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 …

Few-shot object detection (FSOD), with the aim to detect novel objects using very few
training examples, has recently attracted great research interest in the community. Metric …

In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

Few-shot object detection has been extensively investigated by incorporating meta-learning
into region-based detection frameworks. Despite its success, the said paradigm is still …

Few-shot object detection (FSOD) aims to detect objects using only a few examples. How to
adapt state-of-the-art object detectors to the few-shot domain remains challenging. Object …

The objective of this paper is few-shot object detection (FSOD)-the task of expanding an
object detector for a new category given only a few instances as training. We introduce a …

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 …

Learning to detect novel objects with a few instances is challenging. A particularly
challenging but practical regime is the extremely-low-shot regime (less than three training …

Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …