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Semantic segmentation is an important step in understanding the scene for many practical applications such as autonomous driving. Although Deep Convolutional Neural Networks-based methods have significantly improved segmentation accuracy, small/thin objects remain challenging to segment due to convolutional and pooling operations that result in information loss, especially for small objects. This article presents a novel attention-based method called Across Feature Map Attention (AFMA) to address this challenge. It quantifies the inner-relationship between small and large objects belonging to the same category by utilizing the different feature levels of the original image. The AFMA could compensate for the loss of high-level feature information of small objects and improve the small/thin object segmentation. Our method can be used as an efficient plug-in for a wide range of existing architectures and produces much more interpretable feature representation than former studies. Extensive experiments on eight widely used segmentation methods and other existing small-object segmentation models on CamVid and Cityscapes demonstrate that our method substantially and consistently improves the segmentation of small/thin objects.
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Shengtian Sang
Yuyin Zhou
Md Tauhidul Islam
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stanford University
University of California, Santa Cruz
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Sang et al. (Fri,) studied this question.
www.synapsesocial.com/papers/69d9d9cb8988aeabbe6862fe — DOI: https://doi.org/10.1109/tpami.2022.3211171