11 citations · 24 across the 13 of their papers we have counts for
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cs.CV2024
When Do We Not Need Larger Vision Models?
Baifeng Shi, Ziyang Wu, Maolin Mao +2
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 mo…
cs.CV2024
xT: Nested Tokenization for Larger Context in Large Images
Ritwik Gupta, Shufan Li, Tyler Zhu +3
Modern computer vision pipelines handle large images in one of two sub-optimal ways: down-sampling or cropping. These two methods incur significant losses in the amount of informat…
cs.CV2024★ 3 cited
Rethinking Patch Dependence for Masked Autoencoders
Letian Fu, Long Lian, Renhao Wang +6
In this work, we examine the impact of inter-patch dependencies in the decoder of masked autoencoders (MAE) on representation learning. We decompose the decoding mechanism for mask…