An Anchor-Free Contour-Based Method For Instance Segmentation

Abstract

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Instance segmentation methods can be broadly categorized into two types: mask-based and contour-based. Mask-based methods treat it as a pixel-level classification task, while Contour-based methods consider it a regression problem, predicting object boundary polygons. Regardless of the method, many approaches rely on object detectors to identify candidate bounding boxes, limiting performance to detector capabilities. In this paper, we propose a contour-based, anchor-free instance segmentation approach that eliminates the need to use an anchor box as assistance. Our approach leverages learnable initial contours and employs dynamic convolution to achieve this goal. The dynamic convolution utilizes a generated filter to predict an offset map. This map is then utilized by our deformation module to iteratively increase the number of predicted vertices and gradually refine the initial contours, aligning them with object boundaries and ultimately achieving the desired instance segmentation.