Classification-Specific Parts for Improving Fine-Grained Visual Categorization
TL
TL;DR
This work proposes a novel classification-specific part estimation that uses an initial prediction as well as back-propagation of feature importance via gradient computations in order to estimate relevant image regions and shows the effectiveness of the mentioned part selection method in conjunction with the extracted part features.
Abstract
Fine-grained visual categorization is a classification task for distinguishing categories with high intra-class and small inter-class variance. While global approaches aim at using the whole image for performing the classification, part-based solutions gather additional local information in terms of attentions or parts. We propose a novel classification-specific part estimation that uses an initial prediction as well as back-propagation of feature importance via gradient computations in order to estimate relevant image regions. The subsequently detected parts are then not only selected by a-posteriori classification knowledge, but also have an intrinsic spatial extent that is determined automatically. This is in contrast to most part-based approaches and even to available ground-truth part annotations, which only provide point coordinates and no additional scale information. We show in our experiments on various widely-used fine-grained datasets the effectiveness of the mentioned part selection method in conjunction with the extracted part features.
References23 items
1
The Whole Is More Than Its Parts? From Explicit to Implicit Pose Normalization
2
Which and How Many Regions to Gaze: Focus Discriminative Regions for Fine-Grained Visual Categorization
3
Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification From the Bottom Up
4
Unsupervised Part Mining for Fine-grained Image Classification
5