Coarse2Fine: A Two-stage Training Method for Fine-grained Visual Classification
This work addresses the challenge of distinguishing visually similar objects in fine-grained classification, which is important for applications like species identification or product categorization, but it appears incremental as it builds on existing attention models.
The authors tackled the problem of fine-grained visual classification by proposing Coarse2Fine, a two-stage training method for visual attention networks that improves localization of discriminative local features, achieving state-of-the-art accuracies on common tasks.
Small inter-class and large intra-class variations are the main challenges in fine-grained visual classification. Objects from different classes share visually similar structures and objects in the same class can have different poses and viewpoints. Therefore, the proper extraction of discriminative local features (e.g. bird's beak or car's headlight) is crucial. Most of the recent successes on this problem are based upon the attention models which can localize and attend the local discriminative objects parts. In this work, we propose a training method for visual attention networks, Coarse2Fine, which creates a differentiable path from the input space to the attended feature maps. Coarse2Fine learns an inverse mapping function from the attended feature maps to the informative regions in the raw image, which will guide the attention maps to better attend the fine-grained features. We show Coarse2Fine and orthogonal initialization of the attention weights can surpass the state-of-the-art accuracies on common fine-grained classification tasks.