Class-Agnostic Visio-Temporal Scene Sketch Semantic Segmentation

TL;DR

This paper introduces a class-agnostic visio-temporal network for scene sketch segmentation that preserves stroke order and handles unseen categories, supported by a new large annotated dataset.

Contribution

It proposes the first instance and stroke-level segmentation method for scene sketches and introduces the FrISS dataset with dense annotations.

Findings

Outperforms existing scene sketch segmentation models

Effectively segments objects from unseen categories

Provides a large, richly annotated scene sketch dataset

Abstract

Scene sketch semantic segmentation is a crucial task for various applications including sketch-to-image retrieval and scene understanding. Existing sketch segmentation methods treat sketches as bitmap images, leading to the loss of temporal order among strokes due to the shift from vector to image format. Moreover, these methods struggle to segment objects from categories absent in the training data. In this paper, we propose a Class-Agnostic Visio-Temporal Network (CAVT) for scene sketch semantic segmentation. CAVT employs a class-agnostic object detector to detect individual objects in a scene and groups the strokes of instances through its post-processing module. This is the first approach that performs segmentation at both the instance and stroke levels within scene sketches. Furthermore, there is a lack of free-hand scene sketch datasets with both instance and stroke-level class annotations. To fill this gap, we collected the largest Free-hand Instance- and Stroke-level Scene Sketch Dataset (FrISS) that contains 1K scene sketches and covers 403 object classes with dense annotations. Extensive experiments on FrISS and other datasets demonstrate the superior performance of our method over state-of-the-art scene sketch segmentation models. The code and dataset will be made public after acceptance.