Object detection or localization is an incremental step in progression from coarse to fine
digital image inference. It not only provides the classes of the image objects, but also …

Image segmentation is a key task in computer vision and image processing with important
applications such as scene understanding, medical image analysis, robotic perception …

In this paper, we aim to design an efficient real-time object detector that exceeds the YOLO
series and is easily extensible for many object recognition tasks such as instance …

Current semantic segmentation methods focus only on mining" local" context, ie,
dependencies between pixels within individual images, by context-aggregation modules …

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 …

Prevalent state-of-the-art instance segmentation methods fall into a query-based scheme, in
which instance masks are derived by querying the image feature using a set of instance …

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 …

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 …

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

In this paper, we introduce an anchor-box free and single shot instance segmentation
method, which is conceptually simple, fully convolutional and can be used by easily …