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Found 10 papers, 2 papers with code
Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook
Point cloud classification is the basis of point cloud analysis, and many deep learning-based methods have been widely used in this task.
AxWin Transformer: A Context-Aware Vision Transformer Backbone with Axial Windows
Recently Transformer has shown good performance in several vision tasks due to its powerful modeling capabilities.
Exploring vision transformer layer choosing for semantic segmentation
We validate the effectiveness of our method on different datasets and models and surpass previous state-of-the-art methods.
PRSeg: A Lightweight Patch Rotate MLP Decoder for Semantic Segmentation
We expect that our PRSeg can promote the development of MLP-based decoder in semantic segmentation.
HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification
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.
Feature Selective Transformer for Semantic Image Segmentation
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.
StructToken : Rethinking Semantic Segmentation with Structural Prior
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.
Fully Transformer Networks for Semantic Image Segmentation
Transformers have shown impressive performance in various natural language processing and computer vision tasks, due to the capability of modeling long-range dependencies.
DT-Net: A novel network based on multi-directional integrated convolution and threshold convolution
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.
A New Multiple Max-pooling Integration Module and Cross Multiscale Deconvolution Network Based on Image Semantic Segmentation
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.
