Few-shot image classification is a challenging problem that aims to achieve the human level
of recognition based only on a small number of training images. One main solution to few …

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 …

We propose to address the problem of few-shot classification by meta-learning" what to
observe" and" where to attend" in a relational perspective. Our method leverages relational …

Few-shot semantic segmentation aims at learning to segment a target object from a query
image using only a few annotated support images of the target class. This challenging task …

The focus of recent meta-learning research has been on the development of learning
algorithms that can quickly adapt to test time tasks with limited data and low computational …

Multi-level feature fusion is a fundamental topic in computer vision for detecting, segmenting
and classifying objects at various scales. When multi-level features meet multi-modal cues …

Existing metric learning losses can be categorized into two classes: pair-based and proxy-
based losses. The former class can leverage fine-grained semantic relations between data …

Learning with limited data is a key challenge for visual recognition. Many few-shot learning
methods address this challenge by learning an instance embedding function from seen …

We show that the way inference is performed in few-shot segmentation tasks has a
substantial effect on performances--an aspect often overlooked in the literature in favor of …

Metric learning aims to learn a highly discriminative model encouraging the embeddings of
similar classes to be close in the chosen metrics and pushed apart for dissimilar ones. The …