When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning | Paper Citation Record

Observation 91ed72ec-c62b-4ecb-b174-60b9f9382e9b · outbound

This paper cites A broader study of cross-domain few-shot learning,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning A broader study of cross-domain few-shot learning,

Reference 1

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Observation afb2d3d9-56f0-4d3c-93bf-d7e81db7bdc8 · outbound

This paper cites Visual domain bridge: A source-free domain adaptation for cross-domain few-shot learning,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Visual domain bridge: A source-free domain adaptation for cross-domain few-shot learning,

Reference 2

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This paper cites Enhanc- ing information maximization with distance-aware contrastive learning for source-free cross-domain few-shot learning,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Enhanc- ing information maximization with distance-aware contrastive learning for source-free cross-domain few-shot learning,

Reference 3

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Observation b9b8ab8a-ae11-4c74-84e1-d3bb2c157cd1 · outbound

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

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Learning transferable visual models from natural language supervision,

Reference 4

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Observation 07d82370-5e2b-4021-9934-5a8bcd88f082 · outbound

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

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Learning to prompt for vision- language models,

Reference 5

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

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning MaPLe: Multi-modal prompt learning,

Reference 6

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

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning LoRA: Low-rank adaptation of large language models,

Reference 7

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

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Low-rank few-shot adaptation of vision- language models,

Reference 8

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Observation 86a95c56-575c-40af-8039-231ccafa3e5e · outbound

This paper cites Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting

Reference 9

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This paper cites Step-wise distribution-aligned style prompt tuning for source-free cross-domain few-shot learning,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Step-wise distribution-aligned style prompt tuning for source-free cross-domain few-shot learning,

Reference 10

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Observation c6e1e672-f4a2-4684-8d95-1c2b8ed6d66e · outbound

This paper cites Addressing exacerbated attention sink for source-free cross-domain few-shot learning,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Addressing exacerbated attention sink for source-free cross-domain few-shot learning,

Reference 11

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Observation 9ee5d8f1-10f2-4dbb-8348-082fd37bf6b4 · outbound

This paper cites Reviving In-domain Fine-tuning Methods for Source-Free Cross-domain Few-shot Learning.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Reviving In-domain Fine-tuning Methods for Source-Free Cross-domain Few-shot Learning

Reference 12

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This paper cites Mind the discriminability trap in source-free cross-domain few-shot learning,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Mind the discriminability trap in source-free cross-domain few-shot learning,

Reference 13

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This paper cites Reclaiming lost text layers for source-free cross-domain few-shot learning,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Reclaiming lost text layers for source-free cross-domain few-shot learning,

Reference 14

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This paper cites Interpretable cross-domain few-shot learning with rectified target-domain local alignment,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Interpretable cross-domain few-shot learning with rectified target-domain local alignment,

Reference 15

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Observation 1b1a72e1-3b1e-48ae-8640-29e848250260 · outbound

This paper cites Matching networks for one shot learning,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Matching networks for one shot learning,

Reference 16

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This paper cites Model-agnostic meta-learning for fast adaptation of deep networks,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Model-agnostic meta-learning for fast adaptation of deep networks,

Reference 17

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This paper cites Prototypical networks for few-shot learning,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Prototypical networks for few-shot learning,

Reference 18

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This paper cites Learning to compare: Relation network for few-shot learning,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Learning to compare: Relation network for few-shot learning,

Reference 19

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Observation a8997aa7-772d-430e-96b9-6f0c34262a69 · outbound

This paper cites A closer look at few-shot classification,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning A closer look at few-shot classification,

Reference 20

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Observation fdf65a87-e38c-4fd4-a99b-ccacda6f1dcf · outbound

This paper cites Meta-dataset: A dataset of datasets for learning to learn from few examples,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Meta-dataset: A dataset of datasets for learning to learn from few examples,

Reference 21

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Observation bf9d481f-bfbf-4aa5-b09e-75f44968f6bc · outbound

This paper cites Cross-domain few-shot classification via learned feature-wise transformation,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Cross-domain few-shot classification via learned feature-wise transformation,

Reference 22

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This paper cites Understand- ing cross-domain few-shot learning based on domain similarity and few- shot difficulty,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Understand- ing cross-domain few-shot learning based on domain similarity and few- shot difficulty,

Reference 23

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Observation aec70df6-15a2-4b36-9072-7e0f37c01508 · outbound

This paper cites Cross-domain few-shot learning with task-specific adapters,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Cross-domain few-shot learning with task-specific adapters,

Reference 24

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This paper cites Ranking distance calibration for cross-domain few-shot learning,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Ranking distance calibration for cross-domain few-shot learning,

Reference 25

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Observation b8d40b13-6fba-4db6-9437-45d5eb671395 · outbound

This paper cites Revisiting proto- typical network for cross domain few-shot learning,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Revisiting proto- typical network for cross domain few-shot learning,

Reference 26

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Observation 4367262d-fc3d-4f8d-81a4-6d2ffef77f56 · outbound

This paper cites Wave-SAN: Wavelet based style augmentation network for cross-domain few-shot learning,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Wave-SAN: Wavelet based style augmentation network for cross-domain few-shot learning,

Reference 27

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This paper cites StyleAdv: Meta style adversarial training for cross-domain few-shot learning,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning StyleAdv: Meta style adversarial training for cross-domain few-shot learning,

Reference 28

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

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Flatten long-range loss landscapes for cross-domain few-shot learning,

Reference 29

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Observation f72597f2-e8c5-467a-9f1a-cdfb1fe5e7bc · outbound

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

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Reconstruction target matters in masked image modeling for cross-domain few-shot learning,

Reference 30

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This paper cites Attention temperature matters in ViT-based cross-domain few-shot learning,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Attention temperature matters in ViT-based cross-domain few-shot learning,

Reference 31

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

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning A closer look at the CLS token for cross-domain few-shot learning,

Reference 32

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This paper cites Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning

Reference 33

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This paper cites Random Registers for Cross-Domain Few-Shot Learning.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Random Registers for Cross-Domain Few-Shot Learning

Reference 34

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When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Conditional prompt learning for vision-language models,

Reference 35

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This paper cites Visual-language prompt tuning with knowledge-guided context optimization,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Visual-language prompt tuning with knowledge-guided context optimization,

Reference 36

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This paper cites Prompt-aligned gradient for prompt tuning,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Prompt-aligned gradient for prompt tuning,

Reference 37

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This paper cites Self-regulating prompts: Foundational model adaptation without forgetting,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Self-regulating prompts: Foundational model adaptation without forgetting,

Reference 38

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Observation 6f645aeb-b017-4e00-9dcc-56a7eeb6ba56 · outbound

This paper cites Tip-adapter: Training-free adaption of CLIP for few-shot classification,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Tip-adapter: Training-free adaption of CLIP for few-shot classification,

Reference 39

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When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning CLIP-adapter: Better vision-language models with feature adapters,

Reference 40

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This paper cites Task residual for tuning vision-language models,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Task residual for tuning vision-language models,

Reference 41

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This paper cites What does a platypus look like? generating customized prompts for zero-shot image classification,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning What does a platypus look like? generating customized prompts for zero-shot image classification,

Reference 42

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This paper cites Visual classification via description from large language models,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Visual classification via description from large language models,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:53.029081Z

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Observation 4d05aa4e-aae6-4dc9-9cc0-5bb04c14050f · outbound

This paper cites Improving CLIP adaptation by breaking tail alignment for source-free cross-domain few-shot learning,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Improving CLIP adaptation by breaking tail alignment for source-free cross-domain few-shot learning,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:53.019293Z

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Observation 17b1670d-236b-4eed-8bcd-69d710847afc · outbound

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

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning EuroSAT: A novel dataset and deep learning benchmark for land use and land cover classi- fication,

Reference 45

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Observation 96e7a90c-f278-4436-9447-5c15b8b7c968 · outbound

This paper cites Using deep learning for image-based plant disease detection,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Using deep learning for image-based plant disease detection,

Reference 46

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Observation 7b03c1c2-adea-401c-a0ee-dc2b697a1b26 · outbound

This paper cites Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC).

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Reference 47

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Observation 82b632b7-718e-4c16-9082-6375151bffc0 · outbound

This paper cites ChestX-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases,.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning ChestX-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:52.984997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation eeb541f8-e954-4f0b-8467-04a527f8c05a · outbound

This paper cites practical significance.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning practical significance

Reference 49

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T22:46:52.915205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers