Visual semantic segmentation aims at separating a visual sample into diverse blocks with
specific semantic attributes and identifying the category for each block, and it plays a crucial …

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 …

Conventional deep CNN-based segmentation approaches have achieved satisfactory
performance in recent years, however, they are essentially Big Data-driven technologies …

Semantic segmentation is a difficult task in computer vision that have applications in many
scenarios, often as a preprocessing step for a tool. Current solutions are based on Deep …

We study the task of weakly-supervised point cloud semantic segmentation with sparse
annotations (eg, less than 0.1% points are labeled), aiming to reduce the expensive cost of …

Prompt learning is an efficient approach to adapt transformers by inserting learnable set of
parameters into the input and intermediate representations of a pre-trained model. In this …

Few-shot segmentation aims to segment unseen-class objects given only a handful of
densely labeled samples. Prototype learning, where the support feature yields a singleor …

Currently, video semantic segmentation mainly faces two challenges: 1) the demand of
temporal consistency; 2) the balance between segmentation accuracy and inference …

Abstract Deep Metric Learning (DML) serves to learn an embedding function to project
semantically similar data into nearby embedding space and plays a vital role in many …

Generalized Few-shot Semantic Segmentation (GFSS) aims to segment each image pixel
into either base classes with abundant training examples or novel classes with only a …