Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of an image. This …

Image segmentation is the task of associating pixels in an image with their respective object
class labels. It has a wide range of applications in many industries including healthcare …

Abstract We present MaX-DeepLab, the first end-to-end model for panoptic segmentation.
Our approach simplifies the current pipeline that depends heavily on surrogate sub-tasks …

Convolution exploits locality for efficiency at a cost of missing long range context. Self-
attention has been adopted to augment CNNs with non-local interactions. Recent works …

Many modern object detectors demonstrate outstanding performances by using the
mechanism of looking and thinking twice. In this paper, we explore this mechanism in the …

Bottom-up human pose estimation methods have difficulties in predicting the correct pose for
small persons due to challenges in scale variation. In this paper, we present HigherHRNet: a …

In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic
segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve …

Multi-scale inference is commonly used to improve the results of semantic segmentation.
Multiple images scales are passed through a network and then the results are combined …

In this paper, we propose YOSO, a real-time panoptic segmentation framework. YOSO
predicts masks via dynamic convolutions between panoptic kernels and image feature …

Abstract We propose Clustering Mask Transformer (CMT-DeepLab), a transformer-based
framework for panoptic segmentation designed around clustering. It rethinks the existing …