Paper Citation Record · LEDGER

Efficient Few-Shot Continual Learning in Vision-Language Models

As of 10 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2502.04098.

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

pith.paper-citation-record.v1
2502.04098 v2

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T23:37:28.784524Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

48 of 48 outbound references displayed

verified exact2
verified fuzzy13
unresolved33
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b411f066-004c-483f-b86f-ebd22c159a1d · outbound

This paper cites write newline.

Efficient Few-Shot Continual Learning in Vision-Language Models write newline

Reference 1

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Observation b7368ab0-2287-4a27-8703-05c50ddd22fb · outbound

This paper cites K., Ajanthan, T., and Torr, P.

Efficient Few-Shot Continual Learning in Vision-Language Models K., Ajanthan, T., and Torr, P

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:37:29.657235Z

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Observation 205508cb-51bb-4310-a660-2e25087369b5 · outbound

This paper cites MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning.

Efficient Few-Shot Continual Learning in Vision-Language Models MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning

Reference 3

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Observation 9b80ff9e-043f-4ab4-b2f7-2214f87f094a · outbound

This paper cites Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks.

Efficient Few-Shot Continual Learning in Vision-Language Models Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks

Reference 4

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This paper cites Can we edit multimodal large language models? In Bouamor, H., Pino, J., and Bali, K.

Efficient Few-Shot Continual Learning in Vision-Language Models Can we edit multimodal large language models? In Bouamor, H., Pino, J., and Bali, K

Reference 5

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Efficient Few-Shot Continual Learning in Vision-Language Models E., et al

Reference 6

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Observation ad90ecaf-8733-4b6b-a5a6-b9ce7e557e6e · outbound

This paper cites Knowledge Neurons in Pretrained Transformers.

Efficient Few-Shot Continual Learning in Vision-Language Models Knowledge Neurons in Pretrained Transformers

Reference 7

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Observation 12dbc5e0-e4bb-4f2b-bc8c-25cfea4133bf · outbound

This paper cites One VLM to Keep it Learning: Generation and Balancing for Data-free Continual Visual Question Answering.

Efficient Few-Shot Continual Learning in Vision-Language Models One VLM to Keep it Learning: Generation and Balancing for Data-free Continual Visual Question Answering

Reference 8

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Observation a7e3c7f8-c3bb-4da7-8a18-bc6d0d916d20 · outbound

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Efficient Few-Shot Continual Learning in Vision-Language Models Toyota smarthome: Real-world activities of daily living

Reference 9

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Observation 1e26982f-c1b1-407f-9f89-6b05cceb0f28 · outbound

This paper cites A continual learning survey: Defying forgetting in classification tasks.

Efficient Few-Shot Continual Learning in Vision-Language Models A continual learning survey: Defying forgetting in classification tasks

Reference 10

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Observation 99a4f794-781c-429c-8519-24ba08951087 · outbound

This paper cites Image N et: A large-scale hierarchical image database.

Efficient Few-Shot Continual Learning in Vision-Language Models Image N et: A large-scale hierarchical image database

Reference 11

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Observation 437c1faf-db40-43b4-9f87-ffda471c785d · outbound

This paper cites Vlmevalkit: An open-source toolkit for evaluating large multi-modality models.

Efficient Few-Shot Continual Learning in Vision-Language Models Vlmevalkit: An open-source toolkit for evaluating large multi-modality models

Reference 12

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Observation ab323fa8-81b2-4ae0-976b-cb0a53b2844e · outbound

This paper cites Calibrating higher-order statistics for few-shot class-incremental learning with pre-trained vision transformers.

Efficient Few-Shot Continual Learning in Vision-Language Models Calibrating higher-order statistics for few-shot class-incremental learning with pre-trained vision transformers

Reference 13

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This paper cites Sensitivity-aware visual parameter-efficient fine-tuning.

Efficient Few-Shot Continual Learning in Vision-Language Models Sensitivity-aware visual parameter-efficient fine-tuning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:37:29.556818Z

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Observation 4904d7d8-9b36-4b90-8fbc-a409f9e4a06b · outbound

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Efficient Few-Shot Continual Learning in Vision-Language Models Continual Instruction Tuning for Large Multimodal Models

Reference 15

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Observation 52bbe0a8-4106-4f43-bcbb-01b51402724a · outbound

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

Efficient Few-Shot Continual Learning in Vision-Language Models Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 16

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Observation 808de7d4-7cdc-4e7f-baa8-d71e69274896 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Efficient Few-Shot Continual Learning in Vision-Language Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 17

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Observation f7140633-96f6-4c00-9bee-71368dcfa5a6 · outbound

This paper cites VisOnlyQA: Large Vision Language Models Still Struggle with Visual Perception of Geometric Information.

Efficient Few-Shot Continual Learning in Vision-Language Models VisOnlyQA: Large Vision Language Models Still Struggle with Visual Perception of Geometric Information

Reference 18

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Observation 9aff4976-85c2-4df6-af12-8e057ccfb2b6 · outbound

This paper cites The hateful memes challenge: Detecting hate speech in multimodal memes.

Efficient Few-Shot Continual Learning in Vision-Language Models The hateful memes challenge: Detecting hate speech in multimodal memes

Reference 19

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Observation 3ecb4bff-bfa8-4e3f-9493-bfa3226e66ed · outbound

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Efficient Few-Shot Continual Learning in Vision-Language Models Adam: A Method for Stochastic Optimization

Reference 20

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Efficient Few-Shot Continual Learning in Vision-Language Models A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al

Reference 21

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Observation e1b66187-4c8f-46f0-9daa-302073cda715 · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

Efficient Few-Shot Continual Learning in Vision-Language Models Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 22

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Efficient Few-Shot Continual Learning in Vision-Language Models Unresolved cited work

Reference 23

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Reference 24

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Efficient Few-Shot Continual Learning in Vision-Language Models and Ranzato, M

Reference 26

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Efficient Few-Shot Continual Learning in Vision-Language Models Decoupled Weight Decay Regularization

Reference 27

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Efficient Few-Shot Continual Learning in Vision-Language Models Fine-grained visual classification of aircraft

Reference 28

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Observation 73127fd7-f589-4d6c-9e6e-7db05cfd1e32 · outbound

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Efficient Few-Shot Continual Learning in Vision-Language Models Locating and editing factual associations in gpt

Reference 29

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Efficient Few-Shot Continual Learning in Vision-Language Models Unresolved cited work

Reference 30

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Efficient Few-Shot Continual Learning in Vision-Language Models O., Aljundi, R., and Turner, R

Reference 31

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Efficient Few-Shot Continual Learning in Vision-Language Models Pytorch: An imperative style, high-performance deep learning library

Reference 32

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Efficient Few-Shot Continual Learning in Vision-Language Models Fi LM : Visual reasoning with a general conditioning layer

Reference 33

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Efficient Few-Shot Continual Learning in Vision-Language Models W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

Reference 34

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Efficient Few-Shot Continual Learning in Vision-Language Models Editable neural networks

Reference 35

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Observation c7e6b685-9dc2-4a6e-abc3-d5ab741ae19e · outbound

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Efficient Few-Shot Continual Learning in Vision-Language Models Improving Multimodal Large Language Models Using Continual Learning

Reference 36

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Efficient Few-Shot Continual Learning in Vision-Language Models Unresolved cited work

Reference 37

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Efficient Few-Shot Continual Learning in Vision-Language Models Eyes wide shut? exploring the visual shortcomings of multimodal llms

Reference 38

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Efficient Few-Shot Continual Learning in Vision-Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 39

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Efficient Few-Shot Continual Learning in Vision-Language Models N., Kaiser, ., and Polosukhin, I

Reference 40

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Efficient Few-Shot Continual Learning in Vision-Language Models Continual Learning: Applications and the Road Forward

Reference 41

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This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

Efficient Few-Shot Continual Learning in Vision-Language Models Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 42

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This paper cites A Sober Look at the Robustness of CLIPs to Spurious Features.

Efficient Few-Shot Continual Learning in Vision-Language Models A Sober Look at the Robustness of CLIPs to Spurious Features

Reference 43

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Efficient Few-Shot Continual Learning in Vision-Language Models Continual Learning for Large Language Models: A Survey

Reference 44

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Efficient Few-Shot Continual Learning in Vision-Language Models AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 45

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Efficient Few-Shot Continual Learning in Vision-Language Models Overcoming generic knowledge loss with selective parameter update

Reference 46

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Efficient Few-Shot Continual Learning in Vision-Language Models SAFE: Slow and Fast Parameter-Efficient Tuning for Continual Learning with Pre-Trained Models

Reference 47

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Efficient Few-Shot Continual Learning in Vision-Language Models MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 48

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Pith citing papers

No inbound Pith citation observations are available.