SODAWideNet -- Salient Object Detection with an Attention augmented Wide Encoder Decoder network without ImageNet pre-training

• URL: http://arxiv.org/abs/2311.04828v2
• Date: Thu, 9 Nov 2023 01:49:28 GMT
• Title: SODAWideNet -- Salient Object Detection with an Attention augmented Wide Encoder Decoder network without ImageNet pre-training
• Authors: Rohit Venkata Sai Dulam and Chandra Kambhamettu
• Abstract summary: We explore developing a neural network from scratch directly trained on Salient Object Detection without ImageNet pre-training. We propose SODAWideNet, an encoder-decoder-style network for Salient Object Detection. Two variants, SODAWideNet-S (3.03M) and SODAWideNet (9.03M), achieve competitive performance against state-of-the-art models on five datasets.
• Score: 3.66237529322911
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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