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Sequential Modeling Enables Scalable Learning for Large Vision Models
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Rob에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Rob 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
We introduce a novel sequential modeling approach which enables learning a Large Vision Model (LVM) without making use of any linguistic data. To do this, we define a common format,"visual sentences", in which we can represent raw images and videos as well as annotated data sources such as semantic segmentations and depth reconstructions without needing any meta-knowledge beyond the pixels. Once this wide variety of visual data (comprising 420 billion tokens) is represented as sequences, the model can be trained to minimize a cross-entropy loss for next token prediction. By training across various scales of model architecture and data diversity, we provide empirical evidence that our models scale effectively. Many different vision tasks can be solved by designing suitable visual prompts at test time.
2023: Yutong Bai, Xinyang Geng, K. Mangalam, Amir Bar, Alan Yuille, Trevor Darrell, Jitendra Malik, Alexei A. Efros
https://arxiv.org/pdf/2312.00785v1.pdf
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2023: Yutong Bai, Xinyang Geng, K. Mangalam, Amir Bar, Alan Yuille, Trevor Darrell, Jitendra Malik, Alexei A. Efros
https://arxiv.org/pdf/2312.00785v1.pdf
298 에피소드
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