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20242026
most citedAlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data

27 citations · 29 across the 14 of their papers we have counts for

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13 papers · 1 filter

cs.CV2026

LookThere! Sparse Vision by Reinforced Selection

Sreehari Rammohan, Yousef Yassin, Anthony Fuller +3

Vision transformers typically treat every image token as equally important, yet for most tasks in computer vision only a fraction are needed. Adaptive computation methods accelerat…

cs.CV2026

Hedgementation = Hedgerow Segmentation: A Remote Sensing Benchmark

Nathan Senyard, Salem Hamdani, Astrid Zhang +4

We propose Hedgementation: a new benchmark to evaluate machine learning models for hedgerow mapping from remote sensing data at country scale and 10m spatial resolution. We com…

cs.CV2026

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…

cs.CV20261 cited

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…

cs.CV2026

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…

cs.CV2026

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…