Abstract License Plate Recognition (LPR) is a fairly well explored problem and is already a component of several commer- cially operational systems. Many of these systems, however, require sophisticated video capture hardware possibly com- bined wit
The process of classifying objects is a fundamental fea- ture of most human pursuits, and the idea that people clas- sify together those things that people ¯nd similar is both intuitive and popular across a wide range of disciplines. Es- timation of
Action recognition and human pose estimation are closely related but both problems are generally handled as distinct tasks in the literature. In this work, we pro- pose a multitask framework for jointly 2D and 3D pose estimation from still images an
We introduce new, fine-grained action and emotion recognition tasks defined on non-staged videos, recorded during robot-assisted therapy sessions of children with autism. The tasks present several challenges: a large dataset with long videos, a larg
语音识别LAS结构where d and y, are MLP networks. After training, the a; distribution Table 1: WER comparison on the clean and noisy Google voice
is typically very sharp and focuses on only a few frames of h; ci car
search task. The CLDNN-hMM system is the s
TernausNetV2：用于实例分割的全卷积网络
我们将在为第二名解决方案提供网络定义和权重。
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如果您发现这项工作对您的出版物有用，请考虑引用：
InProceedings{Iglovikov_2018_CVPR_Workshops,
author = {Iglovikov, Vladimir and Seferbekov, Selim and Buslaev, Alexander and Shvets, Alexey},
tit
