What leads to generalization of object proposals?

Rui Wang; Dhruv Mahajan; Vignesh Ramanathan

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arXiv · 2008.05700

What leads to generalization of object proposals?

Rui Wang·Dhruv Mahajan·Vignesh Ramanathan

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Abstract

Object proposal generation is often the first step in many detection models. It is lucrative to train a good proposal model, that generalizes to unseen classes. This could help scaling detection models to larger number of classes with fewer annotations. Motivated by this, we study how a detection model trained on a small set of source classes can provide proposals that generalize to unseen classes. We systematically study the properties of the dataset - visual diversity and label space granularity - required for good generalization. We show the trade-off between using fine-grained labels and coarse labels. We introduce the idea of prototypical classes: a set of sufficient and necessary classes required to train a detection model to obtain generalized proposals in a more data-efficient way. On the Open Images V4 dataset, we show that only 25% of the classes can be selected to form such a prototypical set. The resulting proposals from a model trained with these classes is only 4.3% worse than using all the classes, in terms of average recall (AR). We also demonstrate that Faster R-CNN model leads to better generalization of proposals compared to a single-stage network like RetinaNet.

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Rui Wang, Dhruv Mahajan, Vignesh Ramanathan. 2020-08-13. What leads to generalization of object proposals?. https://arxiv.org/abs/2008.05700

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