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Sep 14, 2022 � The proposed method improves the performance of semantic segmentation, especially for exactly segmenting large objects and confusion categories.
6 days ago � To make denser scales, we propose a superdense-scale network (SDSNet). Specifically, we design a simple yet effective structure named the�...
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Superdense-scale network for semantic segmentation ... Authors: Zhiqiang Li; Jie Jiang; Xi Chen; Honggang Qi; Qingli Li; Jiapeng Liu; Laiwen Zheng; Min Liu�...
The unique advantages of our proposed HRDLNet in the field of semantic segmentation of urban streetscapes are also verified by comparing it with the state-of-�...
How deep neural networks can be used for Semantic Segmentation ? • How to model local and contextual information with Deep Nets ? • Differences and Similarities�...
Nov 12, 2021 � In this work we propose a deep learning model to generate high-resolution segmentation maps from low-resolution inputs in a multi-task approach.
Apr 3, 2024 � Our SDN aims to construct a differentiable mapping from the original feature to the inter-class boundary-enhanced feature. The proposed SDN is�...
Current state-of-the-art semantic segmentation method- s often apply high-resolution input to attain high perfor- mance, which brings large computation�...
The goal is to produce a dense pixel-wise segmentation map of an image, where each pixel is assigned to a specific class or object. Some example benchmarks for�...
Aug 5, 2024 � In this paper, we propose a new semantic segmentation network (HRDLNet) for urban street scene images with high-resolution representation.