Few-shot classification is a challenging problem as only very few training examples are
given for each new task. One of the effective research lines to address this challenge …

Deep learning-based models have been shown to outperform human beings in many
computer vision tasks with massive available labeled training data in learning. However …

Few-shot fine-grained recognition (FS-FGR) aims to distinguish several highly similar
objects from different sub-categories with limited supervision. However, traditional few-shot …

In this paper we reformulate few-shot classification as a reconstruction problem in latent
space. The ability of the network to reconstruct a query feature map from support features of …

In image classification, it is common practice to train deep networks to extract a single
feature vector per input image. Few-shot classification methods also mostly follow this trend …

Semantic segmentation assigns category labels to each pixel in an image, enabling
breakthroughs in fields such as autonomous driving and robotics. Deep Neural Networks …

Few-shot fine-grained recognition (FS-FGR) aims to recognize novel fine-grained categories
with the help of limited available samples. Undoubtedly, this task inherits the main …

Object detection has achieved substantial progress in the last decade. However, detecting
novel classes with only few samples remains challenging, since deep learning under low …

Few-shot learning is a challenging problem since only a few examples are provided to
recognize a new class. Several recent studies exploit additional semantic information, eg …

Most of the few-shot learning methods learn to transfer knowledge from datasets with
abundant labeled data (ie, the base set). From the perspective of class space on base set …