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 …

Recent advances in label assignment in object detection mainly seek to independently
define positive/negative training samples for each ground-truth (gt) object. In this paper, we …

Few-shot object detection, which aims at detecting novel objects rapidly from extremely few
annotated examples of previously unseen classes, has attracted significant research interest …

Emerging interests have been brought to recognize previously unseen objects given very
few training examples, known as few-shot object detection (FSOD). Recent researches …

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 …

Detecting objects and estimating their viewpoints in images are key tasks of 3D scene
understanding. Recent approaches have achieved excellent results on very large …

Learning object detection from few examples recently emerged to deal with data-limited
situations. While most previous works merely focus on the performance on few-shot …

Few-shot object detection is an imperative and long-lasting problem due to the inherent long-
tail distribution of real-world data. Its performance is largely affected by the data scarcity of …

Conventional deep learning based methods for object detection require a large amount of
bounding box annotations for training, which is expensive to obtain such high quality …