A Weakly Supervised Semantic Segmentation Network by Aggregating Seed Cues: The Multi-Object Proposal Generation Perspective

As the country vigorously promotes the development of science and technology and tries to enhance independent innovation capabilities, more and more attention is paid on the protection of technology ownership. In recent years, China has developed rapidly in many scientific and technological fields, and the number of patent applications increased year by year. However, various patent quality problems including immature patent technology and low patent authorization rate appear. The indicators of patent quantification and quality evaluation are studied in this paper. First, we quantify the patent quality evaluation indicators and combine the content of the patent text to build a patent evaluation model. US patents with patent grade labels are used for training with multitask learning technology. Second, the evaluation model is transferred from the English patents to the Chinese patents, in which the active learning technology and transfer learning technology are used to minimize the work of manual labeling. Finally, a Chinese patent quality evaluation model based on collaborative training was designed and implemented. Methods used in this experiment have notably improved the prediction effect of the model and achieved a better migration effect. A large number of experimental results show that the Chinese patent quality evaluation model has achieved good evaluation results. This research uses deep learning and natural language processing technology to carry out research on patent quality evaluation models from different perspectives, to provide patent decision support for related companies, and to point out research directions for research institutions and patent inventors.

Section: Experimental Settingsmentioning

confidence: 99%

Applying Deep Learning Technologies to Evaluate the Patent Quality with the Collaborative Training

You

Liu

Wang

et al. 2021

“…Specifically, Res2Net implements multiscale features via splitting channels of feature maps into subgroups and fusing these channel groups hierarchically. It has been proven that Res2Net can boost many backbone networks in some vision tasks, including object detection, semantic segmentation [32,33], and salient object detection [29].…”

Section: High Spatial Resolutionmentioning

confidence: 99%

A Novel Approach of Intelligent Computing for Multiperson Pose Estimation with Deep High Spatial Resolution and Multiscale Features

Wang

Shi

et al. 2021

Currently, human pose estimation (HPE) methods mainly rely on the design framework of Convolutional Neural Networks (CNNs). These CNNs typically consist of high-to-low-resolution subnetworks (encoder) to learn semantic information and low-to-high subnetworks (decoder) to raise the resolution for keypoint localization. Because too low-resolution feature maps in encoder will inevitably lose some spatial information, which cannot be recovered in the upsampling stages, keeping high spatial resolution features is critical for human pose estimation. On the other hand, due to scale variation of human body parts, multiscale features are also very important for human pose estimation. In this paper, a novel backbone network is proposed specifically for HPE, named High Spatial Resolution and Multiscale Networks (HSR-MSNet), which maintain high spatial resolution features in deeper layers of the encoder and meanwhile construct multiscale features within one single residual block via subgroup splitting and fusion of feature maps. Experiments show that our approach outperforms other state-of-the-art methods with more accurate keypoint locations on COCO dataset.

“…The distant supervision hypothesis means that when there is a Wireless Communications and Mobile Computing relationship between two entities, then all sentences containing the pair of entities are considered to express this relationship to some extent. Distant supervision is to provide labels for data with the help of external knowledge bases, to save the trouble of manual labelling [37]. Attributes can also be considered as a type of relationship, so the distant supervision assumption is applied to the annotation of attribute data.…”

Section: Data Annotationmentioning

confidence: 99%

Attribute Extraction Study in the Field of Military Equipment Based on Distant Supervision

You

Yang

Han³

et al. 2021

The effective organization and utilization of military equipment data is an important cornerstone for constructing knowledge system. Building a knowledge graph in the field of military equipment can effectively describe the relationship between entity and entity attribute information. Therefore, relevant personnel can obtain information quickly and accurately. Attribute extraction is an important part of building the knowledge graph. Given the lack of annotated data in the field of military equipment, we propose a new data annotation method, which adopts the idea of distant supervision to automatically build the attribute extraction dataset. We convert the attribute extraction task into a sequence annotation task. At the same time, we propose a RoBERTa-BiLSTM-CRF-SEL-based attribute extraction method. Firstly, a list of attribute name synonyms is constructed, then a corpus of military equipment attributes is obtained through automatic annotation of semistructured data in Baidu Encyclopedia. RoBERTa is used to obtain the vector encoding of the text. Then, input it into the entity boundary prediction layer to label the entity head and tail, and input the BiLSTM-CRF layer to predict the attribute label. The experimental results show that the proposed method can effectively perform attribute extraction in the military equipment domain. The F 1 value of the model reaches 77% on the constructed attribute extraction dataset, which outperforms the current state-of-art model.