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Most existing salient object detection (SOD) models are designed based on data collected in well-lit scenes, which is entirely inadequate for low-light conditions. Although recent models are designed for low-light conditions, they still have limitations. First, they simply integrate features without considering the impact of low-light scenes and fail to enhance the contextual information around salient objects. Second, in extremely dark scenes, it is difficult for the human eye to distinguish between the foreground and background, posing significant challenges for data labeling. To address these issues, we design a brightness Retinex enhancer (BRE) tailored for low-light SOD tasks and, for the first time, explore performing low-light SOD within a semi-supervised framework. By using sparse labeled semantic prompts to augment a large amount of unlabeled data, we mitigate the annotation burden while avoiding ineffective labeling in low-light conditions. More specifically, we first use Retinex decomposition to filter out the influence of illumination, while the semantic features extracted by a large model serve as semantic prompts to assist in enhancement. In addition, we introduce a context-guided encoder (CGE) to improve the model's understanding of salient objects. Finally, both labeled and unlabeled data undergo joint consistency training between the shared decoder (SD) and the perturbation decoder. The semi-supervised model enhances low-light SOD performance while also alleviating the burden of data annotation. Extensive experiments demonstrate that, compared with state-of-the-art fully supervised SOD models, the proposed semi-supervised model achieves highly competitive results across multiple test datasets.
Yu et al. (Fri,) studied this question.