Specifically, we first design a text-related feature enhancement module by incorporating the prior knowledge of the text shape to enhance the feature representations. After that, based on the enhanced features, we employ a region proposal network to generate the candidate boxes of scene texts. For...
2.随着高级特征在卷积网络的传递导致逐渐稀释的问题 提出的方法 采用了四个模块 Feature Interweaved Aggregation (FIA) modules,有效地集成低级外观特征,高级语义特征和全局上下文特征。并以有监督方式生成显著图。 Head Attention (HA) module,利用空间和... 查看原文 显著性目标检测之Global Context-Aware Progressive...
首先,对于高层次和低层次特征的聚合,作者提出了特征交织聚合模块(FIAM: Feature Interweaved Aggregation Module),具体结构如下: 首先,低层次的特征先经过一个 1*1 的卷积压缩其特征,之后高层次特征分支经过或不经过卷积与压缩后低特征相乘,另一条分支则采用每个阶段经过全局网络得到的全局特征相乘。最后,连接三个优化...
Feature Interweaved AggregationBenefitsCombine low-level features and high-level features. 取长补短Additionally use global context information to help understand the relationship between different objects (ping-pong ball for example), which is beneficial in generate more complete and accurate saliency map....
it can be classified as sensed (susceptible to sensing errors), static (system or user provided, potentially wrong), profiled (based on user interaction, with uncertainty about the interaction correctness), or derived (employing context aggregation mechanisms, which may be imperfect) (Henricksen and...
Pre-processing and aggregation of sensors' data can provide information that is more complex. In context-aware healthcare monitoring scenarios, there are interesting factors that could be measured, such as: • User's profile(s). Name, gender, age, social and health situation or patient ...
Moreover, to better capture scale-aware features, Context-aware Aggregation module adaptively harnesses features from different scales for a more comprehensive feature representation. Extensive experiments illustrate that our proposed approach achieves state-of-the-art results on PASCAL VOC and MS COCO ...
It is the result of a combination of low-level context and virtual context processing. It uses various sources of information and combines physical and virtual sensors with additional information to solve higher tasks. For example, the “high-speed vehicle” context is an aggregation of the follow...
This aggregation used a local multi-head mask on the atoms that constitute each residue (S = 64, Nhead = 4). Finally, we employed a multi-layer perceptron in the last module, which used three layers of hidden size (S = 64) to decode the state of all ...
can be used to make effective and efficient decision-making capabilities in different context-aware test cases for smartphones. Several machine learning and data mining techniques, such as contextual data clustering, feature optimization and selection, rule-based classification and association analysis, in...