Efficient Segmentation: Learning Downsampling Near Semantic Boundaries

By Dmitrii Marin, Zijian He, Péter Vajda, Priyam Chatterjee, Sam S. Tsai, Fei Yang, Yuri Boykov. 2019

109 citations recorded by the source corpus.

Many automated processes such as auto-piloting rely on a good semantic segmentation as a critical component. To speed up performance, it is common to downsample the input frame. However, this comes at the cost of missed small objects and reduced accuracy at semantic boundaries. To address this problem, we propose a new content-adaptive downsampling technique that learns to favor sampling locations near semantic boundaries of target classes. Cost-performance analysis shows that our method consistently outperforms the uniform sampling improving balance between accuracy and computational efficiency. Our adaptive sampling gives segmentation with better quality of boundaries and more reliable support for smaller-size objects.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Dynamic Neural Networks: A Survey (2021, neutral): “Sa & Sp Image segmentation [34], [129], [146], [148], [150], [154], [156], [216], [217], [218], [219], [220], Image-to-image translation [221], Object detection [110], [111], [147], [148], [164], Semantic image synthesis [222], [223], [224], Computer Image denoising [225], Fine-grained classificatio”
FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function (2020, background): “Other work downsamples input non-uniformly [40, 44, 26] to reduce computational cost.”
Variable-rate discrete representation learning (2021, background): “Michel et al. (2017) analyse frame-wise predictions of speech features to determine phoneme boundaries.”
Boundary IoU: Improving Object-Centric Image Segmentation Evaluation (2021, methodology): “For example, Trimap IoU is used as an extra evaluation to show boundary quality improvement [5, 25], but it is not reported by most segmentation methods.”
FasterSeg: Searching for Faster Real-time Semantic Segmentation (2019, background): “More works target on segmentation efﬁciency in terms of computation cost (He et al., 2019; Marin et al., 2019) and memory usage (Chen et al., 2019).”