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 …

Humans are able to learn to recognize new objects even from a few examples. In contrast,
training deep-learning-based object detectors requires huge amounts of annotated data. To …

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

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 …

Abstract We design a Kernelized Few-shot Object Detector by leveraging kernelized
matrices computed over multiple proposal regions, which yield expressive non-linear …

As few-shot object detectors are often trained with abundant base samples and fine-tuned
on few-shot novel examples, the learned models are usually biased to base classes and …

Few-shot object detection (FSOD) aims to expand an object detector for novel categories
given only a few instances for training. The few training samples restrict the performance of …

In this paper, we tackle the challenging problem of Few-shot Object Detection. Existing
FSOD pipelines (i) use average-pooled representations that result in information loss; and/or …

Most of existing methods for few-shot object detection follow the fine-tuning paradigm, which
potentially assumes that the class-agnostic generalizable knowledge can be learned and …