  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.
