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We present Self-Context Adaptation (SeCAt), a self-supervised approach that unlocks few-shot abilities for open-ended classification with small visual language models.
The hungarian method for the assignment problem
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Invariant information clustering for unsupervised image classification and segmentation
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Billion-scale similarity search with GPUs
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Language models are few-shot learners
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Language models are general-purpose interfaces
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Training compute-optimal large language models
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Deep spectral methods: A surprisingly strong baseline for unsupervised semantic segmentation and localization
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Learning transferable visual models from natural language supervision
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Photorealistic text-to-image diffusion models with deep language understanding
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Image as a foreign language: Beit pretraining for all vision and vision-language tasks
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