… We extend this idea to few-shot classification in this work. Our adaptive attention module
is somewhat similar to the way of getting object localization maps in [36], but their work was …

… It will hinder the correct classification using the metric learner. Compared with the results
in Fig. 9(b) and 9(d), it indicates that the proposed method shows the better feature extraction …

… In this article, we proposed a few-shot … few-shot detector concentrates on the relations that
lie in the level of objects instead of the full image with the assistance of self-adaptive attention …

… In this work, we propose a novel Cross Attention Network to address the challenging
problems in few-shot classification. Firstly, Cross Attention Module is introduced to deal with the …

… for few-shot … a classification task with K classes, \(W=[{\boldsymbol{c}}_0, {\boldsymbol{c}}_1,...,{\boldsymbol{c}}_K]\)
is used as the linear classifier, and an input sample is classified via …

… classification of the query set samples. The experimental results validate the effectiveness
of our method for Few-shot scene classification in … image scene classification using bag of …

… Few-shot learning (FSL) uses prior knowledge and supervised experience to effectively
classify … However, existing few-shot methods ignore the correlation between cross-domain …

… We evaluate our method on four popular few-shot classification benchmark datasets, ie,
miniImageNet [61], tieredImageNet [53], CIFAR-FS [6], and FC100 [51]. We follow the common …

… Bai, and JE Fowler, “Computationally lightweight hyperspectral image classification using
a multiscale depthwise convolutional network with channel attention,” IEEE Geoscience and …

… sample is classified through a nearest-neighbor classifier using cosine similarity. Likewise,
prototypical network [25] also learns a metric rule to conduct few-shot classification over …