This source explores in-context learning for large vision models, a novel approach for model adaptation that avoids parameter updates by incorporating domain-specific input-output pairs, known as in-context examples or prompts, alongside test data. The authors highlight that the selection of these examples significantly impacts downstream performance. To address this, they propose a prompt retrieval framework that automates the selection process through unsupervised and supervised methods. Their experiments demonstrate that these methods, especially the supervised one, consistently outperform random selection across various computer vision tasks, indicating the framework's potential for black-box adaptation in visual foundation models.keepSave to notecopy_alldocsAdd noteaudio_magic_eraserAudio OverviewflowchartMind Map

Ver todos los episodios