8 papers · 1 filter
Changing Modalities: Adapting Remote Sensing Models to New Satellites and Sensors
Tim G. Zhou, Anthony Fuller, Geoff Pleiss +1
Machine learning models for remote sensing are trained and deployed on a static set of modalities. However, as we equip newer satellites with novel sensors and retire old ones, pra…
No One Knows the State of the Art in Geospatial Foundation Models
Isaac Corley, Nils Lehmann, Caleb Robinson +6
Geospatial foundation models (GFMs) have been proposed as generalizable backbones for disaster response, land-cover mapping, food-security monitoring, and other high-stakes Earth-o…
LookWhen? Fast Video Recognition by Learning When, Where, and What to Compute
Ali Salamatian, Anthony Fuller, Pritam Sarkar +3
Transformers dominate video recognition. They split videos into tokens, and processing them has expensive superlinear computational cost. Yet videos are filled with redundancy, so…
Self-Distillation of Hidden Layers for Self-Supervised Representation Learning
Scott C. Lowe, Anthony Fuller, Sageev Oore +2
The landscape of self-supervised learning (SSL) is currently dominated by generative approaches (e.g. MAE) that reconstruct raw low-level data, and predictive approaches (e.g. I-JE…
Thicker and Quicker: A Jumbo Token for Fast Plain Vision Transformers
Anthony Fuller, Yousef Yassin, Daniel G. Kyrollos +2
ViTs are general and accurate, and address many tasks, but ViTs are slow, and are not always practical when efficiency is key. Existing methods for faster ViTs design hybrid non-Vi…
LookWhere? Efficient Visual Recognition by Learning Where to Look and What to See from Self-Supervision
Anthony Fuller, Yousef Yassin, Junfeng Wen +4
Vision transformers are ever larger, more accurate, and more expensive to compute. The expense is even more extreme at high resolution as the number of tokens grows quadratically w…