Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score | Paper Citation Record

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This paper cites GPT-4 Technical Report.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score GPT-4 Technical Report

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

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Flamingo: a visual language model for few-shot learning

Reference 2

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This paper cites Dpa: Dual prototypes alignment for unsupervised adaptation of vision-language models.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Dpa: Dual prototypes alignment for unsupervised adaptation of vision-language models

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Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Layer Normalization

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Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Food-101–mining discriminative components with random forests

Reference 5

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This paper cites Lan- guage models are few-shot learners.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Lan- guage models are few-shot learners

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This paper cites Emerg- ing properties in self-supervised vision transformers.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Emerg- ing properties in self-supervised vision transformers

Reference 7

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This paper cites Remote sens- ing image scene classification: Benchmark and state of the art.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Remote sens- ing image scene classification: Benchmark and state of the art

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This paper cites Distribution-aware prompt tuning for vision-language mod- els.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Distribution-aware prompt tuning for vision-language mod- els

Reference 9

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This paper cites Describing textures in the wild.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Describing textures in the wild

Reference 10

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This paper cites Randaugment: Practical automated data augmen- tation with a reduced search space.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Randaugment: Practical automated data augmen- tation with a reduced search space

Reference 11

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This paper cites Improving clip training with language rewrites.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Improving clip training with language rewrites

Reference 12

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This paper cites Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories

Reference 13

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This paper cites Gpt-3: Its nature, scope, limits, and consequences.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Gpt-3: Its nature, scope, limits, and consequences

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Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Perceptron-based learning algo- rithms

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Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Unresolved cited work

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This paper cites Open-vocabulary object detection via vision and language knowledge distillation.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Open-vocabulary object detection via vision and language knowledge distillation

Reference 17

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This paper cites Parameter-efficient model adaptation for vision transformers.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Parameter-efficient model adaptation for vision transformers

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

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 19

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

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Lora: Low-rank adaptation of large language models

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This paper cites Reclip: Refine contrastive language image pre-training with source free domain adaptation.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Reclip: Refine contrastive language image pre-training with source free domain adaptation

Reference 21

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Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Unresolved cited work

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Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Unresolved cited work

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This paper cites Adapting visual-language models for generalizable anomaly detection in medical im- ages.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Adapting visual-language models for generalizable anomaly detection in medical im- ages

Reference 24

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This paper cites Unsupervised Prompt Learning for Vision-Language Models.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Unsupervised Prompt Learning for Vision-Language Models

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This paper cites Scaling up visual and vision-language representa- tion learning with noisy text supervision.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 26

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This paper cites Learning to prompt with text only supervision for vision- language models.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Learning to prompt with text only supervision for vision- language models

Reference 27

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Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Maple: Multi-modal prompt learning

Reference 28

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

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score 3d object representations for fine-grained categorization

Reference 29

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Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Learning multiple layers of features from tiny images

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Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Language-driven semantic seg- mentation

Reference 31

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Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

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This paper cites Visual-text cross alignment: Refining the similarity score in vision-language models.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Visual-text cross alignment: Refining the similarity score in vision-language models

Reference 33

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This paper cites Masked unsupervised self-training for label-free image classifica- tion.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Masked unsupervised self-training for label-free image classifica- tion

Reference 34

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Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Align before fuse: Vision and language representation learn- ing with momentum distillation

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Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Promptkd: Unsupervised prompt distillation for vision-language models

Reference 36

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This paper cites Clip is also an efficient segmenter: A text-driven approach for weakly supervised semantic segmentation.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Clip is also an efficient segmenter: A text-driven approach for weakly supervised semantic segmentation

Reference 37

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Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Decoupled weight de- cay regularization

Reference 38

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Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Prompt distribution learning

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Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Lafter: Label-free tuning of zero-shot clas- sifier using language and unlabeled image collections

Reference 40

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Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Automated flower classification over a large number of classes

Reference 41

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This paper cites Valse: A task- independent benchmark for vision and language models cen- tered on linguistic phenomena.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Valse: A task- independent benchmark for vision and language models cen- tered on linguistic phenomena

Reference 42

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Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Cats and dogs

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

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score What does a platypus look like? generating customized prompts for zero-shot image classification

Reference 44

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This paper cites Learning transferable visual models from natural language supervi- sion.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Learning transferable visual models from natural language supervi- sion

Reference 45

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Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Flava: A foundational language and vision alignment model

Reference 46

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This paper cites Ucf101: A dataset of 101 human actions classes from videos in the wild.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Ucf101: A dataset of 101 human actions classes from videos in the wild

Reference 47

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This paper cites Pouf: Prompt-oriented unsupervised fine-tuning for large pre-trained models.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Pouf: Prompt-oriented unsupervised fine-tuning for large pre-trained models

Reference 48

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This paper cites Clip the gap: A single domain generalization approach for object detection.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Clip the gap: A single domain generalization approach for object detection

Reference 49

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This paper cites The caltech-ucsd birds-200-2011 dataset.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score The caltech-ucsd birds-200-2011 dataset

Reference 50

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This paper cites Tent: Fully test-time adaptation by entropy minimization.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Tent: Fully test-time adaptation by entropy minimization

Reference 51

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This paper cites Debiased learning from naturally imbalanced pseudo-labels.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Debiased learning from naturally imbalanced pseudo-labels

Reference 52

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This paper cites Cora: Adapting clip for open-vocabulary detection with region prompting and anchor pre-matching.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Cora: Adapting clip for open-vocabulary detection with region prompting and anchor pre-matching

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:00:31.974225Z

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This paper cites Aid: A benchmark data set for performance evaluation of aerial scene classification.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Aid: A benchmark data set for performance evaluation of aerial scene classification

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:00:31.965642Z

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

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Sun database: Large-scale scene recognition from abbey to zoo

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:00:31.955970Z

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This paper cites Demystifying CLIP Data.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Demystifying CLIP Data

Reference 56

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This paper cites Groupvit: Semantic segmentation emerges from text supervision.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Groupvit: Semantic segmentation emerges from text supervision

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:00:31.946697Z

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This paper cites Lever- aging cross-modal neighbor representation for improved clip classification.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Lever- aging cross-modal neighbor representation for improved clip classification

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:00:31.937415Z

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This paper cites Florence: A New Foundation Model for Computer Vision.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Florence: A New Foundation Model for Computer Vision

Reference 59

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This paper cites When and why vision- language models behave like bags-of-words, and what to do about it? In International Conference on Learning Repre- sentations, 2023.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score When and why vision- language models behave like bags-of-words, and what to do about it? In International Conference on Learning Repre- sentations, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:00:31.927830Z

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This paper cites Boost- ing vision-language models with transduction.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Boost- ing vision-language models with transduction

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:00:31.918023Z

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This paper cites Candidate pseudolabel learning: Enhancing vision-language models by prompt tuning with unlabeled data.International Conference on Machine Learning, 2024.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Candidate pseudolabel learning: Enhancing vision-language models by prompt tuning with unlabeled data.International Conference on Machine Learning, 2024

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:00:31.908386Z

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This paper cites Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:00:31.898969Z

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This paper cites Mediclip: Adapting clip for few-shot medical image anomaly detection.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Mediclip: Adapting clip for few-shot medical image anomaly detection

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:00:31.889355Z

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This paper cites Conditional prompt learning for vision-language mod- els.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Conditional prompt learning for vision-language mod- els

Reference 65

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

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Learning to prompt for vision-language models

Reference 66

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This paper cites Not all features mat- ter: Enhancing few-shot clip with adaptive prior refinement.

Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Not all features mat- ter: Enhancing few-shot clip with adaptive prior refinement

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:00:31.869263Z

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