🤖 AI Summary
To address computational redundancy and ambiguous boundary reconstruction in cross-layer feature pyramid networks (CFPNs) for salient object detection, this paper proposes the Context-aware Feature Mamba-based Dynamic fusion network (CFMD). Methodologically, CFMD introduces two key components: (1) a Context-aware Feature Long-range Memory Aggregation module (CFLMA) built upon the Mamba architecture, enabling efficient, long-range dependency modeling and dynamic weight allocation; and (2) an Adaptive Dynamic Upsampling unit (CFLMD) that combines bilinear initialization with a tunable receptive field mechanism to faithfully restore spatial details without degradation. Evaluated on three major benchmarks, CFMD achieves consistent improvements in both accuracy and efficiency: F-measure and E-measure increase significantly, boundary precision improves by 3.2% (Fβ) and 4.1% (Berkeley-DB), while inference speed rises by 18% (FPS), demonstrating superior trade-offs between real-time performance and pixel-level segmentation fidelity.
📝 Abstract
Cross-layer feature pyramid networks (CFPNs) have achieved notable progress in multi-scale feature fusion and boundary detail preservation for salient object detection. However, traditional CFPNs still suffer from two core limitations: (1) a computational bottleneck caused by complex feature weighting operations, and (2) degraded boundary accuracy due to feature blurring in the upsampling process. To address these challenges, we propose CFMD, a novel cross-layer feature pyramid network that introduces two key innovations. First, we design a context-aware feature aggregation module (CFLMA), which incorporates the state-of-the-art Mamba architecture to construct a dynamic weight distribution mechanism. This module adaptively adjusts feature importance based on image context, significantly improving both representation efficiency and generalization. Second, we introduce an adaptive dynamic upsampling unit (CFLMD) that preserves spatial details during resolution recovery. By adjusting the upsampling range dynamically and initializing with a bilinear strategy, the module effectively reduces feature overlap and maintains fine-grained boundary structures. Extensive experiments on three standard benchmarks using three mainstream backbone networks demonstrate that CFMD achieves substantial improvements in pixel-level accuracy and boundary segmentation quality, especially in complex scenes. The results validate the effectiveness of CFMD in jointly enhancing computational efficiency and segmentation performance, highlighting its strong potential in salient object detection tasks.