Developing a new Salient Object Detection (SOD) model involves selecting an ImageNet pre-trained backbone and creating novel feature refinement modules to use backbone features. However, adding new components to a pre-trained backbone needs retraining the whole network on the ImageNet dataset, which requires significant time. Hence, we explore developing a neural network from scratch directly trained on SOD without ImageNet pre-training. Such a formulation offers full autonomy to design task-specific components. To that end, we propose SODAWideNet, an encoder-decoder-style network for Salient Object Detection. We deviate from the commonly practiced paradigm of narrow and deep convolutional models to a wide and shallow architecture, resulting in a parameter-efficient deep neural network. To achieve a shallower network, we increase the receptive field from the beginning of the network using a combination of dilated convolutions and self-attention. Therefore, we propose Multi Receptive Field Feature Aggregation Module (MRFFAM) that efficiently obtains discriminative features from farther regions at higher resolutions using dilated convolutions. Next, we propose Multi-Scale Attention (MSA), which creates a feature pyramid and efficiently computes attention across multiple resolutions to extract global features from larger feature maps. Finally, we propose two variants, SODAWideNet-S (3.03M) and SODAWideNet (9.03M), that achieve competitive performance against state-of-the-art models on five datasets.

翻译：开发新的显著目标检测模型通常需要选取ImageNet预训练骨干网络并设计新颖的特征精化模块来利用骨干特征。然而，在预训练骨干上添加新组件需要在ImageNet数据集上重新训练整个网络，这需要耗费大量时间。因此，我们探索直接从头训练无需ImageNet预训练的显著目标检测神经网络。这种方案为实现任务特定组件的全自主设计提供了可能。为此，我们提出SODAWideNet——一种用于显著目标检测的编码器-解码器架构网络。我们突破了常见的窄而深卷积模型范式，采用宽而浅的架构，从而构建参数高效的深度神经网络。为实现浅层网络，我们通过组合空洞卷积与自注意力机制从网络起始端增大感受野。据此提出多感受野特征聚合模块（MRFFAM），该模块利用空洞卷积高效获取高分辨率下远距离区域的判别性特征。接着提出多尺度注意力（MSA），通过构建特征金字塔并在多分辨率下高效计算注意力，从更大特征图中提取全局特征。最终，我们提出SODAWideNet-S（3.03M参数）和SODAWideNet（9.03M参数）两个变体，在五个数据集上均取得了与现有最优模型相竞争的性能表现。