S$^3$POT: Contrast-Driven Face Occlusion Segmentation via Self-Supervised Prompt Learning

TL;DR

S$^3$POT introduces a self-supervised, contrast-driven framework that leverages face generation and foundation segmentation models to accurately segment occlusions without requiring annotated occlusion masks.

Contribution

The paper proposes a novel self-supervised approach combining face generation and prompt learning to improve occlusion segmentation in face parsing tasks.

Findings

Outperforms existing methods on a dedicated occlusion dataset

Effectively segments occlusions without annotated masks

Each module contributes to overall performance improvement

Abstract

Existing face parsing methods usually misclassify occlusions as facial components. This is because occlusion is a high-level concept, it does not refer to a concrete category of object. Thus, constructing a real-world face dataset covering all categories of occlusion object is almost impossible and accurate mask annotation is labor-intensive. To deal with the problems, we present S$^3$POT, a contrast-driven framework synergizing face generation with self-supervised spatial prompting, to achieve occlusion segmentation. The framework is inspired by the insights: 1) Modern face generators' ability to realistically reconstruct occluded regions, creating an image that preserve facial geometry while eliminating occlusion, and 2) Foundation segmentation models' (e.g., SAM) capacity to extract precise mask when provided with appropriate prompts. In particular, S$^3$POT consists of three modules: Reference Generation (RF), Feature enhancement (FE), and Prompt Selection (PS). First, a reference image is produced by RF using structural guidance from parsed mask. Second, FE performs contrast of tokens between raw and reference images to obtain an initial prompt, then modifies image features with the prompt by cross-attention. Third, based on the enhanced features, PS constructs a set of positive and negative prompts and screens them with a self-attention network for a mask decoder. The network is learned under the guidance of three novel and complementary objective functions without occlusion ground truth mask involved. Extensive experiments on a dedicatedly collected dataset demonstrate S$^3$POT's superior performance and the effectiveness of each module.