必应词典为您提供high-levelfeatures的释义,网络释义: 高阶结构化特征;高阶结构化特徵;高阶特徵;
hight-level feature:是建立在low level feature之上的,可以用于图像中目标或物体形状的识别和检测,具有更丰富的语义信息。 通常卷积神经网络中都会使用这两种类型的features:卷积神经网络的前几层学习Low level feature, 后几层学习的是high level feature. Quora上面也有这么一段解释: Low-level features are minor d...
hight-level feature:是建立在low level feature之上的,可以用于图像中目标或物体形状的识别和检测,具有更丰富的语义信息。 通常卷积神经网络中都会使用这两种类型的features:卷积神经网络的前几层学习Low level feature, 后几层学习的是high level feature. Quora上面也有这么一段解释: Low-level features are minor ...
High-Level Featuresdoi:10.1007/978-3-319-17885-1_100558Feature Extraction, Abstract
Recent advances in saliency detection have utilized deep learning to obtain high level features to detect salient regions in a scene. These advances have demonstrated superior results over previous works that utilize hand-crafted low level features for saliency detection. In this paper, we demonstrate...
Deep learning has emerged as a powerful technique to extract high-level features from low-level information, which shows that hierarchical representation can be easily achieved. However, applying deep learning into 3D shape is still a challenge. In this paper, we propose a novel high-level feature...
The best feature representation for these activity recognition datasets lay on the upper layers which represent more high-level features. For this dataset, the DL-alone model with three blocks achieves similar accuracy to the hybrid model, but this occurs only for this dataset. Figure 3 shows ...
在语言模型中,公路(highway)作为一种结合单词级别特征(word-level feature)和字符级别局部特征(character-level local features;)的方法。当使用两层公路的时候,改进很小(the improvements in perplexity were reported),毕竟,公路可以被看作为不同阶段转换的特征的特征结合。本实验我们用的是highway层Sigm-HW(本文没...
the emergence of UNet9has led to the rapid development of medical image segmentation based on deep learning, and the skip-connection part of UNet can make it possible to fuse the low-level and high-level features, which is very crucial for medical image segmentation with high detail requirement...
This paper addresses the challenge of Multimedia Event Detection by proposing a novel method for high-level and low-level features fusion based on collective classification. Generally, the method consists of three steps: training a classifier from low-level features; encoding high-level features into...