Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration | Paper Citation Record

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This paper cites Meta-adapter: An online few-shot learner for vision-language model,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Meta-adapter: An online few-shot learner for vision-language model,

Reference 1

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This paper cites Zero-shot visual reasoning by vision- language models: Benchmarking and analysis,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Zero-shot visual reasoning by vision- language models: Benchmarking and analysis,

Reference 2

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This paper cites Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning

Reference 3

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This paper cites Deepseek-vl: Towards real-world vision-language understanding,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Deepseek-vl: Towards real-world vision-language understanding,

Reference 4

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This paper cites Active learning literature survey,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Active learning literature survey,

Reference 5

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This paper cites A survey of deep active learning,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration A survey of deep active learning,

Reference 6

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

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Learning transferable visual models from natural language supervision,

Reference 7

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This paper cites Zero-shot text-to-image generation,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Zero-shot text-to-image generation,

Reference 8

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Observation 44412f6b-a8c3-4515-8763-9f85ee8d6433 · outbound

This paper cites Flava: A foundational language and vision alignment model,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Flava: A foundational language and vision alignment model,

Reference 9

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This paper cites Overconfidence is key: Verbalized uncertainty evaluation in large language and vision-language models,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Overconfidence is key: Verbalized uncertainty evaluation in large language and vision-language models,

Reference 10

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This paper cites Seeing is believing: Mitigating hallu- cination in large vision-language models via clip-guided decoding,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Seeing is believing: Mitigating hallu- cination in large vision-language models via clip-guided decoding,

Reference 11

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This paper cites A Survey on Hallucination in Large Vision-Language Models.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration A Survey on Hallucination in Large Vision-Language Models

Reference 12

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This paper cites A Survey of Hallucination in Large Foundation Models.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration A Survey of Hallucination in Large Foundation Models

Reference 13

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This paper cites Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs

Reference 14

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This paper cites LM-Polygraph: Uncertainty Estimation for Language Models.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration LM-Polygraph: Uncertainty Estimation for Language Models

Reference 15

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This paper cites Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning

Reference 16

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This paper cites Overconfidence is Key: Verbalized Uncertainty Evaluation in Large Language and Vision-Language Models.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Overconfidence is Key: Verbalized Uncertainty Evaluation in Large Language and Vision-Language Models

Reference 17

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This paper cites Activedc: Distribution calibration for active finetuning,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Activedc: Distribution calibration for active finetuning,

Reference 18

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This paper cites Active finetuning: Exploiting annotation budget in the pretraining-finetuning paradigm,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Active finetuning: Exploiting annotation budget in the pretraining-finetuning paradigm,

Reference 19

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This paper cites Querying easily flip-flopped samples for deep active learning,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Querying easily flip-flopped samples for deep active learning,

Reference 20

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This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 21

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This paper cites Learning loss for active learning,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Learning loss for active learning,

Reference 22

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This paper cites Deep active learning for image classification,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Deep active learning for image classification,

Reference 23

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This paper cites Entropy-based active learning for object detection with progressive diversity constraint,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Entropy-based active learning for object detection with progressive diversity constraint,

Reference 24

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This paper cites Active learn- ing for deep object detection via probabilistic modeling,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Active learn- ing for deep object detection via probabilistic modeling,

Reference 25

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This paper cites Margin-based active learning for structured output spaces,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Margin-based active learning for structured output spaces,

Reference 26

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This paper cites A survey of deep active learning for foundation models,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration A survey of deep active learning for foundation models,

Reference 27

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This paper cites Revisiting active learning in the era of vision foundation models,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Revisiting active learning in the era of vision foundation models,

Reference 28

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This paper cites Active Learning Over Multiple Domains in Natural Language Tasks.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Active Learning Over Multiple Domains in Natural Language Tasks

Reference 29

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This paper cites Parameter-Efficient Active Learning for Foundational models.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Parameter-Efficient Active Learning for Foundational models

Reference 30

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Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Source- free continual adaptive learning with limited labels on evolving data drifts,

Reference 31

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This paper cites Active prompt learning in vision language models,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Active prompt learning in vision language models,

Reference 32

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This paper cites Learning to prompt for vision- language models,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Learning to prompt for vision- language models,

Reference 33

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This paper cites One-shot active learning for image segmentation via contrastive learning and diversity-based sampling,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration One-shot active learning for image segmentation via contrastive learning and diversity-based sampling,

Reference 34

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This paper cites Few-shot object detection with foundation models,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Few-shot object detection with foundation models,

Reference 35

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This paper cites A closer look at the few-shot adaptation of large vision-language models,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration A closer look at the few-shot adaptation of large vision-language models,

Reference 36

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Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Parameter efficient fine-tuning via cross block orchestration for segment anything model,

Reference 37

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This paper cites Pela: Learning parameter- efficient models with low-rank approximation,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Pela: Learning parameter- efficient models with low-rank approximation,

Reference 38

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This paper cites Maple: Multi-modal prompt learning,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Maple: Multi-modal prompt learning,

Reference 39

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This paper cites Conditional prompt learning for vision-language models,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Conditional prompt learning for vision-language models,

Reference 40

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This paper cites Improving model calibration with accu- racy versus uncertainty optimization,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Improving model calibration with accu- racy versus uncertainty optimization,

Reference 41

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Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Soft calibration objectives for neural net- works,

Reference 42

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

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration The power of scale for parameter-efficient prompt tuning,

Reference 43

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This paper cites LoRA: Low-rank adaptation of large language models,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration LoRA: Low-rank adaptation of large language models,

Reference 44

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This paper cites Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

Reference 45

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This paper cites Robust Contrastive Active Learning with Feature-guided Query Strategies.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Robust Contrastive Active Learning with Feature-guided Query Strategies

Reference 46

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This paper cites On calibration of modern neural networks,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration On calibration of modern neural networks,

Reference 47

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This paper cites Obtaining well calibrated probabilities using bayesian binning,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Obtaining well calibrated probabilities using bayesian binning,

Reference 48

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This paper cites A mathematical theory of communication,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration A mathematical theory of communication,

Reference 49

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This paper cites Bayesian Active Learning for Classification and Preference Learning.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Bayesian Active Learning for Classification and Preference Learning

Reference 50

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This paper cites Deep deterministic uncertainty: A new simple baseline,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Deep deterministic uncertainty: A new simple baseline,

Reference 51

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This paper cites Active learning for convolutional neural networks: A core-set approach,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Active learning for convolutional neural networks: A core-set approach,

Reference 52

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Observation 729f7435-8c8a-487d-b74e-c0d5c71606c4 · outbound

This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classi- fication,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Eurosat: A novel dataset and deep learning benchmark for land use and land cover classi- fication,

Reference 53

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This paper cites One-shot learning of object cate- gories,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration One-shot learning of object cate- gories,

Reference 54

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This paper cites Describing textures in the wild,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Describing textures in the wild,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:46:04.200745Z

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This paper cites Cats and dogs,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Cats and dogs,

Reference 56

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This paper cites Deep residual learning for image recognition,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Deep residual learning for image recognition,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T12:46:04.048156Z

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

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T12:46:04.049919Z

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This paper cites Online Zero-Shot Classification with CLIP.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Online Zero-Shot Classification with CLIP

Reference 59

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This paper cites Low-rank few-shot adaptation of vision- language models,.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Low-rank few-shot adaptation of vision- language models,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:46:04.183300Z

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