Automatic video analysis from urban surveillance cameras is a fast-emerging field based on
computer vision techniques. We present here a comprehensive review of the state-of-the-art …

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 …

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 …

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 …

Current 3D object detection methods are heavily influenced by 2D detectors. In order to
leverage architectures in 2D detectors, they often convert 3D point clouds to regular grids …

Estimating the 6D pose of known objects is important for robots to interact with the real
world. The problem is challenging due to the variety of objects as well as the complexity of a …

In this paper we introduce a new method for text detection in natural images. The method
comprises two contributions: First, a fast and scalable engine to generate synthetic images …

In this paper, we present ViP-DeepLab, a unified model attempting to tackle the long-
standing and challenging inverse projection problem in vision, which we model as restoring …