Deep/machine Learning In Visual Recognition And Anomaly Detection

This article discusses the advancements in few-/zero-shot learning, which allows machines to learn from a few or even zero labeled samples. It highlights the potential for practical applications and the impact on other tasks such as visual recognition and anomaly detection. The article also calls for submissions on related research topics and welcomes original research articles and reviews.