LIP: Learning Instance Propagation for Video Object Segmentation

TL;DR

This paper introduces a novel end-to-end deep neural network combining Mask-RCNN and Conv-GRU for semi-supervised video object segmentation, effectively handling appearance changes, occlusions, and scale variations.

Contribution

It presents a unified model that integrates instance segmentation with a visual memory module for improved video object segmentation without post-processing.

Findings

Achieves competitive results on DAVIS 2016 and 2017 datasets.

Handles multiple objects and occlusions effectively.

No post-processing or synthetic data needed.

Abstract

In recent years, the task of segmenting foreground objects from background in a video, i.e. video object segmentation (VOS), has received considerable attention. In this paper, we propose a single end-to-end trainable deep neural network, convolutional gated recurrent Mask-RCNN, for tackling the semi-supervised VOS task. We take advantage of both the instance segmentation network (Mask-RCNN) and the visual memory module (Conv-GRU) to tackle the VOS task. The instance segmentation network predicts masks for instances, while the visual memory module learns to selectively propagate information for multiple instances simultaneously, which handles the appearance change, the variation of scale and pose and the occlusions between objects. After offline and online training under purely instance segmentation losses, our approach is able to achieve satisfactory results without any post-processing or synthetic video data augmentation. Experimental results on DAVIS 2016 dataset and DAVIS 2017 dataset have demonstrated the effectiveness of our method for video object segmentation task.