Privileged Pooling: Supervised attention-based pooling for compensating dataset bias
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In this paper we propose a novel supervised image classification method that overcomes dataset bias and scarcity of training data using privileged information in the form of keypoints annotations. Our main motivation is recognition of animal species for ecological applications like biodiversity modelling, which can be challenging due to long-tailed species distributions due to rare species, and strong dataset biases in repetitive scenes such as in camera traps. To counteract these challenges, we propose a weakly-supervised visual attention mechanism that has access to keypoints highlighting the most important object parts. This privileged information, implemented via a novel privileged pooling operation, is only accessible during training and helps the model to focus on the regions that are most discriminative. We show that the proposed approach uses more efficiently small training datasets, generalizes better and outperforms competing methods in challenging training conditions.
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