The deep CNNs in image semantic segmentation typically require a large number of
densely-annotated images for training and have difficulties in generalizing to unseen object …

Training semantic segmentation models requires a large amount of finely annotated data,
making it hard to quickly adapt to novel classes not satisfying this condition. Few-Shot …

A trademark is any recognizable sign that identifies products/services and distinguishes
them from others. Many regional and international intellectual property offices are dedicated …

In online costume design, it is vital to preview the design effect rapidly by entangling design
attributes from reference images. This paper proposes a novel method, named design …

The capability of learning and generalizing from very few samples successfully is a
noticeable demarcation separating artificial intelligence and human intelligence. Despite the …

Searching the targets of interest in large-scale remote sensing images is a fundamental
problem, which becomes a very challenging issue when there is no relevant example at …

Deep Learning (DL) provided powerful tools for various visual information analysis and
retrieval tasks, outperforming previously used methods. However, despite the potential of …

Logo detection is a crucial task for industrial cyber-physical systems in smart cities, which
relies on accurate and efficient analysis of urban environments. In this article, we proposed …

Few-shot semantic segmentation has attracted much attention because it requires only a few
labeled samples to achieve good segmentation performance. However, existing methods …

Abstract Existing Few-Shot Learning (FSL) methods learn and recognize new classes with
the help of prior knowledge. However, they cannot handle this task well in a cross-domain …