A Mutual Learning Method for Salient Object Detection with intertwined Multi-Supervision--Revised

2025-09-21

AI Summary

To address incomplete predictions caused by intra-object complexity and boundary ambiguity induced by convolutional/pooling strides in salient object detection (SOD), this paper proposes a multi-task collaborative supervision framework. The method jointly optimizes three complementary tasks—salient object detection, foreground contour extraction, and edge detection—via an interleaved multi-supervision mechanism and a mutual learning module (MLM). By leveraging cross-task feature guidance and consistency constraints, it enhances semantic completeness and boundary localization accuracy. The architecture adopts a shared backbone with task-specific branches and is trained end-to-end using a weighted multi-task loss. Evaluated on seven mainstream benchmarks, the method achieves state-of-the-art performance across key metrics: SOD (MAE, Fβ) and edge detection (ODS, OIS). It notably improves both detection accuracy and contour sharpness, demonstrating superior robustness to object complexity and boundary uncertainty.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Improves incomplete predictions in salient object detection
• Enhances boundary accuracy using multi-task supervision
• Develops mutual learning for better saliency and edge detection

Innovation

• Mutual learning with intertwined multi-supervision training
• Combined foreground contour and edge detection guidance
• Novel mutual learning module with multiple network branches