Semantic segmentation is the pixel-wise labeling of an image. Boosted by the extraordinary
ability of convolutional neural networks (CNN) in creating semantic, high-level and …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Point cloud segmentation is fundamental in understanding 3D environments. However,
current 3D point cloud segmentation methods usually perform poorly on scene boundaries …

Abstract We present Boundary IoU (Intersection-over-Union), a new segmentation
evaluation measure focused on boundary quality. We perform an extensive analysis across …

We present a new method for efficient high-quality image segmentation of objects and
scenes. By analogizing classical computer graphics methods for efficient rendering with over …

It is challenging to accurately detect camouflaged objects from their highly similar
surroundings. Existing methods mainly leverage a single-stage detection fashion, while …

High resolution of global land cover dynamic is indicative for understanding the influence of
anthropogenic activity on environmental change. However, most of the land cover products …

This paper exploits the intrinsic features of urban-scene images and proposes a general add-
on module, called height-driven attention networks (HANet), for improving semantic …

We present FasterSeg, an automatically designed semantic segmentation network with not
only state-of-the-art performance but also faster speed than current methods. Utilizing neural …

In deep CNN based models for semantic segmentation, high accuracy relies on rich spatial
context (large receptive fields) and fine spatial details (high resolution), both of which incur …