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pro vyhledávání: '"Ranzinger, Mike"'
Autor:
Ranzinger, Mike, Barker, Jon, Heinrich, Greg, Molchanov, Pavlo, Catanzaro, Bryan, Tao, Andrew
Various visual foundation models have distinct strengths and weaknesses, both of which can be improved through heterogeneous multi-teacher knowledge distillation without labels, termed "agglomerative models." We build upon this body of work by studyi
Externí odkaz:
http://arxiv.org/abs/2410.01680
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 12490-12500
A handful of visual foundation models (VFMs) have recently emerged as the backbones for numerous downstream tasks. VFMs like CLIP, DINOv2, SAM are trained with distinct objectives, exhibiting unique characteristics for various downstream tasks. We fi
Externí odkaz:
http://arxiv.org/abs/2312.06709