FOCUS: Towards Universal Foreground Segmentation

2025-01-09

AI Summary

To address task fragmentation, insufficient background semantic modeling, and ambiguous boundary delineation in generic foreground segmentation, this paper proposes the first unified framework explicitly incorporating background semantics. Methodologically, we design an edge-guided multi-scale semantic network to enhance boundary awareness; introduce a multimodal contrastive distillation mechanism that integrates vision-language priors to strengthen foreground-background semantic relationships; and develop a unified decoder enabling seamless multi-task compatibility. Evaluated across 13 diverse datasets and five foreground segmentation tasks—including portrait, object, shadow, reflection, and transparent object segmentation—our approach consistently surpasses single-task state-of-the-art methods. It achieves significant average improvements across standard metrics, demonstrating both strong generalization capability and technical advancement in unified foreground segmentation.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

Foreground segmentation is a fundamental task in computer vision, encompassing various subdivision tasks. Previous research has typically designed task-specific architectures for each task, leading to a lack of unification. Moreover, they primarily focus on recognizing foreground objects without effectively distinguishing them from the background. In this paper, we emphasize the importance of the background and its relationship with the foreground. We introduce FOCUS, the Foreground ObjeCts Universal Segmentation framework that can handle multiple foreground tasks. We develop a multi-scale semantic network using the edge information of objects to enhance image features. To achieve boundary-aware segmentation, we propose a novel distillation method, integrating the contrastive learning strategy to refine the prediction mask in multi-modal feature space. We conduct extensive experiments on a total of 13 datasets across 5 tasks, and the results demonstrate that FOCUS consistently outperforms the state-of-the-art task-specific models on most metrics.

Problem

• Image Segmentation
• Object Recognition
• Foreground-Background Discrimination

Innovation

• FOCUS method
• Edge information utilization
• Enhanced learning strategy