  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.
