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. 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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Notes
- 1.
We chose [25] due to its simplicity. In practice, we can use other weakly supervised approaches too.
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Wang, R., Mahajan, D., Ramanathan, V. (2020). What Leads to Generalization of Object Proposals?. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12536. Springer, Cham. https://doi.org/10.1007/978-3-030-66096-3_32
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