Semantic segmentation, or image segmentation, is the task of clustering parts of an image together which belong to the same object class. It is a form of pixel-level prediction because each pixel in an image is classified according to a category. Some example benchmarks for this task are Cityscapes, PASCAL VOC and ADE20K. Models are usually evaluated with the Mean Intersection-Over-Union (Mean IoU) and Pixel Accuracy metrics.
( Image credit: CSAILVision )
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Fruit size and leaf area are important indicators for plant health and are of interest for plant nutrient management, plant protection and harvest.
In this work we address the task of observing the performance of a semantic segmentation deep neural network (DNN) during online operation, i. e., during inference, which is of high importance in safety-critical applications such as autonomous driving.
Tremendous efforts have been made on instance segmentation but the mask quality is still not satisfactory.
We study the problem of labelling effort for semantic segmentation of large-scale 3D point clouds.
Based on an observation that different segmentation algorithms will perform well on different subsets of examples because of the nature and size of training sets they have been exposed to and because of method-intrinsic factors, we propose to measure the confidence in the prediction of each algorithm and then use an associate threshold to determine whether the confidence is acceptable or not.
We propose a two-layer ensemble of deep learning models for the segmentation of medical images.
To address this, we introduce a novel approach for more accurate and efficient spatio-temporal segmentation.
The recent works have either employed hand-crafted geometrical face features or face-level deep convolutional neural network features for face to BMI prediction.