Context Propagation from Proposals for Semantic Video Object Segmentation

TL;DR

This paper introduces a novel method for semantic video object segmentation that leverages semantic contexts derived from object proposals and propagates them across frames to improve segmentation accuracy.

Contribution

It proposes a new approach to learn and propagate semantic contexts from object proposals for enhanced video object segmentation.

Findings

Improves robustness in resolving visual ambiguities.

Outperforms state-of-the-art methods in experiments.

Effectively models spatio-temporal object relationships.

Abstract

In this paper, we propose a novel approach to learning semantic contextual relationships in videos for semantic object segmentation. Our algorithm derives the semantic contexts from video object proposals which encode the key evolution of objects and the relationship among objects over the spatio-temporal domain. This semantic contexts are propagated across the video to estimate the pairwise contexts between all pairs of local superpixels which are integrated into a conditional random field in the form of pairwise potentials and infers the per-superpixel semantic labels. The experiments demonstrate that our contexts learning and propagation model effectively improves the robustness of resolving visual ambiguities in semantic video object segmentation compared with the state-of-the-art methods.