Class-Agnostic Visio-Temporal Scene Sketch Semantic Segmentation
This work solves the problem of segmenting free-hand scene sketches for applications like sketch-to-image retrieval and scene understanding, representing a novel method for a known bottleneck.
The paper tackles the problem of scene sketch semantic segmentation by addressing the loss of temporal stroke order and inability to segment unseen object categories, proposing a class-agnostic visio-temporal network (CAVT) that achieves superior performance over state-of-the-art models on a new dataset.
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.