Weakly Supervised Semantic Segmentation using Web-Crawled Videos

TL;DR

This paper introduces a weakly supervised semantic segmentation method that leverages web-crawled videos to generate pseudo-labels, significantly improving segmentation accuracy without extra human annotation.

Contribution

It presents a novel approach that uses web videos and discriminative localization to generate effective segmentation labels from only image-level supervision.

Findings

Outperforms existing weakly supervised methods

Achieves results comparable to methods with additional annotations

Effectively identifies relevant spatio-temporal video segments

Abstract

We propose a novel algorithm for weakly supervised semantic segmentation based on image-level class labels only. In weakly supervised setting, it is commonly observed that trained model overly focuses on discriminative parts rather than the entire object area. Our goal is to overcome this limitation with no additional human intervention by retrieving videos relevant to target class labels from web repository, and generating segmentation labels from the retrieved videos to simulate strong supervision for semantic segmentation. During this process, we take advantage of image classification with discriminative localization technique to reject false alarms in retrieved videos and identify relevant spatio-temporal volumes within retrieved videos. Although the entire procedure does not require any additional supervision, the segmentation annotations obtained from videos are sufficiently strong to learn a model for semantic segmentation. The proposed algorithm substantially outperforms existing methods based on the same level of supervision and is even as competitive as the approaches relying on extra annotations.