Deep/Machine Learning in Visual Recognition and Anomaly Detection

Dear Colleagues，

The success of machine learning and deep neural networks have facilitated advances in understanding the high-level semantics of visual content. Conventional learning-based visual semantic recognition approaches rely heavily on large-scale training data with dense annotations and consistently fail to estimate accurate semantic labels for unseen categories. The emergence and rapid progress of few-/zero-shot learning make it possible to learn unseen categories from a few labeled or even zero-labeled samples, which advances the extension to practical applications. This Special Issue aims to demonstrate (1) how machine learning algorithms have contributed, and are contributing, to new theories, models, and datasets related to the topic of few-/zero-shot learning; (2) how few-/zero-shot learning can facilitate other tasks such as visual recognition and anomaly detection. The editors hope to collate a group of research results to report the recent developments in the related research topics. In addition, researchers can exchange their innovative ideas on the topic of few-/zero-shot learning in visual recognition and anomaly detection by submitting manuscripts for this Special Issue.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

(1) Theoretical advances and algorithm developments in few-/zero-shot learning;

(2) Useful applications of few-/zero-shot learning in visual recognition and anomaly detection;

(3) New datasets and benchmarks for few-/zero-shot learning in visual recognition and anomaly detection.

We look forward to receiving your contributions.

Dr. Yang Liu
Dr. Jin Li
Guest Editors

Manuscript Submission Information

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• few-/zero-shot learning
• visual recognition
• anomaly detection