Due to its automatic feature learning ability and high performance, deep learning has
gradually become the mainstream of artificial intelligence in recent years, playing a role in …

Despite its astounding success in learning deeper multi-dimensional data, the performance
of deep learning declines on new unseen tasks mainly due to its focus on same-distribution …

Recent self-supervised pretraining methods for object detection largely focus on pretraining
the backbone of the object detector, neglecting key parts of detection architecture. Instead …

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 …

Labeling data is often expensive and time-consuming, especially for tasks such as object
detection and instance segmentation, which require dense labeling of the image. While few …

Few-shot object detection aims at detecting novel objects with only a few annotated
examples. Prior works have proved meta-learning a promising solution, and most of them …

Abstract Model-agnostic meta-learning (MAML) is one of the most successful meta-learning
algorithms. It has a bi-level optimization structure where the outer-loop process learns a …

Few-shot object detection, the problem of modelling novel object detection categories with
few training instances, is an emerging topic in the area of few-shot learning and object …

Recently, meta-learning provides a powerful paradigm to deal with the few-shot learning
problem. However, existing meta-learning approaches ignore the prior fact that good meta …

Incremental few-shot object detection aims at detecting novel classes without forgetting
knowledge of the base classes with only a few labeled training data from the novel classes …