Xiaoxu Li

TL;DR: In this article, a mutual channel loss (MC-Loss) is proposed for fine-grained image categorization, which consists of two channel-specific components: a discriminality component and a diversity component.

TL;DR: Experimental results on two fine-grained vehicle datasets demonstrate that the CMP modified CNNs can improve the classification accuracies on the task of fine- grained vehicle classification while a massive amount of parameters are reduced.

TL;DR: This work adds a regularization term to the cross-entropy loss and proposes a new loss function, Dual Cross-Entropy Loss, which improves the fine-grained vehicle classification performance and has good performance on three other general image classification tasks.

TL;DR: Experimental results show the superiority of the MC-Loss when implemented on top of common base networks can achieve state-of-the-art performance on all four fine-grained categorization datasets, and ablative studies further demonstrate the supremacy of the loss when compared with other recently proposed general-purpose losses for visual classification.

TL;DR: This short communication gives a concise review on recent representative methods in few-shot meta-learning, which are categorized into four branches according to their technical characteristics.