103 citations · 213 across the 6 of their papers we have counts for
4 papers · 1 filter
Hidden in plain sight: VLMs overlook their visual representations
Stephanie Fu, Tyler Bonnen, Devin Guillory +1
Language provides a natural interface to specify and evaluate performance on visual tasks. To realize this possibility, vision language models (VLMs) must successfully integrate vi…
Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning
Colorado J. Reed, Ritwik Gupta, Shufan Li +7
Large, pretrained models are commonly finetuned with imagery that is heavily augmented to mimic different conditions and scales, with the resulting models used for various tasks wi…
Deep Mixture of Experts via Shallow Embedding
Xin Wang, Fisher Yu, Lisa Dunlap +5
Larger networks generally have greater representational power at the cost of increased computational complexity. Sparsifying such networks has been an active area of research but h…
SkipNet: Learning Dynamic Routing in Convolutional Networks
Xin Wang, Fisher Yu, Zi-Yi Dou +2
While deeper convolutional networks are needed to achieve maximum accuracy in visual perception tasks, for many inputs shallower networks are sufficient. We exploit this observatio…