Few-Shot Learning refers to the problem of learning the underlying pattern in the data just
from a few training samples. Requiring a large number of data samples, many deep learning …

Object detection is a fundamental visual recognition problem in computer vision and has
been widely studied in the past decades. Visual object detection aims to find objects of …

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) helps detectors adapt to unseen classes with few training
instances, and is useful when manual annotation is time-consuming or data acquisition is …

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

Conventional methods for object detection typically require a substantial amount of training
data and preparing such high-quality training data is very labor-intensive. In this paper, we …

With the rapid development of deep learning techniques, deep convolutional neural
networks (DCNNs) have become more important for object detection. Compared with …

Resembling the rapid learning capability of human, low-shot learning empowers vision
systems to understand new concepts by training with few samples. Leading approaches …

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 …