弱监督显著性目标检测研究进展OA北大核心CSTPCD

Salient object detection aims to accurately detect and locate the most attention-grabbing objects or regions in images or videos,facilitating better object recognition and scene analysis.Despite the effectiveness of fully supervised saliency detection methods,acquiring large pixel-level annotated datasets is challenging and costly.Weakly supervised detection methods utilize relatively easy-to-obtain image-level labels or noisy weak labels to train models,demonstrating good performance in practical applications.This paper comprehensively compares the mainstream methods and applica-tion scenarios of fully supervised and weakly supervised saliency detection methods,and then analyzes the data annota-tion methods using weak labels and their impact on salient object detection.The latest research progress in salient object detection under weakly supervised conditions is reviewed,and the performance of various weakly supervised methods is compared on several public datasets.Finally,the potential applications of weakly supervised saliency detection methods in special fields such as agriculture,medicine and military are discussed,highlighting the existing challenges and future development trends in this research area.