Paper Citation Record · LEDGER

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP

As of 8 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2509.23335.

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

pith.paper-citation-record.v1
2509.23335 v3

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T14:50:00.686605Z

measured 39 of 39 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

39 of 39 outbound references displayed

verified exact0
verified fuzzy0
unresolved38
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 971b53a8-7c32-41f1-8d03-057334587bed · outbound

This paper cites Curriculum learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Curriculum learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.546842Z

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Observation 9027aca4-80bf-4057-a86c-07446f622c1c · outbound

This paper cites Dark experience for general continual learning: a strong, simple baseline.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Dark experience for general continual learning: a strong, simple baseline

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.550913Z

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Observation 13e9702a-235c-487d-ad40-a75d725ca069 · outbound

This paper cites Rebalancing batch normalization for exemplar-based class-incremental learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Rebalancing batch normalization for exemplar-based class-incremental learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.554335Z

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Observation aebedace-10c0-4823-a7e8-ab54c6508884 · outbound

This paper cites Towards calibrated multi-label deep neural networks.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Towards calibrated multi-label deep neural networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.558496Z

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Observation 4eda4b3a-89c8-4f4d-bc86-64bc659a981c · outbound

This paper cites Less is more: Summarizing Patch Tokens for efficient Multi-Label Class-Incremental Learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Less is more: Summarizing Patch Tokens for efficient Multi-Label Class-Incremental Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.562530Z

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Observation 70c473bf-7b8a-40ef-be8c-14672437f551 · outbound

This paper cites Knowledge restore and transfer for multi-label class-incremental learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Knowledge restore and transfer for multi-label class-incremental learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.566269Z

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Observation 3d462f00-ab3e-46f5-a5b3-127dc334594e · outbound

This paper cites Podnet: Pooled outputs distillation for small-tasks incremental learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Podnet: Pooled outputs distillation for small-tasks incremental learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.570580Z

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Observation ee26a252-4df6-404e-8556-04d58871c76e · outbound

This paper cites Multi-label continual learning using augmented graph convolutional network.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Multi-label continual learning using augmented graph convolutional network

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.574362Z

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Observation df5d108c-47b0-4249-beff-b1a05ab43b8b · outbound

This paper cites Confidence self-calibration for multi-label class-incremental learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Confidence self-calibration for multi-label class-incremental learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.577514Z

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Observation fe7ad23a-84b6-42bd-8554-9c6f45ab02ec · outbound

This paper cites Rebalancing multi-label class-incremental learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Rebalancing multi-label class-incremental learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.581423Z

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Unavailable: canonical work link unavailable.

Observation 4e6f8fbd-b356-44d4-8f17-4cf3ee4c0b6e · outbound

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

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP The pascal visual object classes (voc) challenge

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.585547Z

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Observation 2246be1c-2527-4bd7-ab85-c2e3030c3088 · outbound

This paper cites Dualcoop++: Fast and effective adaptation to multi-label recognition with limited annotations.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Dualcoop++: Fast and effective adaptation to multi-label recognition with limited annotations

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.589411Z

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Observation ce88d59e-322c-4275-a942-2248347236a7 · outbound

This paper cites Mind the gap: Preserving and compensating for the modality gap in clip-based continual learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Mind the gap: Preserving and compensating for the modality gap in clip-based continual learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.592963Z

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Observation bb5609b4-2446-41b9-af5d-d04fcbda292b · outbound

This paper cites Ovor: Oneprompt with virtual outlier regularization for rehearsal-free class-incremental learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Ovor: Oneprompt with virtual outlier regularization for rehearsal-free class-incremental learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.596116Z

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Observation ca036244-81cc-4467-aed9-465f2ca837e8 · outbound

This paper cites Imbalanced continual learning with partitioning reservoir sampling.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Imbalanced continual learning with partitioning reservoir sampling

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.599394Z

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Observation 0380be24-5bde-4338-a268-9358bbb45244 · outbound

This paper cites Adam: A method for stochastic optimization.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Adam: A method for stochastic optimization

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.603577Z

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Observation f1bfb670-a682-417d-94ac-4c8abefb8633 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Overcoming catastrophic forgetting in neural networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.606738Z

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Observation 42a07ea9-ba0a-4a99-a7a1-a34f305f1952 · outbound

This paper cites Learning without forgetting.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Learning without forgetting

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.609966Z

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Observation a3193732-eeda-482e-b8e2-bbc5f5a967c2 · outbound

This paper cites Optimizing Class Distribution in Memory for Multi-Label Online Continual Learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Optimizing Class Distribution in Memory for Multi-Label Online Continual Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.613209Z

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Observation 8754f631-6b64-487a-872e-3dd9d9150d7e · outbound

This paper cites Microsoft coco: Common objects in context.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Microsoft coco: Common objects in context

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.617045Z

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Unavailable: canonical work link unavailable.

Observation f2bb24ee-30d7-4216-a736-66ed64aea08a · outbound

This paper cites D3former: Debiased dual distilled transformer for incremental learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP D3former: Debiased dual distilled transformer for incremental learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.620584Z

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Observation 8b21b759-288e-4d93-b77c-495f3baceea3 · outbound

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

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Learning transferable visual models from natural language supervision

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.624255Z

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Observation cf484263-9ac4-4a0e-910e-bef7e1e06562 · outbound

This paper cites icarl: Incremental classifier and representation learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP icarl: Incremental classifier and representation learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.627566Z

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Observation 9e76a9d6-d050-4709-8738-8bfb9ae429d0 · outbound

This paper cites Experience replay for continual learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Experience replay for continual learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.631200Z

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Observation 470d1e4e-64e1-41df-8647-68c8a098818d · outbound

This paper cites Progress & compress: A scalable framework for continual learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Progress & compress: A scalable framework for continual learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.634987Z

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Observation b3974361-45fa-4b2c-831e-826e4cd18c56 · outbound

This paper cites Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.639146Z

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Observation 51bfddcc-1b17-4182-a33e-b18e09664fa3 · outbound

This paper cites Topology-preserving class-incremental learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Topology-preserving class-incremental learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.643222Z

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Observation 5bc98fe9-b5a5-4fda-b39f-57fd78344eef · outbound

This paper cites Cut out and replay: A simple yet versatile strategy for multi-label online continual learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Cut out and replay: A simple yet versatile strategy for multi-label online continual learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.646967Z

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Observation 34d7a0c0-d7e9-4d76-af82-fbc626eb6bbf · outbound

This paper cites Dualprompt: Complementary prompting for rehearsal-free continual learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Dualprompt: Complementary prompting for rehearsal-free continual learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.651176Z

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Observation cd8003bb-f5f1-4359-89ab-caa45e697c0d · outbound

This paper cites Learning to prompt for continual learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Learning to prompt for continual learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.654679Z

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Observation e537517d-7c9d-47db-99ab-53c4fd9bac8b · outbound

This paper cites Large scale incremental learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Large scale incremental learning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.658320Z

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Observation b1516dcb-02b8-4da5-800e-5ec89596f6df · outbound

This paper cites Specifying what you know or not for multi-label class-incremental learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Specifying what you know or not for multi-label class-incremental learning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.661602Z

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Observation c3d6efd3-a54b-4c4a-aca5-2d3e862d13ee · outbound

This paper cites Dynamic Prompt Adjustment for Multi-Label Class-Incremental Learning.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Dynamic Prompt Adjustment for Multi-Label Class-Incremental Learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.664698Z

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Observation ddd1f744-5af6-4fd1-bf8e-455fb289e72d · outbound

This paper cites Few-shot class-incremental learning via class-aware bilateral distillation.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Few-shot class-incremental learning via class-aware bilateral distillation

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.668170Z

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Observation 36806599-e544-4493-82f5-debb0b0cad8e · outbound

This paper cites Few-shot class-incremental learning by sampling multi-phase tasks.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Few-shot class-incremental learning by sampling multi-phase tasks

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.671360Z

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Observation cd4cba41-e77e-4b06-ac77-0f60e09254a6 · outbound

This paper cites write newline.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP write newline

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.674675Z

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Observation 9813b4b1-11ab-4916-924d-3bda5a9385f5 · outbound

This paper cites @esa (Ref.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP @esa (Ref

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.679378Z

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Observation 8ad7b49e-7714-4dfa-917b-a9301c3b9c9a · outbound

This paper cites an unresolved cited work.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP Unresolved cited work

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:00.683075Z

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Observation f0d7f097-cc89-46a3-a59f-47bb47461c4e · outbound

This paper cites =5' 7&o6ؒv 9 Ӵe ̙ ͸0k 5;|H݇d VRR R8u!5uU n 5 eQJnr /- Գ[P ? X>Z 1W^ M@nl t!->NteUZ I R 﫰:3 skQ Tx.

DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP =5' 7&o6ؒv 9 Ӵe ̙ ͸0k 5;|H݇d VRR R8u!5uU n 5 eQJnr /- Գ[P ? X>Z 1W^ M@nl t!->NteUZ I R 﫰:3 skQ Tx

Reference 39

Resolution: malformed identifier; no resolver link, observed 2026-08-04T14:50:00.686605Z

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Pith citing papers

No inbound Pith citation observations are available.