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Paper Citation Record · LEDGER

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

As of 19 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 2026-08-19T06:32:44.657259+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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:20.912833Z digest=sha256:66c17a8e57477fb9d828bd37de1aa85ddeaafc0a91839f57491a6d21ad1ed06b

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:20.985698Z digest=sha256:289bf5af7eb51378957e9715ec4f8ef72d55a3f4015feed8402a4717b87ce761

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:21.065735Z digest=sha256:668c3b32dd45f2e0ddfc8c972cba54e7776acfae21735c0d4a9f941e49cc1be2

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:21.140117Z digest=sha256:9ae22cbdf26e5e97b1064eb953b04ad6ebd8360e76a6d2b88474965c564b569f

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:21.302799Z digest=sha256:5fba7e19a6d16a7d51f85f13f5b9bac075781ed2066573727fbbae3cd2393f32

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:21.394607Z digest=sha256:ccb29055e3325f82c0e91271531b7c8ebfb71b9f5072a00d3d4cfd593e0ce031

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=sha256:44ecee8eb70592616f9bc1ed6cc054de828ed2b2e6767dae889417408abe9d76

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:21.545489Z digest=sha256:7ca5073e67fd34a7eb3edc76a1fdfc9cc97b48f12a1edea84ee9258c370b7daf

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:21.628262Z digest=sha256:3bd510cb80ca15f762a78458f68875855aeb22c118f46927b686538522d354d8

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:21.708820Z digest=sha256:a8f6973120d5e5d7908217413b65b7ce75a598c3c7a35015f654e8616b311e5a

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

Resolution
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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:21.758256Z digest=sha256:e9d73a58781cfdc77fd9bf2f8b47ad44282eb6d453088f65260b524eae551214

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:21.841049Z digest=sha256:c18ba4a33bbb4a31e134bac68cc08cd83e094fc5bfcaa35e2f33240085939ad4

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:21.916666Z digest=sha256:50cdeaae2ea0e3dbc5f02d0810636d357583269b5e81f1344357f5ae670071cf

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:22.002407Z digest=sha256:bb14aaa8aee36041a9318896c05d8d6b884cc433d2919a6c55a8da448a3dcb80

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:22.076053Z digest=sha256:95db5acf081dbc869cc0c80583972a33e2a679e173f49713b1dc426d18da57bf

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:22.172146Z digest=sha256:e5652e5217cc2654817134d09fa3aa99b6c452419ba27b0b174cba1f4fb2eeb2

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:22.246265Z digest=sha256:5c4e95fc74bcd1170ff062aa1b5e7139815472dfae19eea30b342aae9b2b38d9

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:22.304194Z digest=sha256:39d326ac7852558b5319fe469020e630a9ebe7c13bbcaf1aaddc72c1bc1de4c4

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:22.377613Z digest=sha256:b9067fbcbe134523662295131ff38b82761cc35cba714998dfc4615adcf80560

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:22.437023Z digest=sha256:ab4df4c9228e736fc29e34498de0dd81194966cedd0d21ad787d0cac2caeb41f

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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verified fuzzy
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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:22.529549Z digest=sha256:f9ee05477fb753a0eed6879b667495e6a69787be73c0b2c278782f36f05497eb

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=sha256:7aa7096087d4290905073016f0ba7f69e8e8cd095d6ef77a0ec7f78947ea1922

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=sha256:66fd80449e9775a065c319b68e934b529fa187f6d42e92e3f87b4448e99ccc13

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:22.787514Z digest=sha256:f4c7396f09af769e5b94dbf8a0e0382faed793d7da15b23dfd5a8485a659e037

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:eb281dd36cccecd4b95fd8f071fcbc1f623d63bf2b1c7f1b6d97feec7b72ee75

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=sha256:8c1f2512b66e733ae0405d051b764c2494cdc0b1d292da0e38e067252ee09fe1

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:22.992006Z digest=sha256:b8dbf98e3ff9599703139920a44f61ec13f89469562d88a9cdceed21606bafdc

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:c19090b0f7418021c2e5e10ecc33d8de785c11796087557111983a49263c4915

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=sha256:8c2c6f35758ea6199f8d103b8668c2108bf7530cb829ebf96c9c13ffff8b4eed

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:23.497066Z digest=sha256:cf2a498ec1bb8d613161bd4b62f1ab5619dd6c5279e69b5fb09786b4bf8a16dd

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:ab6d98bc74496c51d1dfa9e5cf3de4d4c03381cd7cc6c5d064aa88c264e40f38

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:29:23.701411Z digest=sha256:dd31dda39f2ee819bc6e5fe1f123ee1d6acb01ac4fae5a60eff7e29c44329f93

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=sha256:3f8d916918e37f440a0d52c99c8754733e6661280147b510ce7f5dd3a534e673

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:23.827478Z digest=sha256:4a3bf255c779a43f424689e3ad484045c3f944e1b4f3119f9a0683fd1a92ee5d

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:c2a5aee5f8a12e038f3bcae7330d0d49dccc6122c4bb2e529a9d190ca99ddb59

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=sha256:1609532f6df18874e7f3639951c20a85db489f22875fbb5b8feb43cdb343cba5

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:24.067977Z digest=sha256:563215d4e653d024b1ffe57447c6440374fa4b94301874442bed619b6ba35ccd

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

Resolution
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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=sha256:8de84447f1242c19b3ea934ac2b334504e3e8e4f982088cb96cb5ba3aa0f25c6

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=sha256:99ac30926ddd5787b289266cbe4df7d9506a4fb2b6968eada95e103698b25908

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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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=sha256:14876294e7ea867657ad01d989a843a8c6eb2e1e7d10f17da1b3a02c8b951d7a

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:24.397782Z digest=sha256:07c9650dcd50360c614a4d8c74d5d664782b5c0dd5f65139d1aff9cec28b73bd

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:24.456552Z digest=sha256:4c2ad7cf58d10e0a8a4393954fba438b32d4f64aa7644c04116486b6e460aad9

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:24.565152Z digest=sha256:0894d9e8b310c32dc066b4cf7b8ea52ea7517518580c8bce30e87c554b7ce35f

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:24.669507Z digest=sha256:df11430bb2270e53abe1c5fe855d8dea796be72ff207851a8b9d4054366b6ff8

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

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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=sha256:609490c6c33d42cf176b3458ddcda9116219252f90ef10cde585f2c519954b24

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:24.822899Z digest=sha256:19a8876c24d5e90fd2422c797e9319c14df6e9e6f1df28393591ed58877e47af

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:24.906797Z digest=sha256:ca7171a32e7ef80c10620490d6805199b682e398b8f005fb1d557fd2ec9bb076

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:25.011815Z digest=sha256:8d2a518f42e155814fa1fe157fd6668cd3541d31a3c7e812aa7aadefd282a1d1

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:25.068637Z digest=sha256:b778f46f834fcf3bed1a11f6f92acb35437e3c6214dfd04e12023290d94ab1e2

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:25.215372Z digest=sha256:673b396265ae2f8e2c1b8113bc256b6f91845dd827067cd1118e3ebe497535dd

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=sha256:2a1e7d9eb1ef56b2ae055560c858e769ac186d3885f3399598529f055f5a2c50

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:25.508076Z digest=sha256:fc2d3f6fe7d9af573ed000849298dc58748169ea506d3e452d43cb4a43586307

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:25.657227Z digest=sha256:78fa2f19e37c673333e649b73e125bbca8de72bcdc89068b685dc52136d8410d

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:25.787028Z digest=sha256:a6011fef0bb5b3b1e4f5e356a08927438cd75a8981e0ea8c6f46c09a57cbf33d

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=sha256:1d98910e4e15775d5dafa1276feefe71e7a28d9451440441b1a60257b211ce02

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=sha256:2893c109501d15b9daa32d36a11d366bc76af4db5c784a664cd0474f4166fd29

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:29:26.022916Z digest=sha256:353876b4b82ccb6d0b3404bae0e5ff3ac5a80dafce5a0717660d247e7c4a5abb

Pith citing papers

No inbound Pith citation observations are available.