轻量级网络中实现多尺度特征提取，精度效率双提升
Lightweight Multi-Scale Feature Extraction with Fully Connected LMF Layer for Salient Object Detection
- 用全连接结构融合空洞卷积，实现高效多尺度特征提取
- 仅0.81M参数，在5个数据集上达到顶尖或接近顶尖性能
- 适合移动端或资源受限场景的显著目标检测任务
在计算机视觉领域，多尺度特征提取对显著目标检测等任务至关重要。然而，轻量级网络在保持高效性的同时实现多尺度能力仍具挑战。本文提出一种新型轻量级多尺度特征提取层——LMF层，采用全连接结构中的深度可分离空洞卷积。通过集成多个LMF层，构建了专用于显著目标检测的轻量级网络LMFNet。该方法显著减少参数量，同时保持优异性能。实验表明，LMFNet仅需0.81M参数，在五个基准数据集上达到当前最优或可比结果，优于多种传统及轻量级模型，在效率与精度上均表现突出。本工作不仅解决了轻量级网络中的多尺度学习难题，也展示了其在图像处理任务中的广泛潜力。相关代码已开源：https://github.com/Shi-Yun-peng/LMFNet
原文摘要 · Abstract (English)
In the domain of computer vision, multi-scale feature extraction is vital for tasks such as salient object detection. However, achieving this capability in lightweight networks remains challenging due to the trade-off between efficiency and performance. This paper proposes a novel lightweight multi-scale feature extraction layer, termed the LMF layer, which employs depthwise separable dilated convolutions in a fully connected structure. By integrating multiple LMF layers, we develop LMFNet, a lightweight network tailored for salient object detection. Our approach significantly reduces the number of parameters while maintaining competitive performance. Here, we show that LMFNet achieves state-of-the-art or comparable results on five benchmark datasets with only 0.81M parameters, outperforming several traditional and lightweight models in terms of both efficiency and accuracy. Our work not only addresses the challenge of multi-scale learning in lightweight networks but also demonstrates the potential for broader applications in image processing tasks. The related code files are available at https://github.com/Shi-Yun-peng/LMFNet
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