Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification | Paper Citation Record

Observation bf6135d5-8c0e-45ad-a7a9-7c5398badcc9 · outbound

This paper cites Siamese neural networks for one-shot image recognition,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Siamese neural networks for one-shot image recognition,

Reference 1

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Observation 482b07cb-a3ec-4fd1-9c50-d05f35b519b5 · outbound

This paper cites Matching networks for one shot learning,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Matching networks for one shot learning,

Reference 2

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

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Prototypical networks for few-shot learning,

Reference 3

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This paper cites U-net: Convolutional networks for biomedical image segmentation,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification U-net: Convolutional networks for biomedical image segmentation,

Reference 4

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This paper cites Meta r- cnn: Towards general solver for instance-level low-shot learning,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Meta r- cnn: Towards general solver for instance-level low-shot learning,

Reference 5

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Observation dc3d3ff2-a9d1-4382-98cc-0bba76d6d287 · outbound

This paper cites Few-shot object detection with attention-rpn and multi-relation detector,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Few-shot object detection with attention-rpn and multi-relation detector,

Reference 6

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Observation de91cf57-5a5a-4d4e-a1a6-3161cafb025c · outbound

This paper cites Mining latent classes for few-shot segmentation,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Mining latent classes for few-shot segmentation,

Reference 7

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This paper cites Imagenet classification with deep convolutional neural networks,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Imagenet classification with deep convolutional neural networks,

Reference 8

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Observation 108a793c-f5e3-4278-982c-bbad6ffa040e · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 9

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This paper cites Deep residual learning for image recognition,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Deep residual learning for image recognition,

Reference 10

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

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Learning to prompt for vision- language models,

Reference 11

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

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Conditional prompt learning for vision-language models,

Reference 12

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Observation 1df16ee4-a776-4ac4-bc7d-2d783d2cb0bc · outbound

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

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification What does a platypus look like? generating customized prompts for zero-shot image classification,

Reference 13

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Observation fcb989ce-5a41-4dad-913b-1bf6c60878c6 · outbound

This paper cites Chatgpt-powered hierarchical comparisons for image classification,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Chatgpt-powered hierarchical comparisons for image classification,

Reference 14

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This paper cites Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling

Reference 15

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This paper cites Sus-x: Training-free name- only transfer of vision-language models,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Sus-x: Training-free name- only transfer of vision-language models,

Reference 16

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This paper cites Partial-tuning based mixed-modal prototypes for few-shot classification,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Partial-tuning based mixed-modal prototypes for few-shot classification,

Reference 17

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Observation a14aa247-d146-4e6c-ae22-adec9623065d · outbound

This paper cites an unresolved cited work.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Unresolved cited work

Reference 18

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This paper cites Meta networks,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Meta networks,

Reference 19

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This paper cites High- resolution image synthesis with latent diffusion models,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification High- resolution image synthesis with latent diffusion models,

Reference 20

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This paper cites On First-Order Meta-Learning Algorithms.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification On First-Order Meta-Learning Algorithms

Reference 21

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This paper cites Attribute-guided feature learning for few- shot image recognition,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Attribute-guided feature learning for few- shot image recognition,

Reference 22

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Observation 7d236ba4-bc85-4197-aed4-b62904747a05 · outbound

This paper cites Revisiting Fine-tuning for Few-shot Learning.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Revisiting Fine-tuning for Few-shot Learning

Reference 23

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

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification A closer look at few-shot classification,

Reference 24

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This paper cites Meta-baseline: Exploring simple meta-learning for few-shot learning,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Meta-baseline: Exploring simple meta-learning for few-shot learning,

Reference 25

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This paper cites A baseline for few-shot image classification,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification A baseline for few-shot image classification,

Reference 26

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This paper cites Self-training for Few-shot Transfer Across Extreme Task Differences.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Self-training for Few-shot Transfer Across Extreme Task Differences

Reference 27

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Observation 8ff762c7-07e4-48e0-9ca0-9df3fc11ca3d · outbound

This paper cites Universal representation learning from multiple domains for few-shot classification,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Universal representation learning from multiple domains for few-shot classification,

Reference 28

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Observation f81ee1b5-48da-4a14-a9ea-9c6990da43d5 · outbound

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

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Visual-language prompt tuning with knowledge-guided context optimization,

Reference 29

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

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Prompt-aligned gradient for prompt tuning,

Reference 30

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

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Visual classification via description from large language models,

Reference 31

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This paper cites Clip-adapter: Better vision-language models with feature adapters,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Clip-adapter: Better vision-language models with feature adapters,

Reference 32

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This paper cites Infinite mixture prototypes for few-shot learning,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Infinite mixture prototypes for few-shot learning,

Reference 33

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This paper cites Multi- prototype few-shot learning in histopathology,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Multi- prototype few-shot learning in histopathology,

Reference 34

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This paper cites Local descriptor-based multi-prototype network for few-shot learning,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Local descriptor-based multi-prototype network for few-shot learning,

Reference 35

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This paper cites Token contrast for weakly- supervised semantic segmentation,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Token contrast for weakly- supervised semantic segmentation,

Reference 36

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This paper cites Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. XX, XXXX 2025 11 models,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. XX, XXXX 2025 11 models,

Reference 37

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This paper cites Flamingo: a visual language model for few-shot learning,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Flamingo: a visual language model for few-shot learning,

Reference 38

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This paper cites Ofa: Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Ofa: Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework,

Reference 39

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Observation dce535f6-8f22-486a-a655-69b964159b2e · outbound

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

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Learning transferable visual models from natural language supervision,

Reference 40

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This paper cites Language mod- els are few-shot learners,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Language mod- els are few-shot learners,

Reference 41

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This paper cites Imagenet: A large-scale hierarchical image database,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Imagenet: A large-scale hierarchical image database,

Reference 42

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This paper cites 3d object representations for fine-grained categorization,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification 3d object representations for fine-grained categorization,

Reference 43

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This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 44

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This paper cites Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,

Reference 45

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This paper cites Automated flower classification over a large number of classes,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Automated flower classification over a large number of classes,

Reference 46

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This paper cites Sun database: Large-scale scene recognition from abbey to zoo,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Sun database: Large-scale scene recognition from abbey to zoo,

Reference 47

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This paper cites Describing textures in the wild,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Describing textures in the wild,

Reference 48

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

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Eurosat: A novel dataset and deep learning benchmark for land use and land cover classi- fication,

Reference 49

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This paper cites Fine-Grained Visual Classification of Aircraft.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Fine-Grained Visual Classification of Aircraft

Reference 50

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This paper cites Cats and dogs,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Cats and dogs,

Reference 51

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This paper cites Food-101 – mining discriminative components with random forests,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Food-101 – mining discriminative components with random forests,

Reference 52

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This paper cites Plot: Prompt learning with optimal transport for vision-language models,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Plot: Prompt learning with optimal transport for vision-language models,

Reference 53

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Observation d66bb7c5-f8d5-46d0-83bf-8a5d4edfe32b · outbound

This paper cites Lp++: A surprisingly strong linear probe for few-shot clip,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Lp++: A surprisingly strong linear probe for few-shot clip,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:34.488826Z

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Observation bcacbde8-0160-40cc-a208-3a8b73c77de2 · outbound

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

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification A closer look at the few-shot adaptation of large vision-language models,

Reference 55

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Observation 233f1f5d-9bae-4a59-b90b-0dc9268517ed · outbound

This paper cites Attention is all you need,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Attention is all you need,

Reference 56

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Observation 56169733-40dc-443c-ac17-6d3608bd38f8 · outbound

This paper cites Do imagenet clas- sifiers generalize to imagenet?.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Do imagenet clas- sifiers generalize to imagenet?

Reference 57

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Observation a9c7ceff-c7f5-48e5-81d3-73b1cdc42758 · outbound

This paper cites Learning robust global representations by penalizing local predictive power,.

Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification Learning robust global representations by penalizing local predictive power,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:33.918131Z

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