CRAUM-Net: Contextual Recursive Attention with Uncertainty Modeling for Salient Object Detection

TL;DR

CRAUM-Net introduces a novel saliency detection framework that combines multi-scale context aggregation, advanced attention mechanisms, and uncertainty modeling to produce accurate, boundary-aware saliency maps with confidence estimates.

Contribution

The paper proposes a new framework integrating recursive attention, multi-scale context fusion, and uncertainty estimation for improved salient object detection.

Findings

Outperforms existing methods on standard SOD benchmarks.

Effectively captures fine details and boundaries in saliency maps.

Provides reliable uncertainty estimates alongside saliency predictions.

Abstract

Salient Object Detection (SOD) plays a crucial role in many computer vision applications, requiring accurate localization and precise boundary delineation of salient regions. In this work, we present a novel framework that integrates multi-scale context aggregation, advanced attention mechanisms, and an uncertainty-aware module for improved SOD performance. Our Adaptive Cross-Scale Context Module effectively fuses features from multiple levels, leveraging Recursive Channel Spatial Attention and Convolutional Block Attention to enhance salient feature representation. We further introduce an edge-aware decoder that incorporates a dedicated Edge Extractor for boundary refinement, complemented by Monte Carlo Dropout to estimate uncertainty in predictions. To train our network robustly, we employ a combination of boundary-sensitive and topology-preserving loss functions, including Boundary IoU, Focal Tversky, and Topological Saliency losses. Evaluation metrics such as uncertainty-calibrated error and Boundary F1 score, along with the standard SOD metrics, demonstrate our method's superior ability to produce accurate and reliable saliency maps. Extensive experiments validate the effectiveness of our approach in capturing fine-grained details while quantifying prediction confidence, advancing the state-of-the-art in salient object detection.