Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective | Paper Citation Record

Observation fa803f8e-848a-4c18-91a1-da9608fff9c0 · outbound

This paper cites Pseudo-labeling and confirmation bias in deep semi-supervised learning.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Pseudo-labeling and confirmation bias in deep semi-supervised learning

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Observation e5366ae3-813f-4b0c-b147-5fc6a04d33c9 · outbound

This paper cites Mixmatch: A holistic approach to semi-supervised learning.Advances in Neural Information Processing Systems (NeurIPS), 32, 2019.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Mixmatch: A holistic approach to semi-supervised learning.Advances in Neural Information Processing Systems (NeurIPS), 32, 2019

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This paper cites Cubuk, Alex Ku- rakin, Kihyuk Sohn, Han Zhang, and Colin Raffel.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Cubuk, Alex Ku- rakin, Kihyuk Sohn, Han Zhang, and Colin Raffel

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This paper cites Adamatch: A unified approach to semi-supervised learning and domain adaptation.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Adamatch: A unified approach to semi-supervised learning and domain adaptation

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Observation fc1c8c5e-ec21-4b55-85b0-9c746366767b · outbound

This paper cites Exponential moving average normalization for self-supervised and semi- supervised learning.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Exponential moving average normalization for self-supervised and semi- supervised learning

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This paper cites Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning

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This paper cites Semi-supervised learning (chapelle, o.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Semi-supervised learning (chapelle, o

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Observation 43d0a68d-ef0b-40ad-bfc2-fbb47992b7c5 · outbound

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

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Plot: Prompt learning with optimal transport for vision-language models

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This paper cites Softmatch: Addressing the quantity-quality tradeoff in semi- supervised learning.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Softmatch: Addressing the quantity-quality tradeoff in semi- supervised learning

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Observation a72c4a10-46b5-4dad-b166-ef5cddcdcc23 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective A simple framework for contrastive learning of visual representations

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Observation 81675ae9-5bcf-40a7-a5c0-0b32c8d7178d · outbound

This paper cites Big self-supervised models are strong semi-supervised learners.Advances in Neural Information Processing Systems (NeurIPS), 2020.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Big self-supervised models are strong semi-supervised learners.Advances in Neural Information Processing Systems (NeurIPS), 2020

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Observation 83259781-3a64-413c-80d0-db96cb3f1f3b · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Reproducible scaling laws for contrastive language-image learning

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This paper cites Enhancing Semi-supervised Learning with Zero-shot Pseudolabels.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Enhancing Semi-supervised Learning with Zero-shot Pseudolabels

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Observation f9ca760e-2be8-45d6-9b07-fcabb5183d80 · outbound

This paper cites Describing textures in the wild.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Describing textures in the wild

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Observation 85be580a-d1e6-4c2f-8d29-0a3b26ddbc24 · outbound

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

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Imagenet: A large-scale hierarchical image database

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This paper cites Pseudo-labeling based practical semi-supervised meta-training for few-shot learning.IEEE Transactions on Image Processing (TIP), 2024.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Pseudo-labeling based practical semi-supervised meta-training for few-shot learning.IEEE Transactions on Image Processing (TIP), 2024

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This paper cites An image is worth 16x16 words: Transform- ers for image recognition at scale.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective An image is worth 16x16 words: Transform- ers for image recognition at scale

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This paper cites SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning

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Observation a1248a2f-ccfe-47a7-905e-a743ea4e9096 · outbound

This paper cites Erasing the bias: Fine-tuning founda- tion models for semi-supervised learning.Forty-first Interna- tional Conference on Machine Learning (ICML), 2024.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Erasing the bias: Fine-tuning founda- tion models for semi-supervised learning.Forty-first Interna- tional Conference on Machine Learning (ICML), 2024

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

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Clip- adapter: Better vision-language models with feature adapters

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This paper cites On calibration of modern neural networks.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective On calibration of modern neural networks

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This paper cites Class-imbalanced semi- supervised learning with adaptive thresholding.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Class-imbalanced semi- supervised learning with adaptive thresholding

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Deep residual learning for image recognition

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Momentum contrast for unsupervised visual repre- sentation learning

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This paper cites Introducing eurosat: A novel dataset and deep learning benchmark for land use and land cover clas- sification.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Introducing eurosat: A novel dataset and deep learning benchmark for land use and land cover clas- sification

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Deep anomaly detection with outlier exposure

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Distilling the Knowledge in a Neural Network

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This paper cites The curious case of neural text degeneration.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective The curious case of neural text degeneration

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Pseudo-loss confidence metric for semi-supervised few- shot learning

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Retrieval-enhanced contrastive vision-text models

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Categorical reparam- eterization with gumbel-softmax

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Scaling up visual and vision-language representa- tion learning with noisy text supervision

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Opengan: Open-set recog- nition via open data generation

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective 3d object representations for fine-grained categorization

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Temporal ensembling for semi- supervised learning

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Learning to self-train for semi-supervised few-shot classification.Advances in Neural Information Processing Systems (NeurIPS), 2019

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Multimodality helps unimodality: Cross- modal few-shot learning with multimodal models

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Semi- supervised few-shot learning via multi-factor clustering

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Learning customized visual models with retrieval-augmented knowledge

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Few-shot recognition via stage-wise retrieval-augmented fine- tuning

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Tenet: Beyond pseudo-labeling for semi-supervised few-shot learn- ing.Machine Intelligence Research, pages 1–13, 2025

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Revisiting few-shot object detection with vision-language models

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Fine-Grained Visual Classification of Aircraft

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Long-tail learning via logit adjustment

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Unresolved cited work

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective The neglected tails in vision-language models

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Learning transferable visual models from natural language supervision

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Im- proved zero-shot classification by adapting vlms with text 10 descriptions

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Laion-5b: An open large-scale dataset for training next gen- eration image-text models.Advances in Neural Information Processing Systems (NeurIPS), 2022

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective A closer look at the few-shot adaptation of large vision-language models

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Fixmatch: Simpli- fying semi-supervised learning with consistency and confi- dence.Advances in Neural Information Processing Systems (NeurIPS), 2020

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective The Semi-Supervised iNaturalist-Aves Challenge at FGVC7 Workshop

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective A real- istic evaluation of semi-supervised learning for fine-grained classification

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Unresolved cited work

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Observation 4a9afe3c-3bbe-43f6-b331-209ffd5b50a7 · outbound

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective A closer look at the robustness of contrastive language-image pre-training (clip)

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Observation f207a2b8-192f-4f5a-a6c5-6f9a3cb76c57 · outbound

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective An empirical study into what matters for calibrating vision-language models

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Observation fdeca76f-1829-435d-b00f-198d2d95c0e8 · outbound

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Neural priming for sample-efficient adaptation

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Observation c7137a49-7384-491a-b4bb-9349bd344071 · outbound

This paper cites Normface: L2 hypersphere embedding for face verifi- cation.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Normface: L2 hypersphere embedding for face verifi- cation

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Observation d8773510-abad-45e3-a91a-01c36a33f4b4 · outbound

This paper cites Enabling val- idation for robust few-shot recognition.arXiv preprint arXiv:2506.04713, 2025.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Enabling val- idation for robust few-shot recognition.arXiv preprint arXiv:2506.04713, 2025

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Observation 114256ee-15ad-4eb1-8125-c3c150c21dea · outbound

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

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Debiased learning from naturally imbalanced pseudo-labels

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Observation 95f88d44-1924-49a7-a859-b61aba964c5e · outbound

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective USB: A unified semi-supervised learning benchmark for classification

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Observation 25fb76cf-573b-46b1-b4e7-9f2796989ffc · outbound

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Freematch: Self-adaptive thresholding for semi-supervised learning

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Observation 087e82eb-fc46-465c-a564-d962d5e34d03 · outbound

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Towards realistic long-tailed semi- supervised learning: Consistency is all you need

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective An embarrassingly simple approach to semi-supervised few-shot learning.Advances in Neural Information Process- ing Systems (NeurIPS), 2022

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Robust fine-tuning of zero-shot models

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Observation fa663c69-33e3-4d55-936d-181f80099046 · outbound

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Unsupervised feature learning via non-parametric instance discrimination

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Observation 67e7d1de-8245-4066-9514-e8510a4af749 · outbound

This paper cites Self-training with noisy student improves imagenet clas- sification.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Self-training with noisy student improves imagenet clas- sification

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Observation 09d23c76-3583-4545-acd1-d3ad0eeb0750 · outbound

This paper cites Demysti- fying clip data.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Demysti- fying clip data

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Observation 3f3502cb-f7ef-4d9f-b6a7-bcb8e64e3794 · outbound

This paper cites Dash: Semi-supervised learning with dynamic thresholding.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Dash: Semi-supervised learning with dynamic thresholding

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Observation bb38ec88-570a-4f25-8b17-f46adbb43243 · outbound

This paper cites Unimatch v2: Pushing the limit of semi-supervised semantic segmenta- tion.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Unimatch v2: Pushing the limit of semi-supervised semantic segmenta- tion

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Observation bd506807-94ac-49c2-8b2e-9a4524639e5c · outbound

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Task residual for tuning vision-language models

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Observation 85438b39-4bb0-41e5-b869-7ac982fe46d4 · outbound

This paper cites Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling.Advances in Neural Information Processing Systems (NeurIPS), 2021.

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Observation 4d4a7296-ce93-45fa-8e67-60f6c656bc3a · outbound

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective En- hancing vision-language few-shot adaptation with negative learning

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Observation f81f267a-131a-4631-a700-c2c264498d06 · outbound

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

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Observation 5640e4f8-1330-451d-82dc-0c4e8d7c7218 · outbound

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Revisiting semi-supervised learning in the era of foundation models

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Observation 50bfb3ca-2c4c-4e65-9f79-2540ccf37c96 · outbound

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Revisiting semi-supervised learning in the era of foundation models

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Observation 12c15386-ed5a-45bf-b05d-7d8f75804758 · outbound

This paper cites Revisiting semi-supervised learning in the era of foun- dation models.Advances in Neural Information Processing Systems (NeurIPS), 2025.

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Observation 2fc51c2e-660c-4d7c-a03d-9981d9ba225a · outbound

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Tip- adapter: Training-free adaptation of clip for few-shot classifi- cation

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Observation e4e30573-92e4-4c45-bbf2-13cac5b01150 · outbound

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Simmatch: Semi-supervised learning with similarity matching

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Observation dd28173c-75e7-467e-a569-cb7b9a55fb77 · outbound

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Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective string- matching

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Observation d8c5969b-bb85-4b8b-a37b-b09e11f743c4 · outbound

This paper cites flat softmax probabilities.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective flat softmax probabilities

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Observation 4bb5b2c3-ac84-4ca9-9d5e-a167b352111c · outbound

This paper cites Specifically, for both FSL and SSL, we initialize the classifier via linear probing on few-shot data per Tab.

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective Specifically, for both FSL and SSL, we initialize the classifier via linear probing on few-shot data per Tab

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Observation 64be7eea-7101-4e37-babd-0c6a7ab958c3 · outbound

This paper cites We compare the performance with finetuning DINOv2 directly using few-shot data (FS-FT) following [41].

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective We compare the performance with finetuning DINOv2 directly using few-shot data (FS-FT) following [41]

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