CAMV: Class Activation Mapping Value Towards Open Set Fine-Grained Recognition

Abstract

Open Set Fine-Grained Recognition (OSFGR) aims at distinguishing known fine-grained categories from the data (contain known and unknown categories). The feature distribution of the known fine-grained category usually has the characteristics of small inter-class variance and large intra-class variance. Directly using the traditional Open Set Recognition (OSR) method on OSFGR does not achieve competitive performance. This is mainly due to the fact that the traditional OSR method is designed based on SoftMax function, whose translation invariance for input weakens the representation ability for the known fine-grained category in OSFGR. To settle this problem, we present a unified method based on class activation mapping value (CAMV) for OSFGR, which preserves the original discriminate feature. Besides, most OSR methods are based on the discrimination model with multi-class thresholds. When most known categories need to recognize, it will face the high computational cost and low efficiency. To overcome this problem, we propose the discrimination model with fusion one-class thresholds, which can greatly improve the inference speed without losing the recognition accuracy. Meanwhile, our idea can settle the extreme situation of OSR (only one known category). Our method achieves the state-of-art performance on the fine-grained open set datasets consisting of 4 public fine-grained datasets.