Negative visual attention bias, a core pathological feature of depression, is crucial for its diagnosis and treatment. Related research has become a cutting-edge focus in the cognitive neuroscience of depression. This article focuses on the diagnostic and therapeutic value of negative visual attention bias as a key pathological target in depression, systematically reviewing the paradigm shifts, key advances, and current challenges in this research field driven by artificial intelligence (AI) over the past three years. First, we outline the basic concepts of negative visual attention bias and its central role in the pathogenesis of depression. Next, we focus on three breakthrough directions driven by AI: dynamic modeling of neural mechanisms, multimodal AI assessment techniques, and innovative AI-assisted precision intervention strategies. We also provide an in-depth analysis of the technical limitations and standardization challenges currently facing AI-driven research. Finally, we explore future developments, such as the development of closed-loop precision diagnosis and treatment systems integrating multidimensional data from dynamic brain networks, multi-omics, and digital phenotyping. This review of existing research aims to provide both theoretically insightful and technologically cutting-edge insights for deepening our understanding of the neural mechanisms of negative visual attention bias and promoting the clinical translation of precision diagnosis and treatment for depression.

Salient object detection, as a key research direction in the field of computer vision, is also one of the hotspots of academic research. In this paper, we systematically sort out the research methods, challenges and future development directions in this field. First, the development of salient object detection is summarized, as well as its wide application in the field of computer vision. Second, a detailed review of salient object detection methods is provided, covering detection methods based on saliency features as well as those under the deep learning framework. Third, salient object detection methods based on traditional convolutional neural networks and full convolutional neural networks, as well as salient object detection methods based on the attention mechanism, are discussed in depth, and commonly used datasets and evaluation metrics in the field of salient object detection are introduced. Again, the article summarizes and analyzes the current challenges of salient object detection, such as the limitations of existing datasets and the detection accuracy in complex scenes. Finally, it looks forward to the future development direction of salient object detection. Through this review, this article aims to provide a comprehensive and in-depth reference for researchers engaged in salient object detection in order to promote the further development of this field.