Co-salient object detection with iterative purification and predictive optimization

Q1 Computer Science
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引用次数: 0

Abstract

Background

Co-salient object detection (Co-SOD) aims to identify and segment commonly salient objects in a set of related images. However, most current Co-SOD methods encounter issues with the inclusion of irrelevant information in the co-representation. These issues hamper their ability to locate co-salient objects and significantly restrict the accuracy of detection.

Methods

To address this issue, this study introduces a novel Co-SOD method with iterative purification and predictive optimization (IPPO) comprising a common salient purification module (CSPM), predictive optimizing module (POM), and diminishing mixed enhancement block (DMEB).

Results

These components are designed to explore noise-free joint representations, assist the model in enhancing the quality of the final prediction results, and significantly improve the performance of the Co-SOD algorithm. Furthermore, through a comprehensive evaluation of IPPO and state-of-the-art algorithms focusing on the roles of CSPM, POM, and DMEB, our experiments confirmed that these components are pivotal in enhancing the performance of the model, substantiating the significant advancements of our method over existing benchmarks. Experiments on several challenging benchmark co-saliency datasets demonstrate that the proposed IPPO achieves state-of-the-art performance.
通过迭代净化和预测优化进行共轴物体检测
背景显著性物体检测(Co-SOD)旨在识别和分割一组相关图像中的共同显著性物体。然而,目前大多数共相关对象检测方法都会遇到在共呈现中包含无关信息的问题。方法为了解决这一问题,本研究引入了一种新型的共同突出物检测方法,该方法具有迭代净化和预测优化(IPPO)功能,包括共同突出物净化模块(CSPM)、预测优化模块(POM)和递减混合增强块(DMEB)。结果这些组件旨在探索无噪声联合表征,协助模型提高最终预测结果的质量,并显著提高 Co-SOD 算法的性能。此外,通过对 IPPO 和最先进算法的全面评估,重点关注 CSPM、POM 和 DMEB 的作用,我们的实验证实了这些组件在提高模型性能方面的关键作用,从而证实了我们的方法比现有基准有了显著的进步。在几个具有挑战性的基准共锯齿数据集上进行的实验证明,所提出的 IPPO 达到了最先进的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Virtual Reality  Intelligent Hardware
Virtual Reality Intelligent Hardware Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
6.40
自引率
0.00%
发文量
35
审稿时长
12 weeks
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