We present CLUSTSEG, a general, transformer-based framework that tackles different
image segmentation tasks (ie, superpixel, semantic, instance, and panoptic) through a …

Transformers have been at the heart of the Natural Language Processing (NLP) and
Computer Vision (CV) revolutions. The significant success in NLP and CV inspired exploring …

Superpixel segmentation consists of partitioning images into regions composed of similar
and connected pixels. Its methods have been widely used in many computer vision …

RGBT tracking has received more and more attention in recent years, and in this paper, we
propose a multi-task auxiliary learning framework for RGBT tracking. Specifically, we simplify …

Deep learning-based hyperspectral image (HSI) change detection (CD) approaches have a
strong ability to leverage spectral-spatial-temporal information through automatic feature …

This article presents a multi-phase image segmentation methodology based on fuzzy
superpixel decomposition, aggregation and merging. First, a collection of layers of dense …

Superpixel is the over-segmentation region of an image, whose basic units “pixels” have
similar properties. Although many popular seeds-based algorithms have been proposed to …

Limited by storage conditions, the degradation of old photos exhibits complex and diverse
features. Existing image restoration methods heavily rely on features extracted from a single …

Superpixel segmentation divides an original image into mid-level regions to reduce the
number of computational primitives for subsequent tasks. The two-stage approaches work …

To enhance the low clustering accuracy of the fuzzy clustering segmentation algorithm for
analyzing high spatial resolution remote sensing images (HSRRSIs), a deep fuzzy …