Learning to Segment Every Thing

by   Ronghang Hu, et al.

Existing methods for object instance segmentation require all training instances to be labeled with segmentation masks. This requirement makes it expensive to annotate new categories and has restricted instance segmentation models to 100 well-annotated classes. The goal of this paper is to propose a new partially supervised training paradigm, together with a novel weight transfer function, that enables training instance segmentation models over a large set of categories for which all have box annotations, but only a small fraction have mask annotations. These contributions allow us to train Mask R-CNN to detect and segment 3000 visual concepts using box annotations from the Visual Genome dataset and mask annotations from the 80 classes in the COCO dataset. We carefully evaluate our proposed approach in a controlled study on the COCO dataset. This work is a first step towards instance segmentation models that have broad comprehension of the visual world.


page 1

page 3

page 7

page 8


One-Shot Instance Segmentation

We tackle one-shot visual search by example for arbitrary object categor...

Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision

Instance segmentation is an active topic in computer vision that is usua...

Active Pointly-Supervised Instance Segmentation

The requirement of expensive annotations is a major burden for training ...

Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations

Existing instance segmentation models learn task-specific information us...

The surprising impact of mask-head architecture on novel class segmentation

Instance segmentation models today are very accurate when trained on lar...

Iterative Learning for Instance Segmentation

Instance segmentation is a computer vision task where separate objects i...

TACO: Trash Annotations in Context for Litter Detection

TACO is an open image dataset for litter detection and segmentation, whi...

Please sign up or login with your details

Forgot password? Click here to reset