The majority of current salient object detection (SOD) models are focused on designing a series of decoders based on fully convolutional networks (FCNs) or Transformer architectures and integrating them in a skillful manner. These models have achieved remarkable high performance and made significant contributions to the development of SOD. Their primary research objective is to develop novel algorithms that can outperform state-of-the-art models, a task that is extremely difficult and time-consuming. In contrast, this paper proposes a positive feedback method based on F-measure value for SOD, aiming to improve the accuracy of saliency prediction using existing methods. Specifically, our proposed method takes an image to be detected and inputs it into several existing models to obtain their respective prediction maps. These prediction maps are then fed into our positive feedback method to generate the final prediction result, without the need for careful decoder design or model training. Moreover, our method is adaptive and can be implemented based on existing models without any restrictions. Experimental results on five publicly available datasets show that our proposed positive feedback method outperforms the latest 12 methods in five evaluation metrics for saliency map prediction. Additionally, we conducted a robustness experiment, which shows that when at least one good prediction result exists in the selected existing model, our proposed approach can ensure that the prediction result is not worse. Our approach achieves a prediction speed of 20 frames per second (FPS) when evaluated on a low configuration host and after removing the prediction time overhead of inserted models. These results highlight the effectiveness, efficiency, and robustness of our proposed approach for salient object detection.

翻译：当前主流的显著目标检测模型主要关注于设计基于全卷积网络或Transformer架构的一系列解码器，并以巧妙方式进行集成。这些模型已取得显著的高性能表现，为显著目标检测的发展作出重要贡献。其主要研究目标是开发能够超越现有最先进模型的新算法，这项任务极为困难且耗时。与此不同，本文提出一种基于F-measure值的正反馈显著目标检测方法，旨在利用现有方法提升显著性预测的精度。具体而言，所提方法将待检测图像输入若干现有模型获得各自预测图，随后将这些预测图输入正反馈方法生成最终预测结果，无需精心设计解码器或进行模型训练。此外，该方法具有自适应性，可基于现有模型无限制地实现。在五个公开数据集上的实验结果表明，所提正反馈方法在显著性图预测的五项评估指标上均优于最新12种方法。同时，我们进行了鲁棒性实验，结果表明当所选现有模型中至少存在一个良好预测结果时，所提方法能确保预测结果不恶化。在去除插入模型预测耗时后，该方法在低配置主机上可实现每秒20帧的预测速度。这些结果充分证明了所提方法在显著目标检测中的有效性、高效性和鲁棒性。