Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning | Paper Citation Record

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Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Unresolved cited work

Reference 1

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This paper cites Subspace regularizers for few-shot class in- cremental learning, 2022.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Subspace regularizers for few-shot class in- cremental learning, 2022

Reference 2

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Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Unresolved cited work

Reference 3

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This paper cites E2vpt: An effec- tive and efficient approach for visual prompt tuning.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning E2vpt: An effec- tive and efficient approach for visual prompt tuning

Reference 4

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This paper cites Uncertainty-aware distillation for semi-supervised few-shot class-incremental learning.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Uncertainty-aware distillation for semi-supervised few-shot class-incremental learning

Reference 5

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This paper cites Lpt: Long-tailed prompt tuning for image classifica- tion, 2023.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Lpt: Long-tailed prompt tuning for image classifica- tion, 2023

Reference 6

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale, 2021.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning An image is worth 16x16 words: Transformers for image recognition at scale, 2021

Reference 7

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Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Unresolved cited work

Reference 8

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Observation 62c0b818-994a-4963-b331-49799f935c31 · outbound

This paper cites Decoupling represen- tation and knowledge for few-shot intent classification and slot filling.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Decoupling represen- tation and knowledge for few-shot intent classification and slot filling

Reference 9

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This paper cites The inaturalist species classification and detection dataset.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning The inaturalist species classification and detection dataset

Reference 10

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Observation 02002ae8-ffaf-4669-b11e-417a331eaa32 · outbound

This paper cites Diversity- aware meta visual prompting, 2023.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Diversity- aware meta visual prompting, 2023

Reference 11

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Observation 2faa4f19-9bcc-459c-8a77-edfa316e95ab · outbound

This paper cites Learning prompt with distribution-based feature replay for few-shot class-incremental learning, 2024.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Learning prompt with distribution-based feature replay for few-shot class-incremental learning, 2024

Reference 12

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This paper cites Vi- sual prompt tuning, 2022.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Vi- sual prompt tuning, 2022

Reference 13

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Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Unresolved cited work

Reference 14

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Observation 2830f972-d195-4bb7-af0f-c74c69368074 · outbound

This paper cites Warping the space: Weight space rotation for class- incremental few-shot learning.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Warping the space: Weight space rotation for class- incremental few-shot learning

Reference 15

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This paper cites Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross Girshick.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross Girshick

Reference 16

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Observation 9e97c21f-79f3-4504-98ad-5204b04926be · outbound

This paper cites Learning multiple layers of features from tiny images.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Learning multiple layers of features from tiny images

Reference 17

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Observation 0a4cf309-706e-4fe7-a184-52daf1cc11d6 · outbound

This paper cites General- ized and incremental few-shot learning by explicit learning and calibration without forgetting.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning General- ized and incremental few-shot learning by explicit learning and calibration without forgetting

Reference 18

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Observation 9d8c9c45-145d-49d1-a207-cc6488f2fd5f · outbound

This paper cites The power of scale for parameter-efficient prompt tuning, 2021.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning The power of scale for parameter-efficient prompt tuning, 2021

Reference 19

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This paper cites Few-shot class incre- mental learning with attention-aware self-adaptive prompt,.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Few-shot class incre- mental learning with attention-aware self-adaptive prompt,

Reference 20

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This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natu- ral language processing, 2021.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Pre-train, prompt, and predict: A systematic survey of prompting methods in natu- ral language processing, 2021

Reference 21

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This paper cites InsVP: Efficient instance visual prompting from image itself.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning InsVP: Efficient instance visual prompting from image itself

Reference 22

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This paper cites Recon- struction target matters in masked image modeling for cross- domain few-shot learning.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Recon- struction target matters in masked image modeling for cross- domain few-shot learning

Reference 23

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Observation 4c8a73bc-7856-460d-b732-974e4a2075f2 · outbound

This paper cites Fine-grained visual classi- fication of aircraft, 2013.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Fine-grained visual classi- fication of aircraft, 2013

Reference 24

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This paper cites Pseudo-set frequency refinement architecture for fine-grained few-shot class- incremental learning.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Pseudo-set frequency refinement architecture for fine-grained few-shot class- incremental learning

Reference 25

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Observation 83a04789-629f-4588-8e00-471e6cd375f5 · outbound

This paper cites Pre-trained vision and language transformers are few-shot incremental learners, 2024.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Pre-trained vision and language transformers are few-shot incremental learners, 2024

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:07:22.599278Z

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Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Unresolved cited work

Reference 27

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This paper cites Learning transferable visual models from natural language supervision, 2021.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Learning transferable visual models from natural language supervision, 2021

Reference 28

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This paper cites Self-supervised Knowledge Distillation for Few-shot Learning.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Self-supervised Knowledge Distillation for Few-shot Learning

Reference 29

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Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Unresolved cited work

Reference 30

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This paper cites Few-shot class incremental learning with generative feature replay.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Few-shot class incremental learning with generative feature replay

Reference 31

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This paper cites Coda-prompt: Contin- ual decomposed attention-based prompting for rehearsal-free continual learning, 2023.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Coda-prompt: Contin- ual decomposed attention-based prompting for rehearsal-free continual learning, 2023

Reference 32

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This paper cites Rethinking few-shot class-incremental learning: Learning from yourself.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Rethinking few-shot class-incremental learning: Learning from yourself

Reference 33

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This paper cites Few-shot class- incremental learning.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Few-shot class- incremental learning

Reference 34

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This paper cites Matching networks for one shot learning, 2017.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Matching networks for one shot learning, 2017

Reference 35

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This paper cites Belongie.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Belongie

Reference 36

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This paper cites Foster: Feature boosting and compression for class- incremental learning, 2022.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Foster: Feature boosting and compression for class- incremental learning, 2022

Reference 37

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This paper cites Hierarchical decomposition of prompt-based continual learning: Rethinking obscured sub- optimality, 2023.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Hierarchical decomposition of prompt-based continual learning: Rethinking obscured sub- optimality, 2023

Reference 38

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This paper cites Few-shot class-incremental learning via training-free prototype calibration, 2023.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Few-shot class-incremental learning via training-free prototype calibration, 2023

Reference 39

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Observation 2bcc4a9f-99de-4eaf-94c1-a8821f455bd7 · outbound

This paper cites On the approximation risk of few-shot class- incremental learning.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning On the approximation risk of few-shot class- incremental learning

Reference 40

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Observation 5b93e04b-d684-4de0-b201-4fb2b97344a3 · outbound

This paper cites S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning, 2023.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning, 2023

Reference 41

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Observation 6637392d-7888-4aa4-8a54-2922cbd103a0 · outbound

This paper cites Dualprompt: Com- plementary prompting for rehearsal-free continual learning,.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Dualprompt: Com- plementary prompting for rehearsal-free continual learning,

Reference 42

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This paper cites Learning to prompt for continual learning, 2022.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Learning to prompt for continual learning, 2022

Reference 43

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This paper cites Dynamic sup- port network for few-shot class incremental learning.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Dynamic sup- port network for few-shot class incremental learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:07:19.321055Z

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Observation 75963f4f-d802-4b1f-9918-84c386ff1cb7 · outbound

This paper cites Neural collapse inspired feature- classifier alignment for few-shot class-incremental learning.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Neural collapse inspired feature- classifier alignment for few-shot class-incremental learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:07:19.083351Z

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Observation feb64986-0a1e-4272-a6d9-50e4603e89a0 · outbound

This paper cites Few-shot incremental learning with contin- ually evolved classifiers.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Few-shot incremental learning with contin- ually evolved classifiers

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:07:18.920885Z

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Observation 99c33817-31d8-4e0f-b43b-089c8258258f · outbound

This paper cites Mi- haylova.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Mi- haylova

Reference 47

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Observation 7d93c46b-6d93-416e-bf57-afa6b58b9b86 · outbound

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

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Few-shot class- incremental learning via class-aware bilateral distillation

Reference 48

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Observation cd6561ac-0ed2-4df0-872c-90be0f860ae3 · outbound

This paper cites Forward compatible few-shot class-incremental learning.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Forward compatible few-shot class-incremental learning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:07:18.317217Z

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

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Few-shot class-incremental learn- ing by sampling multi-phase tasks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:07:18.112591Z

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Observation e7e2b6d6-72ce-4985-bcdd-5465de15e5f0 · outbound

This paper cites Delve into Base-Novel Confusion: Redundancy Exploration for Few-Shot Class-Incremental Learning.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Delve into Base-Novel Confusion: Redundancy Exploration for Few-Shot Class-Incremental Learning

Reference 51

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This paper cites Self-promoted prototype refinement for few-shot class- incremental learning, 2021.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Self-promoted prototype refinement for few-shot class- incremental learning, 2021

Reference 52

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This paper cites Margin-based few-shot class-incremental learning with class-level overfitting mitigation.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Margin-based few-shot class-incremental learning with class-level overfitting mitigation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:07:17.770992Z

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This paper cites Flatten long-range loss landscapes for cross-domain few- shot learning.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Flatten long-range loss landscapes for cross-domain few- shot learning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:07:17.603621Z

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Observation cd4a2307-2e85-4416-a7fd-eff26a60adc1 · outbound

This paper cites Atten- tion temperature matters in vit-based cross-domain few-shot learning.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Atten- tion temperature matters in vit-based cross-domain few-shot learning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:07:17.402987Z

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Observation aec7f8ef-4374-4eef-b14d-c37a6e8b47eb · outbound

This paper cites A closer look at the CLS token for cross-domain few-shot learning.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning A closer look at the CLS token for cross-domain few-shot learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:07:17.291276Z

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Observation 9c932479-c6f7-4cdb-b7e8-d192ee161c5e · outbound

This paper cites Compositional Few-Shot Class-Incremental Learning.

Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning Compositional Few-Shot Class-Incremental Learning

Reference 57

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