Salient Object Detection (SOD) with deep learning often requires substantial computational resources and large annotated datasets, making it impractical for resource-constrained applications. Lightweight models address computational demands but typically strive in complex and scarce labeled-data scenarios. Feature Learning from Image Markers (FLIM) learns an encoder's convolutional kernels among image patches extracted from discriminative regions marked on a few representative images, dismissing large annotated datasets, pretraining, and backpropagation. Such a methodology exploits information redundancy commonly found in biomedical image applications. This study presents methods to learn dilated-separable convolutional kernels and multi-dilation layers without backpropagation for FLIM networks. It also proposes a novel network simplification method to reduce kernel redundancy and encoder size. By combining a FLIM encoder with an adaptive decoder, a concept recently introduced to estimate a pointwise convolution per image, this study presents very efficient (named flyweight) SOD models for biomedical images. Experimental results in challenging datasets demonstrate superior efficiency and effectiveness to lightweight models. By requiring significantly fewer parameters and floating-point operations, the results show competitive effectiveness to heavyweight models. These advances highlight the potential of FLIM networks for data-limited and resource-constrained applications with information redundancy.

翻译：基于深度学习的显著目标检测通常需要大量的计算资源和标注数据集，这在资源受限的应用中不切实际。轻量级模型解决了计算需求，但通常在复杂且标注数据稀缺的场景中表现不佳。基于图像标记的特征学习从少量代表性图像上标记的判别区域中提取图像块，学习编码器的卷积核，从而无需大型标注数据集、预训练和反向传播。这种方法利用了生物医学图像应用中常见的信息冗余。本研究提出了无需反向传播为FLIM网络学习空洞可分离卷积核和多空洞层的方法，并提出了一种新颖的网络简化方法以减少核冗余和编码器尺寸。通过将FLIM编码器与自适应解码器（一种最近提出的用于估计每幅图像逐点卷积的概念）相结合，本研究提出了用于生物医学图像的高效（称为轻量级）显著目标检测模型。在具有挑战性的数据集上的实验结果表明，其效率和效果均优于轻量级模型。通过显著减少参数数量和浮点运算量，结果显示其效果可与重量级模型相媲美。这些进展凸显了FLIM网络在具有信息冗余的数据有限和资源受限应用中的潜力。