no code implementations • 5 Nov 2023 • Huang Zhang, Changshuo Wang, Shengwei Tian, Baoli Lu, Liping Zhang, Xin Ning, Xiao Bai
Point cloud classification is the basis of point cloud analysis, and many deep learning-based methods have been widely used in this task.
no code implementations • 2 May 2023 • Fangjian Lin, Yizhe Ma, Sitong Wu, Long Yu, Shengwei Tian
Recently Transformer has shown good performance in several vision tasks due to its powerful modeling capabilities.
no code implementations • 2 May 2023 • Fangjian Lin, Yizhe Ma, Shengwei Tian
We validate the effectiveness of our method on different datasets and models and surpass previous state-of-the-art methods.
no code implementations • 1 May 2023 • Yizhe Ma, Fangjian Lin, Sitong Wu, Shengwei Tian, Long Yu
We expect that our PRSeg can promote the development of MLP-based decoder in semantic segmentation.
1 code implementation • 21 Sep 2022 • Xiangzuo Huo, Gang Sun, Shengwei Tian, Yan Wang, Long Yu, Jun Long, Wendong Zhang, Aolun Li
A parallel hierarchy of local and global feature blocks is designed to efficiently extract local features and global representations at various semantic scales, with the flexibility to model at different scales and linear computational complexity relevant to image size.
no code implementations • 26 Mar 2022 • Fangjian Lin, Tianyi Wu, Sitong Wu, Shengwei Tian, Guodong Guo
In this work, we focus on fusing multi-scale features from Transformer-based backbones for semantic segmentation, and propose a Feature Selective Transformer (FeSeFormer), which aggregates features from all scales (or levels) for each query feature.
no code implementations • 23 Mar 2022 • Fangjian Lin, Zhanhao Liang, Sitong Wu, Junjun He, Kai Chen, Shengwei Tian
In previous deep-learning-based methods, semantic segmentation has been regarded as a static or dynamic per-pixel classification task, \textit{i. e.,} classify each pixel representation to a specific category.
1 code implementation • 8 Jun 2021 • Sitong Wu, Tianyi Wu, Fangjian Lin, Shengwei Tian, Guodong Guo
Transformers have shown impressive performance in various natural language processing and computer vision tasks, due to the capability of modeling long-range dependencies.
no code implementations • 26 Sep 2020 • Hongfeng You, Long Yu, Shengwei Tian, Xiang Ma, Yan Xing, Xiaojie Ma
To solve the above problems, in this paper, we propose a novel end-to-end semantic segmentation algorithm, DT-Net, and use two new convolution strategies to better achieve end-to-end semantic segmentation of medical images.
no code implementations • 25 Mar 2020 • Hongfeng You, Shengwei Tian, Long Yu, Xiang Ma, Yan Xing, Ning Xin
We use the output feature maps from the multiple max-pooling integration module as the input of the decoder network; the multiscale convolution of each submodule in the decoder network is cross-fused with the feature maps generated by the corresponding multiscale convolution in the encoder network.