Pluralistic Salient Object Detection
Xuelu Feng, Yunsheng Li, Dongdong Chen, Chunming Qiao, Junsong Yuan,, Lu Yuan, and Gang Hua

TL;DR
This paper introduces a new task called pluralistic salient object detection (PSOD) that generates multiple plausible segmentation masks for images, addressing ambiguity and multiple object saliencies, supported by new datasets and a Mixture-of-Experts model.
Contribution
The paper proposes the first pluralistic SOD framework, new datasets with enhanced annotations and human preferences, and demonstrates its effectiveness through extensive experiments.
Findings
Datasets improve boundary and fine-grained mask quality.
The MOE-based model predicts multiple masks and human preferences.
Experimental results validate the proposed approach's effectiveness.
Abstract
We introduce pluralistic salient object detection (PSOD), a novel task aimed at generating multiple plausible salient segmentation results for a given input image. Unlike conventional SOD methods that produce a single segmentation mask for salient objects, this new setting recognizes the inherent complexity of real-world images, comprising multiple objects, and the ambiguity in defining salient objects due to different user intentions. To study this task, we present two new SOD datasets "DUTS-MM" and "DUS-MQ", along with newly designed evaluation metrics. DUTS-MM builds upon the DUTS dataset but enriches the ground-truth mask annotations from three aspects which 1) improves the mask quality especially for boundary and fine-grained structures; 2) alleviates the annotation inconsistency issue; and 3) provides multiple ground-truth masks for images with saliency ambiguity. DUTS-MQ consists…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Visual Attention and Saliency Detection
