图像处理_Human face Dataset(人脸数据集)Human face Dataset(人脸数据集)数据摘要：An animated three-dimensional face showing different facial expressions, acquired using a real-time range camera. The acquisition speed is approximately 3 frames/sec. The database contains 15 expressions of the same face,...
Fig. 1: Overview of image dataset and challenge design. Full size image Fig. 2: Competition results. Full size image Typically, learning algorithms perform well on common classes but perform poorly on rare classes. To encourage an equally distributed classification performance among all the 28 clas...
We illustrate three scenarios in which ActivityNet can be used to compare algorithms for human activity understanding: global video classification,trimmed activity classification and activity detection.2013: UF101, Action recognition datasetUCF101 gives the largest diversity in terms of actions and with ...
Our aim is to provide a large scale high quality dataset,covering a diverse range of human actions, that can be usedfor human action classification, rather than temporal localization.Since the use case is classification, only short clipsof around 10s containing the action are included, and there...
Large-Scale/Diverse Dataset Research Multi-Modality: sensor-vision, sensor-skeleton, sensor-3DPose, Sensor-Motion window selection Generative Model: e.g., cross modality data generation, IMU2Skeleton Handling the NULL-Class problem Open-World, Real-World: complex/non-repetitive activities ...
and returns a probability distribution over all of theCIFAR-10Hclasses. These weights are learned based on minimizing classification loss over the training subset of theCIFAR-10dataset described above. Although not explicitly trained to output human classification probabilities, these models are the most...
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In this article, the state of the art for image classification is analyzed and discussed. Based on the elaborated knowledge, four different approaches will be implemented to successfully extract features out of fashion data. For this purpose, a human-worn fashion dataset with 2567 images was ...
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier - guillaume-chevalier/LSTM-Human-Activity-Recognition
(PReLU-nets), we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset. This is a 26% relative improvement over the ILSVRC 2014 winner (GoogLeNet, 6.66% [29]). To our knowledge, our result is the first to surpass human-level performance (5.1%, [22]) on this ...