Fine-Grained Species Recognition With Privileged Pooling: Better Sample Efficiency Through Supervised Attention

We propose a scheme that uses privileged information, in the form of keypoint annotations for the training data, to learn strong models from small and/or biased training sets.

Recognition of Unseen Bird Species by Learning from Field Guides

We exploit existing field guides to learn bird species recognition in large scale datasets for zero-shot recognition of unseen species

Counting the uncountable: deep semantic density estimation from space

We propose a new method to count objects of specific categories, including oil palm trees, olive trees and cars. All of these objects are significantly smaller than the ground sampling distance of a satellite image.