Abstract
Though deep learning techniques have made great progress in salient object detection recently, the predicted saliency maps still suffer from incomplete predictions due to the internal complexity of objects and inaccurate boundaries caused by strides in convolution and pooling operations. To alleviate these issues, we propose to train saliency detection networks by exploiting the supervision from not only salient object detection, but also foreground contour detection and edge detection. First, we leverage salient object detection and foreground contour detection tasks in an intertwined manner to generate saliency maps with uniform highlight. Second, the foreground contour and edge detection tasks guide each other simultaneously, thereby leading to precise foreground contour prediction and reducing the local noises for edge prediction. In addition, we develop a novel mutual learning module (MLM) which serves as the building block of our method. Each MLM consists of multiple network branches trained in a mutual learning manner, which improves the performance by a large margin. Extensive experiments on seven challenging datasets demonstrate that the proposed method has delivered state-of-the-art results in both salient object detection and edge detection.
Abstract (translated)
尽管深度学习技术在显著物体检测方面最近取得了巨大进展，预测的显著性图仍然由于物体内部复杂性和卷积及池化操作中的步长导致边界不准确而存在不完整预测的问题。为了缓解这些问题，我们提出通过利用不仅来自显著物体检测、而且来自前景轮廓检测和边缘检测的任务监督来训练显著性检测网络。 首先，我们将显著物体检测和前景轮廓检测任务以交织的方式结合使用，生成具有均匀高亮的显著性图。其次，前景轮廓检测和边缘检测任务相互指导，从而实现精确的前景轮廓预测，并减少边缘预测中的局部噪声。此外，我们开发了一种新颖的互学习模块（MLM），这是方法的基础构建块。每个MLM包含多个以互学习方式训练的网络分支，显著提高了性能。 在七个具有挑战性的数据集上进行的广泛实验表明，所提出的方法在显著物体检测和边缘检测方面均达到了最先进的结果。
URL
https://arxiv.org/abs/2509.21363
