Abstract
Recently, semantic segmentation has become an emerging research area in computer vision due to a strong demand for autonomous vehicles, robotics, video surveillance, and medical image processing. To address this demand, several real-time semantic segmentation models have been introduced. Relying on existing Deep Convolution Neural networks (DCNNs), these models extract contextual features from the input image and construct the output at the decoder end by simply fusing deep features with shallow features which causes a large semantic gap. However, this large gap causes boundary degeneration and noisy feature effects in the output. To address this issue, we propose a novel architecture, called Feature Scaling Feature Fusion Network (FSFFNet) which alleviates the gap by successively fusing features at consecutive levels in multiple directions. For better dense pixel-level representation, we also employ a feature scaling technique which helps the model assimilate more contextual information from the global features and improves model performance. Our proposed model achieves 71.8% validation accuracy (mIoU) on the Cityscapes dataset whilst having only 1.3M parameters.
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Singha, T., Pham, DS., Krishna, A., Gedeon, T. (2021). A Lightweight Multi-scale Feature Fusion Network for Real-Time Semantic Segmentation. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13109. Springer, Cham. https://doi.org/10.1007/978-3-030-92270-2_17
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