Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Deep learning approaches have recently raised the bar in many fields, from Natural
Language Processing to Computer Vision, by leveraging large amounts of data. However …

This paper aims to tackle the challenging problem of one-shot object detection. Given a
query image patch whose class label is not included in the training data, the goal of the task …

Taking time to identify expected products and waiting for the checkout in a retail store are
common scenes we all encounter in our daily lives. The realization of automatic product …

Few-shot instance segmentation methods are promising when labeled training data for
novel classes is scarce. However, current approaches do not facilitate flexible addition of …

Few-shot instance segmentation (FSIS) conjoins the few-shot learning paradigm with
general instance segmentation, which provides a possible way of tackling instance …

Abstract We present an Augmentative Contrastive Learning for One-Shot Object Detection
method that is inspired by the co-attention and co-excitation (CoAE) method. In the One-shot …

Remote sensing ship image detection methods have broad application prospects in areas
such as maritime traffic and fisheries management. However, previous detection methods …