CTR: Contrastive Training Recognition Classifier for Few-Shot Open-World Recognition

Nikolaos Dionelis, Sotirios A. Tsaftaris, Mehrdad Yaghoobi

Abstract: AI-enabled systems in security, autonomous systems, safety, and healthcare do not only need to effectively detect Out-of-Distribution (OoD) samples, but also to recognize Objects of Concern (OoC), e.g. multiple thorax diseases, efficiently with few-shots. Detecting OoD samples is crucial, because reporting an out-of-domain input as abnormal is better than falsely classifying it. Data samples, during inference, are not confined to a finite labelled set, and thus closed-set approaches are limiting, as they misclassify OoD inputs, and they may assign them high prediction confidence. Furthermore, although anomaly detection is possible, recognizing new OoC fast using only few-shot samples remains challenging. There is a lack of methodologies for joint anomaly detection and few-shot OoC classification. Our contribution is the development of a framework for joint few-shot OoC detection and classification and anomaly detection in the unknown previously-unseen, in the wild, environment, which is known as Open-World Recognition (OWR). We propose a novel methodology, the data distribution boundary Contrastive Training Recognition (CTR) classifier for few-shot OWR. CTR takes advantage of labels and classes to learn the normal (and few-shot abnormal) data better, to more accurately detect OoD. The proposed model: (i) reduces failures to detect anomalies in health- and safety-critical applications for avoiding unfavourable consequences, (ii) decreases false alarms, and (iii) improves performance overall. Our framework differs from existing approaches because: (a) anomaly and OoC detection are combined, which has several benefits, including improved OoD performance, (b) the performance, accuracy, and robustness of OoD and few-shot OoC detection are improved by strengthening the estimation of the normal class distribution at the boundary of its support, self-generating samples and setting them as abnormal, and (c) the knowledge base of models is also augmented by learning class-incrementally, alleviating forgetting. CTR outperforms baselines in several settings, including on the SVHN, CIFAR-FS, and BSCD-FS ChestX and ISIC datasets.