DeepAI AI Chat
Log In Sign Up

Interactive Video Object Segmentation in the Wild

by   Arnaud Benard, et al.

In this paper we present our system for human-in-the-loop video object segmentation. The backbone of our system is a method for one-shot video object segmentation. While fast, this method requires an accurate pixel-level segmentation of one (or several) frames as input. As manually annotating such a segmentation is impractical, we propose a deep interactive image segmentation method, that can accurately segment objects with only a handful of clicks. On the GrabCut dataset, our method obtains 90 average, setting the new state of the art. Furthermore, as our method iteratively refines an initial segmentation, it can effectively correct frames where the video object segmentation fails, thus allowing users to quickly obtain high quality results even on challenging sequences. Finally, we investigate usage patterns and give insights in how many steps users take to annotate frames, what kind of corrections they provide, etc., thus giving important insights for further improving interactive video segmentation.


page 1

page 2

page 4

page 5

page 6


FOMTrace: Interactive Video Segmentation By Image Graphs and Fuzzy Object Models

Common users have changed from mere consumers to active producers of mul...

Gamifying Video Object Segmentation

Video object segmentation can be considered as one of the most challengi...

Quality Control in Crowdsourced Object Segmentation

This paper explores processing techniques to deal with noisy data in cro...

Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps

We propose a novel guided interactive segmentation (GIS) algorithm for v...

Fast Interactive Video Object Segmentation with Graph Neural Networks

Pixelwise annotation of image sequences can be very tedious for humans. ...

Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild

This paper proposes a framework for the interactive video object segment...

R-Clustering for Egocentric Video Segmentation

In this paper, we present a new method for egocentric video temporal seg...