6075 papers with code • 148 benchmarks • 335 datasets Semantic Segmentation is a computer vision task in which the goal is to categorize each pixel in an image into a class or object. The goal is to produce a dense pixel-wise segmentation map of an image, where each pixel is ...
We argue that a study of this kind is crucial to under- stand whether research on semantic segmentation is moving in the right direction. Following the rise of transformer ar- chitectures in computer vision [4,15,29,50], several studies have compared recent self...
Application of thermal imaging in the CM of industrial infrastructure has been the topic of several papers. Different traditional image processing (TIP) methods have been proposed for thermal image segmentation. The proposed methodologies can be categorized into four different groups, namely: region-base...
We address the point cloud semantic segmentation problem through modeling long-range dependencies based on the self-attention mechanism. Existing semantic segmentation models generally focus on local feature aggregation. By comparison, we propose a point attention network (PA-Net) to selectively extract ...
1. Introduction Semantic segmentation aims to segment all objects in- cluding 'things' and 'stuff' in an image and determine their categories. It is a challenging task in computer vision, and serves as a foundation for many higher-level tasks, such as scene understanding [15,34], object ...
Large-scale Unsupervised Semantic Segmentation Empowered by large datasets, e.g., ImageNet, unsupervised learning on large-scale data has enabled significant advances for classification tasks. However, whether the large-scale unsupervised semantic segmentation can be achieved remains unknown. There are ...
In this post, I review the literature on semantic segmentation. Most research on semantic segmentation use natural/real world image datasets. Although the results are not directly applicable to medical images, I review these papers because research on the natural images is much more mature than tha...
The research presented in this paper concerns semantic segmentation of lesions regarding Diabetic Retinopathy. Most of the state-of-the-art papers nowadays use Convolutional Neural Networks, Fully Convolutional Networks, and UNETs, a modified version of Convolutional Neural Networks for segmentation tasks...
Some notes from various research papers machine-learningcomputer-visiondeep-learningobject-detectionsemantic-segmentationobject-countingresearch-paper-sum UpdatedDec 3, 2019 MATLAB Star4 This example shows how to train a semantic segmentation network using deep learning. ...
The results show that with a good segmentation network as a base, a tool can be developed which can support pathologists in the risk stratification of colorectal cancer patients, among other possible uses. We have made the segmentation model available for research use on https://grand-challenge....