Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

Image segmentation is usually addressed by training a model for a fixed set of object
classes. Incorporating additional classes or more complex queries later is expensive as it …

We present LSeg, a novel model for language-driven semantic image segmentation. LSeg
uses a text encoder to compute embeddings of descriptive input labels (eg," grass" or" …

Recently few-shot segmentation (FSS) has been extensively developed. Most previous
works strive to achieve generalization through the meta-learning framework derived from …

While deep learning models have become the predominant method for medical image
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …

Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting
unseen classes with only a handful of annotations. Previous methods limited to the semantic …

Prototype learning is extensively used for few-shot segmentation. Typically, a single
prototype is obtained from the support feature by averaging the global object information …

Existing few-shot segmentation methods have achieved great progress based on the
support-query matching framework. But they still heavily suffer from the limited coverage of …

This paper presents a novel cost aggregation network, called Volumetric Aggregation with
Transformers (VAT), for few-shot segmentation. The use of transformers can benefit …

Siamese network based trackers formulate the visual tracking task as a similarity matching
problem. Almost all popular Siamese trackers realize the similarity learning via convolutional …