U$^2$Mamba: A Two-level Nested U-structure Mamba for Salient Object Detection

2026-06-18

AI Summary

This work addresses the limitations of existing Mamba-based salient object detection methods, which struggle to adequately model long-range contextual dependencies and are constrained by network depth. To overcome these issues, we propose a dual-nested U-shaped architecture that enhances local feature extraction through multi-scale Mamba U-blocks and effectively integrates shallow and deep features with diverse receptive fields to capture richer contextual relationships. Furthermore, we introduce a novel hierarchical training supervision mechanism that applies loss constraints at multiple network levels, departing from conventional top-layer-only supervision. Extensive experiments demonstrate that the proposed method achieves state-of-the-art or superior performance on salient object detection benchmarks, validating its effectiveness and architectural advantages.

Abstract

Mamba-based models have emerged as a promising alternative for salient object detection (SOD), offering significant advantages in modeling long sequences. However, existing models often fail to explore contextual information and the depth of the entire architecture. This paper introduces U$^2$Mamba, a powerful and innovative U-structured network for salient object detection. We propose multiscale Mamba U-blocks (MMUBs) that enhance the model depth to improve local feature extraction capabilities. Our newly developed nested U-structure, incorporating MMUBs, enables the network to integrate various receptive fields from shallow and deep layers, thereby collecting richer contextual information and longer-range data without being constrained by resolution. Instead of using the traditional deep supervision scheme and top-level supervised training, we propose a hierarchical training supervision method where the loss is computed at each level during the training process. Extensive experiments demonstrate that U$^2$Mamba achieves highly competitive performance against state-of-the-art methods. The source code is available at \url{https://github.com/JL021/U2Mamba}.

Problem

• salient object detection
• Mamba
• contextual information
• model depth
• receptive fields

Innovation

• Mamba
• salient object detection
• nested U-structure
• multiscale Mamba U-block
• hierarchical supervision