This paper addresses the challenge of low-level saliency cues failing to produce accurate saliency detection results, especially in low-contrast backgrounds with confusing visual appearances. To tackle this issue, the authors propose a multi-context deep learning framework for salient object detection. The framework employs deep Convolutional Neural Networks (CNNs) to model saliency in images, integrating both global and local contexts. Global context is used to model saliency over the full image, while local context is used for more detailed saliency prediction in specific regions. The authors also investigate different pre-training strategies and introduce a task-specific pre-training scheme to enhance the performance of the deep models. The effectiveness of the proposed approach is evaluated on five public datasets, showing significant improvements over state-of-the-art methods. The results demonstrate that the multi-context deep learning framework significantly outperforms single-context models and contemporary deep models, particularly in complex scenes with challenging backgrounds.This paper addresses the challenge of low-level saliency cues failing to produce accurate saliency detection results, especially in low-contrast backgrounds with confusing visual appearances. To tackle this issue, the authors propose a multi-context deep learning framework for salient object detection. The framework employs deep Convolutional Neural Networks (CNNs) to model saliency in images, integrating both global and local contexts. Global context is used to model saliency over the full image, while local context is used for more detailed saliency prediction in specific regions. The authors also investigate different pre-training strategies and introduce a task-specific pre-training scheme to enhance the performance of the deep models. The effectiveness of the proposed approach is evaluated on five public datasets, showing significant improvements over state-of-the-art methods. The results demonstrate that the multi-context deep learning framework significantly outperforms single-context models and contemporary deep models, particularly in complex scenes with challenging backgrounds.