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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

AI-enabled systems in security, autonomous systems, safety, and healthcare do not only need to effectively detect Outof- Distribution (OoD) samples, but also to recognize Objects of Concern (OoC), e.g. multiple thorax diseases, efficiently with fewshots. 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 previouslyunseen, 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.
Original languageEnglish
Title of host publication2022 26th International Conference on Pattern Recognition (ICPR)
PublisherIEEE Xplore
Number of pages8
ISBN (Electronic)978-1-6654-9062-7
ISBN (Print)978-1-6654-9063-4
Publication statusE-pub ahead of print - 29 Nov 2022
Event26TH International Conference on Pattern Recognition - Palais des congrès de Montréal, Montréal Québec, Canada
Duration: 21 Aug 202225 Aug 2022


Conference26TH International Conference on Pattern Recognition
Abbreviated titleICPR 2022
CityMontréal Québec
Internet address


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