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Recent advances in open set recognition: A survey
In real-world recognition/classification tasks, limited by various objective factors, it is usually
difficult to collect training samples to exhaust all classes when training a recognizer or …
difficult to collect training samples to exhaust all classes when training a recognizer or …
Open-world machine learning: A review and new outlooks
Machine learning has achieved remarkable success in many applications. However,
existing studies are largely based on the closed-world assumption, which assumes that the …
existing studies are largely based on the closed-world assumption, which assumes that the …
Towards open vocabulary learning: A survey
In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …
advancements in various core tasks like segmentation, tracking, and detection. However …
Openood: Benchmarking generalized out-of-distribution detection
Abstract Out-of-distribution (OOD) detection is vital to safety-critical machine learning
applications and has thus been extensively studied, with a plethora of methods developed in …
applications and has thus been extensively studied, with a plethora of methods developed in …
Generalized out-of-distribution detection: A survey
Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …
machine learning systems. For instance, in autonomous driving, we would like the driving …
Delving into out-of-distribution detection with vision-language representations
Recognizing out-of-distribution (OOD) samples is critical for machine learning systems
deployed in the open world. The vast majority of OOD detection methods are driven by a …
deployed in the open world. The vast majority of OOD detection methods are driven by a …
Deep long-tailed learning: A survey
Deep long-tailed learning, one of the most challenging problems in visual recognition, aims
to train well-performing deep models from a large number of images that follow a long-tailed …
to train well-performing deep models from a large number of images that follow a long-tailed …
Openood v1. 5: Enhanced benchmark for out-of-distribution detection
Out-of-Distribution (OOD) detection is critical for the reliable operation of open-world
intelligent systems. Despite the emergence of an increasing number of OOD detection …
intelligent systems. Despite the emergence of an increasing number of OOD detection …
Generalized category discovery
In this paper, we consider a highly general image recognition setting wherein, given a
labelled and unlabelled set of images, the task is to categorize all images in the unlabelled …
labelled and unlabelled set of images, the task is to categorize all images in the unlabelled …
A unified survey on anomaly, novelty, open-set, and out-of-distribution detection: Solutions and future challenges
Machine learning models often encounter samples that are diverged from the training
distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently …
distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently …