12 citations · 22 across the 10 of their papers we have counts for
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cs.CV2024
Understanding the Effect of using Semantically Meaningful Tokens for Visual Representation Learning
Neha Kalibhat, Priyatham Kattakinda, Sumit Nawathe +5
Vision transformers have established a precedent of patchifying images into uniformly-sized chunks before processing. We hypothesize that this design choice may limit models in lea…
cs.LG2024★ 3 cited
Augmentations vs Algorithms: What Works in Self-Supervised Learning
Warren Morningstar, Alex Bijamov, Chris Duvarney +8
We study the relative effects of data augmentations, pretraining algorithms, and model architectures in Self-Supervised Learning (SSL). While the recent literature in this space le…