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When Do We Not Need Larger Vision Models?
March 21, 2024, 4:45 a.m. | Baifeng Shi, Ziyang Wu, Maolin Mao, Xin Wang, Trevor Darrell
cs.CV updates on arXiv.org arxiv.org
Abstract: Scaling up the size of vision models has been the de facto standard to obtain more powerful visual representations. In this work, we discuss the point beyond which larger vision models are not necessary. First, we demonstrate the power of Scaling on Scales (S$^2$), whereby a pre-trained and frozen smaller vision model (e.g., ViT-B or ViT-L), run over multiple image scales, can outperform larger models (e.g., ViT-H or ViT-G) on classification, segmentation, depth estimation, Multimodal …
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