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 …

Abstract The Depth-aware Video Panoptic Segmentation (DVPS) is a new challenging
vision problem that aims to predict panoptic segmentation and depth in a video …

This paper presents a unified framework for depth-aware panoptic segmentation (DPS),
which aims to reconstruct 3D scene with instance-level semantics from one single image …

Abstract Video Panoptic Segmentation (VPS) aims to achieve comprehensive pixel-level
scene understanding by segmenting all pixels and associating objects in a video. Current …

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 …

In this paper, we propose a unified panoptic segmentation network (UPSNet) for tackling the
newly proposed panoptic segmentation task. On top of a single backbone residual network …

We introduce MGNet, a multi-task framework for monocular geometric scene understanding.
We define monocular geometric scene understanding as the combination of two known …

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

Panoptic image segmentation is the computer vision task of finding groups of pixels in an
image and assigning semantic classes and object instance identifiers to them. Research in …

Panoptic segmentation has become a new standard of visual recognition task by unifying
previous semantic segmentation and instance segmentation tasks in concert. In this paper …