ScribbleSeg: Scribble-based Interactive Image Segmentation
Xi Chen, Yau Shing Jonathan Cheung, Ser-Nam Lim, Hengshuang Zhao

TL;DR
ScribbleSeg introduces a new framework for scribble-based interactive image segmentation, with a standardized protocol, a novel training and evaluation strategy, and demonstrates superior performance over click-based methods.
Contribution
The paper formulates a standard protocol for scribble-based segmentation, proposes a new framework with specialized modules, and establishes a challenging benchmark for evaluation.
Findings
ScribbleSeg outperforms previous click-based segmentation methods.
A deterministic scribble generator improves evaluation consistency.
The framework demonstrates robustness across diverse images.
Abstract
Interactive segmentation enables users to extract masks by providing simple annotations to indicate the target, such as boxes, clicks, or scribbles. Among these interaction formats, scribbles are the most flexible as they can be of arbitrary shapes and sizes. This enables scribbles to provide more indications of the target object. However, previous works mainly focus on click-based configuration, and the scribble-based setting is rarely explored. In this work, we attempt to formulate a standard protocol for scribble-based interactive segmentation. Basically, we design diversified strategies to simulate scribbles for training, propose a deterministic scribble generator for evaluation, and construct a challenging benchmark. Besides, we build a strong framework ScribbleSeg, consisting of a Prototype Adaption Module(PAM) and a Corrective Refine Module (CRM), for the task. Extensive…
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Taxonomy
TopicsVisual Attention and Saliency Detection · Advanced Image and Video Retrieval Techniques · Advanced Neural Network Applications
