable Prompt Learning for Vision Language Models Understanding and improving visual prompting: A label-mapping perspec- tive

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.316752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ec158d87-8400-400b-acc1-7d52185d1287 · outbound

This paper cites Describing textures in the wild.

Differentiable Prompt Learning for Vision Language Models Describing textures in the wild

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.284620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation dba4fc56-8ba7-4f98-9894-ed6792f955c5 · outbound

This paper cites An overview of bilevel optimization.

Differentiable Prompt Learning for Vision Language Models An overview of bilevel optimization

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.267240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6aa38f07-c040-477e-a625-24dec8ce7d9d · outbound

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

Differentiable Prompt Learning for Vision Language Models Imagenet: A large-scale hierarchical image database

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:51.981311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d403fa01-0997-4058-a5df-1a232fb7a343 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Differentiable Prompt Learning for Vision Language Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:51.993547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6d5ef96-b713-47f6-9a7c-c707764fe2ee · outbound

This paper cites Learning generative visual models from few train- ing examples: An incremental bayesian approach tested on 101 object categories.

Differentiable Prompt Learning for Vision Language Models Learning generative visual models from few train- ing examples: An incremental bayesian approach tested on 101 object categories

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.202377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4f1f59bd-16b0-4a75-a51f-0b93f455e5ae · outbound

This paper cites BERTese: Learning to Speak to BERT.

Differentiable Prompt Learning for Vision Language Models BERTese: Learning to Speak to BERT

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:54:52.565408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1821ac28-68f1-4030-94b5-8b31dbb4ba29 · outbound

This paper cites Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.

Differentiable Prompt Learning for Vision Language Models Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.023475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4ca9f23-d258-4b83-8a54-9b217084f166 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Differentiable Prompt Learning for Vision Language Models Distilling the Knowledge in a Neural Network

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.028153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7db54a0-e17f-4633-aed9-7bf3bb084a00 · outbound

This paper cites Visual prompt tuning.

Differentiable Prompt Learning for Vision Language Models Visual prompt tuning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.039555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66c2265a-512d-4d10-acd1-d7c8150ec422 · outbound

This paper cites How can we know what language models know? Transactions of the Association for Com- putational Linguistics, 8:423–438,.

Differentiable Prompt Learning for Vision Language Models How can we know what language models know? Transactions of the Association for Com- putational Linguistics, 8:423–438,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.133142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 65b66052-e51c-40a5-a7dd-86eba4f22c3f · outbound

This paper cites Less-forgetting Learning in Deep Neural Networks.

Differentiable Prompt Learning for Vision Language Models Less-forgetting Learning in Deep Neural Networks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.048021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f607f94-f3d4-4e18-8f34-cda67d81b86d · outbound

This paper cites Maple: Multi-modal prompt learn- ing.

Differentiable Prompt Learning for Vision Language Models Maple: Multi-modal prompt learn- ing

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.116226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ea3f00dc-b283-4e47-84cf-e2ae9ba6340b · outbound

This paper cites Wilds: A benchmark of in-the-wild distribution shifts.

Differentiable Prompt Learning for Vision Language Models Wilds: A benchmark of in-the-wild distribution shifts

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.089633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 01c29e02-5cd5-46bf-ba44-d003fec84068 · outbound

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

Differentiable Prompt Learning for Vision Language Models 3d object representations for fine- grained categorization

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.060585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdd065aa-f906-4598-abbc-84323ca40830 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Differentiable Prompt Learning for Vision Language Models The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.068238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3b54e4c-c246-4084-aa57-eace6bd69f6b · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Differentiable Prompt Learning for Vision Language Models Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.072330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8edf096-0d6c-47b8-bc58-1620a7d92906 · outbound

This paper cites DARTS+: Improved Differentiable Architecture Search with Early Stopping.

Differentiable Prompt Learning for Vision Language Models DARTS+: Improved Differentiable Architecture Search with Early Stopping

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.076803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b98e668a-0713-433e-b6ac-a2ad2a3b3e9e · outbound

This paper cites DARTS: Differentiable Architecture Search.

Differentiable Prompt Learning for Vision Language Models DARTS: Differentiable Architecture Search

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.080866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 986378c6-4c94-4366-b2fb-219c43dc95e7 · outbound

This paper cites P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks.

Differentiable Prompt Learning for Vision Language Models P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.088349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88961cd2-89aa-425e-8dbb-11c9c1e7e7df · outbound

This paper cites Gpt understands, too.

Differentiable Prompt Learning for Vision Language Models Gpt understands, too

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.053820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6e53b338-8d20-4eef-abff-d081c0212bc0 · outbound

This paper cites On Surgical Fine-tuning for Language Encoders.

Differentiable Prompt Learning for Vision Language Models On Surgical Fine-tuning for Language Encoders

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:54:52.419208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3eb165ea-3e03-4a89-845e-0173ea03428a · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

Differentiable Prompt Learning for Vision Language Models Fine-Grained Visual Classification of Aircraft

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.105386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ff9715f-798d-4f48-b967-c3f40406ba82 · outbound

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

Differentiable Prompt Learning for Vision Language Models Automated flower classification over a large number of classes

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.036941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 99102c1b-74ad-406a-b303-d36ec01423a5 · outbound

This paper cites Task-specific skill localization in fine-tuned language models.

Differentiable Prompt Learning for Vision Language Models Task-specific skill localization in fine-tuned language models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:52.993885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 56f901aa-054a-4fb5-a923-b1bac98bbc2e · outbound

This paper cites Cats and dogs.

Differentiable Prompt Learning for Vision Language Models Cats and dogs

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:52.962071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4559d784-72d0-44f9-8d7a-d6312cf10c2c · outbound

This paper cites Efficient neural architecture search via parameters sharing.

Differentiable Prompt Learning for Vision Language Models Efficient neural architecture search via parameters sharing

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:52.922031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1d9027e5-661c-422f-9bd2-a42073b5a744 · outbound

This paper cites Learning transferable visual models from nat- ural language supervision.

Differentiable Prompt Learning for Vision Language Models Learning transferable visual models from nat- ural language supervision

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.139942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 093bdcc2-03b9-475c-b25d-cdb190a019fb · outbound

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

Differentiable Prompt Learning for Vision Language Models Do imagenet classi- fiers generalize to imagenet? In International conference on machine learning, pages 5389–5400

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.143824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d604b531-3019-4dee-86ee-8e695379e137 · outbound

This paper cites Au- tolr: Layer-wise pruning and auto-tuning of learning rates in fine-tuning of deep networks.

Differentiable Prompt Learning for Vision Language Models Au- tolr: Layer-wise pruning and auto-tuning of learning rates in fine-tuning of deep networks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:52.869626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 08c40e91-4e1e-4d4b-b1fb-0ae1b071f014 · outbound

This paper cites Targeted transfer learning to improve performance in small medical physics datasets.

Differentiable Prompt Learning for Vision Language Models Targeted transfer learning to improve performance in small medical physics datasets

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:52.850846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8e2a26e3-99f7-44e5-b901-daca902acfee · outbound

This paper cites The earth mover’s distance as a met- ric for image retrieval.

Differentiable Prompt Learning for Vision Language Models The earth mover’s distance as a met- ric for image retrieval

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:52.829764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 252de4ad-8403-4f1c-afc8-4291f439b7f3 · outbound

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

Differentiable Prompt Learning for Vision Language Models Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.172960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73dd840a-7d69-43c1-ad0e-98c7da14b22b · outbound

This paper cites It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners.

Differentiable Prompt Learning for Vision Language Models It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.177524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44f41f55-09a1-44a0-bc01-b312f4f061c2 · outbound

This paper cites Few-shot text generation with natural language instructions.

Differentiable Prompt Learning for Vision Language Models Few-shot text generation with natural language instructions

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:52.789874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cc2cbead-238a-4813-bde8-5e0054bde652 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

Differentiable Prompt Learning for Vision Language Models Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.186339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66e8d0db-c1d1-4ef9-9655-fc59d4f029a2 · outbound

This paper cites AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts.

Differentiable Prompt Learning for Vision Language Models AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.193849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 718608fe-216e-4636-b065-e62228b800e8 · outbound

This paper cites What does clip know about a red circle? visual prompt engineering for vlms.

Differentiable Prompt Learning for Vision Language Models What does clip know about a red circle? visual prompt engineering for vlms

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:52.747612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 82b4ce41-4a73-435a-b5fb-fdff61ed1942 · outbound

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

Differentiable Prompt Learning for Vision Language Models UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.201882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51af25a9-8ac8-45bb-a984-139734b91243 · outbound

This paper cites An Empirical Study of Example Forgetting during Deep Neural Network Learning.

Differentiable Prompt Learning for Vision Language Models An Empirical Study of Example Forgetting during Deep Neural Network Learning

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.205852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4479f38-2e3c-4314-811c-adefda872571 · outbound

This paper cites Convolutional visual prompt for robust visual perception.

Differentiable Prompt Learning for Vision Language Models Convolutional visual prompt for robust visual perception

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:52.729550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d668aa96-f39d-4f7e-ae9a-5399d2feba98 · outbound

This paper cites Attention is all you need.

Differentiable Prompt Learning for Vision Language Models Attention is all you need

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.215376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08371d3a-bc88-444a-87fb-893478d79291 · outbound

This paper cites Advances and challenges in meta- learning: A technical review.

Differentiable Prompt Learning for Vision Language Models Advances and challenges in meta- learning: A technical review

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:52.703282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bca7125c-0872-47b6-8948-d06c9bf6c89b · outbound

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

Differentiable Prompt Learning for Vision Language Models Sun database: Large-scale scene recognition from abbey to zoo

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:52.686315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 45e9c61c-255b-4bf1-84bc-6ac8ac38c202 · outbound

This paper cites Zeronas: Differentiable generative adversarial networks search for zero-shot learning.

Differentiable Prompt Learning for Vision Language Models Zeronas: Differentiable generative adversarial networks search for zero-shot learning

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:52.673591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bd62be96-d776-4c02-9fe7-8644d2e43f5f · outbound

This paper cites Improving vi- sual prompt tuning for self-supervised vision transformers.

Differentiable Prompt Learning for Vision Language Models Improving vi- sual prompt tuning for self-supervised vision transformers

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:52.659652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d83aaa4e-80f9-4c28-a96b-cb41c20b476a · outbound

This paper cites Unified Vision and Language Prompt Learning.

Differentiable Prompt Learning for Vision Language Models Unified Vision and Language Prompt Learning

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.244277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97994ee4-27ae-4847-bd6c-c51285cca764 · outbound

This paper cites Understanding and Robustifying Differentiable Architecture Search.

Differentiable Prompt Learning for Vision Language Models Understanding and Robustifying Differentiable Architecture Search

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.248431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dac48d9d-1fba-40de-9bc0-c3d6d127a116 · outbound

This paper cites Distilling BERT into Simple Neural Networks with Unlabeled Transfer Data.

Differentiable Prompt Learning for Vision Language Models Distilling BERT into Simple Neural Networks with Unlabeled Transfer Data

Reference 1781

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.109744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4e3795d-fd5f-4bed-8fc7-5e0f4c216c09 · outbound

This paper cites Visual prompt- ing via image inpainting.

Differentiable Prompt Learning for Vision Language Models Visual prompt- ing via image inpainting

Reference 1992

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.393036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 601ee261-4d92-46ac-95d2-38a40439b58f · outbound

This paper cites Convolutional neural fabrics.

Differentiable Prompt Learning for Vision Language Models Convolutional neural fabrics

Reference 2000

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:52.811744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9207e045-38e5-47ef-97a4-87cc55f78ae0 · outbound

This paper cites Making Pre-trained Language Models Better Few-shot Learners.

Differentiable Prompt Learning for Vision Language Models Making Pre-trained Language Models Better Few-shot Learners

Reference 2004

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.005058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ed6334c-5880-4106-a339-ce61894782d7 · outbound

This paper cites Probing representation forgetting in supervised and un- supervised continual learning.

Differentiable Prompt Learning for Vision Language Models Probing representation forgetting in supervised and un- supervised continual learning

Reference 2007

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.247073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 099460eb-283d-4aee-9ea0-68657a123fa1 · outbound

This paper cites Efficient test-time model adaptation without forgetting.

Differentiable Prompt Learning for Vision Language Models Efficient test-time model adaptation without forgetting

Reference 2008

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.118784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e119f8e-b0f8-4765-a1bc-33bf7d0af392 · outbound

This paper cites Search- ing for a robust neural architecture in four gpu hours.

Differentiable Prompt Learning for Vision Language Models Search- ing for a robust neural architecture in four gpu hours

Reference 2009

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.218907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ad25701e-d814-4f79-89bc-909c49178261 · outbound

This paper cites PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search.

Differentiable Prompt Learning for Vision Language Models PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search

Reference 2010

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.231335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b655e70-6e74-4531-9e03-65d027b71196 · outbound

This paper cites Com- putational optimal transport: With applications to data sci- ence.

Differentiable Prompt Learning for Vision Language Models Com- putational optimal transport: With applications to data sci- ence

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.131087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62b0c98b-1e7b-49a3-a69e-2b2d5b3a080e · outbound

This paper cites Surgical Fine-Tuning Improves Adaptation to Distribution Shifts.

Differentiable Prompt Learning for Vision Language Models Surgical Fine-Tuning Improves Adaptation to Distribution Shifts

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.064110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3134ef2-c44e-4b1c-ab56-10d674ec0153 · outbound

This paper cites Language models are few-shot learners.

Differentiable Prompt Learning for Vision Language Models Language models are few-shot learners

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:51.945435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c0bb6df-e69e-4afd-ae57-936176f3971d · outbound

This paper cites Prompt learning with one-shot setting based feature space analy- sis in vision-and-language models.

Differentiable Prompt Learning for Vision Language Models Prompt learning with one-shot setting based feature space analy- sis in vision-and-language models

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.159613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6f7a7b4c-4591-4ee6-b954-a95226c4b78f · outbound

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

Differentiable Prompt Learning for Vision Language Models Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.174930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 71211da6-5a8f-4be1-82cb-d38537c69617 · outbound

This paper cites Partial is better than all: Revisiting fine-tuning strategy for few-shot learn- ing.

Differentiable Prompt Learning for Vision Language Models Partial is better than all: Revisiting fine-tuning strategy for few-shot learn- ing

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:52.762538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 93701892-dec8-4732-ab21-378e9727efcc · outbound

This paper cites Transfer without forgetting.

Differentiable Prompt Learning for Vision Language Models Transfer without forgetting

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.367316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4a98b662-ebb9-413b-a4db-4f9aff6f255e · outbound

This paper cites PLOT: Prompt Learning with Optimal Transport for Vision-Language Models.

Differentiable Prompt Learning for Vision Language Models PLOT: Prompt Learning with Optimal Transport for Vision-Language Models

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:51.953413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbb78763-4dfb-4c18-8156-cea4cb6a4800 · outbound

This paper cites Catastrophic for- getting meets negative transfer: Batch spectral shrinkage for safe transfer learning.

Differentiable Prompt Learning for Vision Language Models Catastrophic for- getting meets negative transfer: Batch spectral shrinkage for safe transfer learning

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.330570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ba189604-37c9-488a-88ab-cee672476ba6 · outbound

This paper cites Deep residual learning for image recog- nition.

Differentiable Prompt Learning for Vision Language Models Deep residual learning for image recog- nition

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.015111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87e20199-99be-49a8-9d06-f9d52a86cef9 · outbound

This paper cites Un- derstanding and simplifying one-shot architecture search.

Differentiable Prompt Learning for Vision Language Models Un- derstanding and simplifying one-shot architecture search

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.379834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f583fbcf-4da3-443e-8195-c9cdf9975003 · outbound

This paper cites Fair darts: Eliminating unfair advantages in differentiable architecture search.

Differentiable Prompt Learning for Vision Language Models Fair darts: Eliminating unfair advantages in differentiable architecture search

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:53.303495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1288ee13-8f56-49e1-8446-b072a0d6ace2 · outbound

This paper cites Universal Language Model Fine-tuning for Text Classification.

Differentiable Prompt Learning for Vision Language Models Universal Language Model Fine-tuning for Text Classification

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-10T22:54:52.035696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers