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Paper Citation Record · LEDGER

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning

As of 8 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2506.10575.

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

pith.paper-citation-record.v1
2506.10575 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:29:26.022916Z

measured 57 of 57 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

57 of 57 outbound references displayed

  • verified exact6
  • verified fuzzy29
  • unresolved22
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 24aece88-8079-4a30-ba7c-32df2a373b82 · outbound

This paper cites Flamingo: a visual language model for few- shot learning.Advances in Neural Information Processing Systems, 35:23716–23736, 2022.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Flamingo: a visual language model for few- shot learning.Advances in Neural Information Processing Systems, 35:23716–23736, 2022

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:33.806086Z

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=2026-08-07T04:29:20.912833Z digest=sha256:259e437ba4365b924afc965cdca711ac8f30cc67eab16ab4d3750a1e8fdb9af0

Observation 1552687b-5962-46d6-aecc-2cb2e60d956b · outbound

This paper cites Laso: Label-set operations networks for multi-label few-shot learning.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Laso: Label-set operations networks for multi-label few-shot learning

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:33.647913Z

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=2026-08-07T04:29:20.985698Z digest=sha256:50ade8483daf52d21ceb62f496e0d4d90dcc8b1108b7bff1cb8f919919d3ff4f

Observation 5efae362-3a40-4e91-b222-daec85b6a6d3 · outbound

This paper cites Structured semantic transfer for multi-label recognition with partial labels.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Structured semantic transfer for multi-label recognition with partial labels

Reference 3

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verified fuzzy
raw_fallback, observed 2026-08-07T04:29:33.518040Z

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=2026-08-07T04:29:21.065735Z digest=sha256:6bf52f0b6b171bef9dd0374dce6b2cad451bfb0e6d60f876f3fe9a26b8e39b19

Observation a6051b62-25ce-4961-a61e-9d26f9b257e2 · outbound

This paper cites Recurrent attentional reinforcement learning for multi-label image recognition.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Recurrent attentional reinforcement learning for multi-label image recognition

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-07T04:29:33.309046Z

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=2026-08-07T04:29:21.140117Z digest=sha256:4e50580cd2b7c286d7b94fd1936e23cb14f240612587333e848a58f8eeff8529

Observation 048f16a2-88e9-4802-8a94-4b8d63e5d365 · outbound

This paper cites Learning semantic-specific graph representation for multi-label image recognition.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Learning semantic-specific graph representation for multi-label image recognition

Reference 6

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verified fuzzy
raw_fallback, observed 2026-08-07T04:29:33.067742Z

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=2026-08-07T04:29:21.302799Z digest=sha256:4d54708bab74f5f3814999be1497485ed8c9c9babf4ef1a996730be2334787ce

Observation 39eed48b-0cdf-4cb1-824d-24101b2bda98 · outbound

This paper cites Multi- label image recognition with graph convolutional networks.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Multi- label image recognition with graph convolutional networks

Reference 7

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verified fuzzy
raw_fallback, observed 2026-08-07T04:29:32.749727Z

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=2026-08-07T04:29:21.394607Z digest=sha256:9873703e698969f0ae329e4d5d6e97c1ade30e611d4affaa0c4e9d7d4e7f14be

Observation 235e497b-5f42-4b70-a15f-61e19b4031f5 · outbound

This paper cites Stargan v2: Diverse image synthesis for multiple domains.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Stargan v2: Diverse image synthesis for multiple domains

Reference 8

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no resolver link, observed 2026-08-07T04:29:21.467483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:21.467483Z digest=sha256:c8d8bd2f21edee10606e7cb4449877c1979c18ebf0ebda9165cea543afd47783

Observation 5945c96b-80b9-4135-8f0b-7f51c38f2fa0 · outbound

This paper cites Nus-wide: a real-world web image database from national university of singapore.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Nus-wide: a real-world web image database from national university of singapore

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:32.482326Z

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=2026-08-07T04:29:21.545489Z digest=sha256:504d0bf1d30129e7ba5a6981719e44256fc27ca82500131d922696d922d17426

Observation e41dbda1-01c0-46d6-9b8d-a374b62d3df8 · outbound

This paper cites Bayesian Prompt Learning for Image-Language Model Generalization.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Bayesian Prompt Learning for Image-Language Model Generalization

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-08-07T04:29:27.519841Z

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=2026-08-07T04:29:21.628262Z digest=sha256:846752be8453d3e0b68c2a5323a1b62206dc2777a3362c7d98953a4c0aeb07dc

Observation 422915f3-7be1-4344-8f8b-0645292626ed · outbound

This paper cites Learning a deep convnet for multi-label classification with partial labels.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Learning a deep convnet for multi-label classification with partial labels

Reference 11

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verified fuzzy
raw_fallback, observed 2026-08-07T04:29:32.223302Z

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=2026-08-07T04:29:21.708820Z digest=sha256:5055b7e175074e35a9800e4fd388840473fd9619722a3fb4614a96103b34c68f

Observation 1dea58b2-387b-4247-b1cb-07baa7981e5a · outbound

This paper cites The pascal visual object classes (voc) challenge.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning The pascal visual object classes (voc) challenge

Reference 12

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verified fuzzy
raw_fallback, observed 2026-08-07T04:29:32.110623Z

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=2026-08-07T04:29:21.758256Z digest=sha256:eb7d93636488ab1efdabcafdc0942e151f231c513d487265f90f8ad080b1b9c8

Observation df16fc1f-6483-4425-bf26-883dbb59b702 · outbound

This paper cites Learning federated visual prompt in null space for mri reconstruction.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Learning federated visual prompt in null space for mri reconstruction

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:31.979476Z

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=2026-08-07T04:29:21.841049Z digest=sha256:fcf591584f590a83eb7149ca74bbe61560a96ccb66a4f989d42f7b9bbb366d9c

Observation c93a5f1e-5710-4697-bdcc-ae4a8987fc02 · outbound

This paper cites Diverse data augmentation with diffusions for effective test-time prompt tuning.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Diverse data augmentation with diffusions for effective test-time prompt tuning

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:31.848906Z

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=2026-08-07T04:29:21.916666Z digest=sha256:41335eabaa20e8ba40edeb6310bb1985c2b6b8834ac4ff25dded56cc2b5138a9

Observation 1cc7885b-241b-46f2-bae0-8af644ddc3e6 · outbound

This paper cites Deep Convolutional Ranking for Multilabel Image Annotation.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Deep Convolutional Ranking for Multilabel Image Annotation

Reference 15

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no resolver link, observed 2026-08-07T04:29:22.002407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:22.002407Z digest=sha256:bce96b05366ae39f7b1be83c46e35acc4dbe4bd930c140cd553302816b8a7444

Observation 925ea13d-e7ed-4268-9490-fc6d23eebb5b · outbound

This paper cites Gener- ative adversarial networks.Communications of the ACM, 63(11):139–144, 2020.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Gener- ative adversarial networks.Communications of the ACM, 63(11):139–144, 2020

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:31.670788Z

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=2026-08-07T04:29:22.076053Z digest=sha256:ea5505fa5dd9ed5c9924c1091145bc1a9d970a2a5ae555e9aa5f9cbd44afb911

Observation a135c362-b34c-441a-b0e1-d298e75e8688 · outbound

This paper cites I Can't Believe There's No Images! Learning Visual Tasks Using only Language Supervision.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning I Can't Believe There's No Images! Learning Visual Tasks Using only Language Supervision

Reference 17

Resolution
verified exact
local_arxiv, observed 2026-08-07T04:29:27.334748Z

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=2026-08-07T04:29:22.172146Z digest=sha256:d3d2f1abb04f81977850c1580143392f133b86408ea9cb416d889870ac49801c

Observation 43de6bae-3c0d-4fa4-8064-fc9267afba04 · outbound

This paper cites Texts as Images in Prompt Tuning for Multi-Label Image Recognition.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Texts as Images in Prompt Tuning for Multi-Label Image Recognition

Reference 18

Resolution
verified exact
local_arxiv, observed 2026-08-07T04:29:27.005782Z

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=2026-08-07T04:29:22.246265Z digest=sha256:2339ae023baf82c8bdea5744eb44adee82034012bf9a1fd13b7ba5484aa1ee5d

Observation 54d438c6-cdf6-4d65-82f8-29d6c4fc8afe · outbound

This paper cites Imagen Video: High Definition Video Generation with Diffusion Models.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Imagen Video: High Definition Video Generation with Diffusion Models

Reference 19

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no resolver link, observed 2026-08-07T04:29:22.304194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:22.304194Z digest=sha256:5116494122bb8e9bf6da6000406f9cac3079bc38618ec954110edc3a63fd4092

Observation cac53a72-af1a-4bcd-8974-9ffe1b0e1fef · outbound

This paper cites Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental Learning.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental Learning

Reference 20

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no resolver link, observed 2026-08-07T04:29:22.377613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:22.377613Z digest=sha256:86cd59a6de8b518e5dbb6283bca885f0d81c3e5167b0509f32081c4cedea08ef

Observation 759e43e3-18a9-41aa-9f15-c2db223297a5 · outbound

This paper cites Class concept rep- resentation from contextual texts for training-free multi-label recognition.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Class concept rep- resentation from contextual texts for training-free multi-label recognition

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:31.536598Z

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=2026-08-07T04:29:22.437023Z digest=sha256:615d79721f187aabecdb7c25eb6209fa9e2effefa621e4f6ae75c05827864fdb

Observation 151bea06-1562-4acc-b5f9-115f8abc2da4 · outbound

This paper cites Enhancing clip conceptual embedding through knowl- edge distillation, 2024.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Enhancing clip conceptual embedding through knowl- edge distillation, 2024

Reference 22

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raw_fallback, observed 2026-08-07T04:29:31.301209Z

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=2026-08-07T04:29:22.529549Z digest=sha256:9e578791c400b6055f3539e2c1ab4650c99cf1ba696365464c7032460c374d86

Observation 99c04b5b-f88f-4d51-95c0-fcaf71f40039 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Adam: A Method for Stochastic Optimization

Reference 23

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:22.608769Z digest=sha256:d36486aafd7bd1bbdea5165ec62a8c564a7cc03be1de56d9e27329a17a7bd8df

Observation 52dbb060-3b2b-477f-a02b-be5cbc2d169c · outbound

This paper cites Auto-Encoding Variational Bayes.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Auto-Encoding Variational Bayes

Reference 24

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no resolver link, observed 2026-08-07T04:29:22.693267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:22.693267Z digest=sha256:886a179e4d23490780fa29df0882fd352afbbb1aa28ade4b824249d4aa8a983c

Observation d2f91786-85d5-485a-b6e4-ee057a6ffe95 · outbound

This paper cites Openimages: A public dataset for large-scale multi-label and multi-class image classification.Dataset available from https://github.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Openimages: A public dataset for large-scale multi-label and multi-class image classification.Dataset available from https://github

Reference 25

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verified fuzzy
raw_fallback, observed 2026-08-07T04:29:30.991687Z

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=2026-08-07T04:29:22.787514Z digest=sha256:55d4635e140d95254cf3ef44be23125de4b0e298693fd97953e74b99867a5bd2

Observation 9325cba4-ff10-48d2-a049-c1ef4caf4611 · outbound

This paper cites Microsoft coco: Common objects in context.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Microsoft coco: Common objects in context

Reference 26

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no resolver link, observed 2026-08-07T04:29:22.866684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:22.866684Z digest=sha256:67c42ba5e23a9ed5334563537eb3fec69e5052aa265a32cd551d66c194add886

Observation 8f654f19-4017-4f3f-9870-981ca8d86a5f · outbound

This paper cites Compositional visual generation with composable diffusion models.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Compositional visual generation with composable diffusion models

Reference 27

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no resolver link, observed 2026-08-07T04:29:22.937312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:22.937312Z digest=sha256:226f8630eaf066b712a71b75ece1ac9c9bb853b00597a1d82db1f5b0e1bd6235

Observation 174fc98a-68c0-4902-95e4-34ca7f7c42b2 · outbound

This paper cites Multi-label image classification via knowledge distillation from weakly-supervised detection.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Multi-label image classification via knowledge distillation from weakly-supervised detection

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:30.851628Z

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=2026-08-07T04:29:22.992006Z digest=sha256:10daf8cc6c9b24c1c950aec7669998540da1cf2e69a5d3402216c279cbd6d9b5

Observation 940ac585-f768-4090-b721-ff3dd2c0cd50 · outbound

This paper cites Decoupled Weight Decay Regularization.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Decoupled Weight Decay Regularization

Reference 29

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no resolver link, observed 2026-08-07T04:29:23.154171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:23.154171Z digest=sha256:94ef74c4a5d2e99153d484d6a33edf85fd95d3f098100cf8cfeb0fd578fdeb4b

Observation 60b3b8b3-01c9-4ce7-8665-67607d9997c3 · outbound

This paper cites T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models

Reference 30

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no resolver link, observed 2026-08-07T04:29:23.300405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:23.300405Z digest=sha256:90b3702e996964aeec18a5729d26d34abeb9bf4ea941533f8d9dd60b44c731c1

Observation e145ff17-4e59-44aa-8190-0fecda34f588 · outbound

This paper cites Discriminative region-based multi- label zero-shot learning.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Discriminative region-based multi- label zero-shot learning

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:30.640677Z

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=2026-08-07T04:29:23.497066Z digest=sha256:fffb6fe4b3fe1d2fc4a6c2907ee7411bb1daa5734f0b745c3526930411e16daf

Observation 4bd800af-6817-439b-aa85-83e068109af3 · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 32

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no resolver link, observed 2026-08-07T04:29:23.648601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:23.648601Z digest=sha256:4825950981c347e164f5cadeee3982e3c11c1bb014b78013cfbd7d0fd34ae1aa

Observation 09055feb-c934-45bb-b5b2-a5b9304eaafe · outbound

This paper cites Text-Only Training for Image Captioning using Noise-Injected CLIP.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Text-Only Training for Image Captioning using Noise-Injected CLIP

Reference 33

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no resolver link, observed 2026-08-07T04:29:23.701411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:23.701411Z digest=sha256:99cfb877f3faaa569b45d31c1b62c90791cd71b46038b7c0adfeb95f60ad2828

Observation 9c55f7d6-eb43-4fbc-9bec-9eddc3062d8f · outbound

This paper cites Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023

Reference 34

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no resolver link, observed 2026-08-07T04:29:23.757452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:23.757452Z digest=sha256:dd797a7c446f42054170eb845cf96e70f6d4911d79630205aae2573297246507

Observation ad348d85-18e1-4a2d-8f9e-f92ce3949f50 · outbound

This paper cites Semantic-aware representation blending for multi-label image recognition with partial labels.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Semantic-aware representation blending for multi-label image recognition with partial labels

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:30.451465Z

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=2026-08-07T04:29:23.827478Z digest=sha256:96b19751b4e6e5aeb9181ef0d59732f0e175a475738eab1c2fe5336c960b0ce1

Observation c98aeb9a-3e22-4866-98ec-8e79f405e115 · outbound

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

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Learning transferable visual models from natural language supervision

Reference 36

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no resolver link, observed 2026-08-07T04:29:23.905795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:23.905795Z digest=sha256:4bd7ee0392a44da51ca934465d41c8cf4db08e62d3528aedb1acc957314abcd2

Observation 93cbaf98-1dac-440e-8fe9-4168ea114428 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 37

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no resolver link, observed 2026-08-07T04:29:23.971618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:23.971618Z digest=sha256:64014ef319f76b6a9f098b1313d4cf356f2e9867204e26cc6a392a8440e83882

Observation 67c2f28a-8042-4ba4-83f8-7b31737b9bfc · outbound

This paper cites Rethinking Prompting Strategies for Multi-Label Recognition with Partial Annotations.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Rethinking Prompting Strategies for Multi-Label Recognition with Partial Annotations

Reference 38

Resolution
verified exact
local_arxiv, observed 2026-08-07T04:29:26.741089Z

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=2026-08-07T04:29:24.067977Z digest=sha256:391de149b27de2ccdbe9fec093888bd50f10c2fe0fc985a1f23549ebdcf236d3

Observation 3e5370b9-3067-4406-b06a-7fa346309749 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning High-resolution image synthesis with latent diffusion models

Reference 39

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no resolver link, observed 2026-08-07T04:29:24.140777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:24.140777Z digest=sha256:c8923db1a8f2224af952519222eac050a778d1225b9e8cef24a508706b457b00

Observation 6e4f146f-2784-4af2-8e8a-503d755e9b84 · outbound

This paper cites DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T04:29:24.201454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:24.201454Z digest=sha256:86756b8a7e495da1c68213286abf268b979b6b42da84c9e33f257a6e6cd40906

Observation 4f3c9b3c-79d2-489f-ab99-da2bca4e214d · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.Advances in Neural Information Processing Systems, 35:36479–36494, 2022.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Photorealistic text-to-image diffusion models with deep language understanding.Advances in Neural Information Processing Systems, 35:36479–36494, 2022

Reference 41

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unresolved
no resolver link, observed 2026-08-07T04:29:24.328312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:24.328312Z digest=sha256:c9ced0db2e8c14ac04ea432efdb7f0778a021567bf2607b2e17d6f00222fc98b

Observation b82ffa68-b37d-4f30-be73-b103de74c0ae · outbound

This paper cites Meta-learning for multi-label few-shot classification.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Meta-learning for multi-label few-shot classification

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:30.155597Z

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=2026-08-07T04:29:24.397782Z digest=sha256:7da14f741dc62ad14503077fc13c44b7aeba4cc2af0ed079f959d36988820b39

Observation 92e242d5-05a3-4554-919b-970c885771cd · outbound

This paper cites D2c: Diffusion-decoding models for few-shot conditional generation.Advances in Neural Information Processing Systems, 34:12533–12548, 2021.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning D2c: Diffusion-decoding models for few-shot conditional generation.Advances in Neural Information Processing Systems, 34:12533–12548, 2021

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:29.964146Z

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=2026-08-07T04:29:24.456552Z digest=sha256:0b2faf325daeb7f1708b5d53edb431edef2c9fc467966f9a0e40714dcfd7a025

Observation 2bce9440-2692-46ef-9e32-e27523688dcd · outbound

This paper cites DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations

Reference 44

Resolution
verified exact
local_arxiv, observed 2026-08-07T04:29:26.503407Z

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=2026-08-07T04:29:24.565152Z digest=sha256:a0c191fd20ced6189eb0e1bae7a0bdd68d03e984fb63b6e5df04ba14d66bc1b5

Observation 6fdb1315-0afe-44f3-9465-a167a5c433b5 · outbound

This paper cites Vl-adapter: Parameter- efficient transfer learning for vision-and-language tasks.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Vl-adapter: Parameter- efficient transfer learning for vision-and-language tasks

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:29.756867Z

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=2026-08-07T04:29:24.669507Z digest=sha256:f8e6829cbec228a1e7b60f3d092e939ca7879bed77cfeb9fdb29288be3242908

Observation af3ca6be-ea1e-4188-859c-f811ac3a0582 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning LLaMA: Open and Efficient Foundation Language Models

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-07T04:29:24.742090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:24.742090Z digest=sha256:ce656e7cc0fe201e6c5cc1984ad4deaaa2e9158512b71075d3bbe6b824ae8d30

Observation 69079c93-4405-4a67-b3dd-facb53320b39 · outbound

This paper cites Schwing, and Heng Ji.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Schwing, and Heng Ji

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:29.467747Z

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=2026-08-07T04:29:24.822899Z digest=sha256:6e49f5df5771d73f430dba45e00aa05574db5738edf2dd9073b35d168bb51554

Observation 22bd1813-84d4-49b2-b047-f7026cd06b86 · outbound

This paper cites Cnn-rnn: A unified framework for multi-label image classification.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Cnn-rnn: A unified framework for multi-label image classification

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:29.250117Z

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=2026-08-07T04:29:24.906797Z digest=sha256:d02381ee5a459e18dfccb49976df5a242570b5858823503d762c80d91114fe82

Observation 174fdf45-c604-41ac-b7e2-25289eee0a40 · outbound

This paper cites Beyond object proposals: Random crop pooling for multi-label image recognition.IEEE Transactions on Image Processing, 25(12):5678–5688, 2016.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Beyond object proposals: Random crop pooling for multi-label image recognition.IEEE Transactions on Image Processing, 25(12):5678–5688, 2016

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:29.090608Z

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=2026-08-07T04:29:25.011815Z digest=sha256:c44ec5599b89f2782e57b68692863fab1dfb03ca29047ec9b523bc99bc89ca3e

Observation dce809da-2730-4619-affa-4980c9be33dd · outbound

This paper cites Multi-label classification with label graph superimposing.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Multi-label classification with label graph superimposing

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:28.742045Z

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=2026-08-07T04:29:25.068637Z digest=sha256:7490b9a4d22ee03e71d30ce11b5790f8155bd641bc37a06e7c67b4eb5cc77099

Observation 48618230-4984-4bd0-acf7-75eadd39a688 · outbound

This paper cites Multi-label image recognition by recurrently discovering attentional regions.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Multi-label image recognition by recurrently discovering attentional regions

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:28.509634Z

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=2026-08-07T04:29:25.215372Z digest=sha256:f1a6ec244220dfec0eaccdbb925def7727759a7632de356b12acf41925235c8c

Observation fb074065-6d3a-4991-9b4d-40e4d1514758 · outbound

This paper cites TAI++: Text as Image for Multi-Label Image Classification by Co-Learning Transferable Prompt.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning TAI++: Text as Image for Multi-Label Image Classification by Co-Learning Transferable Prompt

Reference 52

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no resolver link, observed 2026-08-07T04:29:25.323228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:25.323228Z digest=sha256:f284fbd082c42e4a1266747a91d064a37659133d3f14d19e42f539f16a8ab7e9

Observation dbdbd1f7-a38c-4168-9111-1df0ebb44a80 · outbound

This paper cites Orderless recurrent models for multi-label classification.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Orderless recurrent models for multi-label classification

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:28.291057Z

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=2026-08-07T04:29:25.508076Z digest=sha256:6acadf510330c2cb018a4b8085f765b90a81948c1d970ffaf42fc36062eb3055

Observation 7ca2c462-991a-4087-9e94-b6d750d30dbb · outbound

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

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Tip-adapter: Training-free adaption of clip for few-shot classification

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:28.026308Z

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=2026-08-07T04:29:25.657227Z digest=sha256:78ea91fca84942b1fb9cac1b01ef5fafc519dcd8cd89daa6183c60998d2f819e

Observation 34f917ba-feac-407e-b5f9-f4cd954240b7 · outbound

This paper cites Transformer-based dual relation graph for multi-label image recognition.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Transformer-based dual relation graph for multi-label image recognition

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:29:27.754042Z

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=2026-08-07T04:29:25.787028Z digest=sha256:236f23b1efa5b1452221ee9c20734f748af2bd4007256270b052fc380e9905c3

Observation 786f5b84-e50e-41b7-84be-a94a0819c3be · outbound

This paper cites Con- ditional prompt learning for vision-language models.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Con- ditional prompt learning for vision-language models

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-07T04:29:25.856436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:25.856436Z digest=sha256:ac841c3d2765755c7758e650dedf67fc0b16b99ba6efeeec89484754cd6cf5b1

Observation 2320c6c6-4efe-499d-b709-e6fc9b5d7e67 · outbound

This paper cites Learning to prompt for vision-language models.International Journal of Computer Vision, 130(9):2337–2348, 2022.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Learning to prompt for vision-language models.International Journal of Computer Vision, 130(9):2337–2348, 2022

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-07T04:29:25.936192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:25.936192Z digest=sha256:d18fc48c9cab813e6adb273aa6eba7dbc28b688e81d5dec524937399bd039404

Observation c164c584-c577-4927-b2c5-dd84080dafe2 · outbound

This paper cites Prompt-aligned Gradient for Prompt Tuning.

Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning Prompt-aligned Gradient for Prompt Tuning

Reference 58

Resolution
verified exact
local_arxiv, observed 2026-08-07T04:29:26.267957Z

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=2026-08-07T04:29:26.022916Z digest=sha256:7802f75b954d39a8a71c45331595ca15f32eaded8689d9e9df099a3b48c863d2

Pith citing papers

No inbound Pith citation observations are available.