scholarly journals SASO: Joint 3D semantic‐instance segmentation via multi‐scale semantic association and salient point clustering optimization

2021 ◽  
Author(s):  
Jingang Tan ◽  
Lili Chen ◽  
Kangru Wang ◽  
Jiamao Li ◽  
Xiaolin Zhang
Keyword(s):  
Salient Point ◽  
Semantic Association ◽  
Multi Scale ◽  
Clustering Optimization ◽  
Instance Segmentation

scholarly journals Synergistic Attention for Ship Instance Segmentation in SAR Images

2021 ◽  
Vol 13 (21) ◽  
pp. 4384
Author(s):  
Danpei Zhao ◽  
Chunbo Zhu ◽  
Jing Qi ◽  
Xinhu Qi ◽  
Zhenhua Su ◽  
...  
Keyword(s):  
Target Location ◽  
Feature Fusion ◽  
Sar Images ◽  
Ship Detection ◽  
Multi Scale ◽  
Whole Process ◽  
Optical Images ◽  
Segmentation Methods ◽  
Target Characteristics ◽  
Instance Segmentation

This paper takes account of the fact that there is a lack of consideration for imaging methods and target characteristics of synthetic aperture radar (SAR) images among existing instance segmentation methods designed for optical images. Thus, we propose a method for SAR ship instance segmentation based on the synergistic attention mechanism which not only improves the performance of ship detection with multi-task branches but also provides pixel-level contours for subsequent applications such as orientation or category determination. The proposed method—SA R-CNN—presents a synergistic attention strategy at the image, semantic, and target level with the following module corresponding to the different stages in the whole process of the instance segmentation framework. The global attention module (GAM), semantic attention module(SAM), and anchor attention module (AAM) were constructed for feature extraction, feature fusion, and target location, respectively, for multi-scale ship targets under complex background conditions. Compared with several state-of-the-art methods, our method reached 68.7 AP in detection and 56.5 AP in segmentation on the HRSID dataset, and showed 91.5 AP in the detection task on the SSDD dataset.

scholarly journals Chinese Medical Question Answer Matching based on Interactive Sentence Representation Learning

2020 ◽  
Author(s):  
Xiongtao Cui ◽  
Jungang Han
Keyword(s):  
Semantic Information ◽  
State Of The Art ◽  
Representation Learning ◽  
Semantic Features ◽  
Learning Method ◽  
Open Domain ◽  
Semantic Association ◽  
Performance Deficits ◽  
Multi Scale ◽  
Questions And Answers

Chinese medical question-answer matching is more challenging than the open-domain questionanswer matching in English. Even though the deep learning method has performed well in improving the performance of question-answer matching, these methods only focus on the semantic information inside sentences, while ignoring the semantic association between questions and answers, thus resulting in performance deficits. In this paper, we design a series of interactive sentence representation learning models to tackle this problem. To better adapt to Chinese medical question-answer matching and take the advantages of different neural network structures, we propose the Crossed BERT network to extract the deep semantic information inside the sentence and the semantic association between question and answer, and then combine with the multi-scale CNNs network or BiGRU network to take the advantage of different structure of neural networks to learn more semantic features into the sentence representation. The experiments on the cMedQA V2.0 and cMedQA V1.0 dataset show that our model significantly outperforms all the existing state-of-the-art models of Chinese medical question answer matching.

scholarly journals Exploiting Sentence Embedding for Medical Question Answering

2019 ◽  
Vol 33 ◽  
pp. 938-945 ◽  
Author(s):  
Yu Hao ◽  
Xien Liu ◽  
Ji Wu ◽  
Ping Lv
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Question Answering ◽  
Great Success ◽  
Semantic Matching ◽  
Semantic Association ◽  
Learning Framework ◽  
Multi Scale ◽  
Comparison Results ◽  
Real World Applications

Despite the great success of word embedding, sentence embedding remains a not-well-solved problem. In this paper, we present a supervised learning framework to exploit sentence embedding for the medical question answering task. The learning framework consists of two main parts: 1) a sentence embedding producing module, and 2) a scoring module. The former is developed with contextual self-attention and multi-scale techniques to encode a sentence into an embedding tensor. This module is shortly called Contextual self-Attention Multi-scale Sentence Embedding (CAMSE). The latter employs two scoring strategies: Semantic Matching Scoring (SMS) and Semantic Association Scoring (SAS). SMS measures similarity while SAS captures association between sentence pairs: a medical question concatenated with a candidate choice, and a piece of corresponding supportive evidence. The proposed framework is examined by two Medical Question Answering(MedicalQA) datasets which are collected from real-world applications: medical exam and clinical diagnosis based on electronic medical records (EMR). The comparison results show that our proposed framework achieved significant improvements compared to competitive baseline approaches. Additionally, a series of controlled experiments are also conducted to illustrate that the multi-scale strategy and the contextual self-attention layer play important roles for producing effective sentence embedding, and the two kinds of scoring strategies are highly complementary to each other for question answering problems.

scholarly journals Crop Organ Segmentation and Disease Identification Based on Weakly Supervised Deep Neural Network

Agronomy ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 737 ◽  
Author(s):  
Wu ◽  
Xu
Keyword(s):  
Neural Network ◽  
Low Cost ◽  
Large Data ◽  
Model Parameters ◽  
Memory Space ◽  
Multi Scale ◽  
Low Performance ◽  
Weakly Supervised ◽  
The One ◽  
Instance Segmentation

Object segmentation and classification using the deep convolutional neural network (DCNN) has been widely researched in recent years. On the one hand, DCNN requires large data training sets and precise labeling, which bring about great difficulties in practical application. On the other hand, it consumes a large amount of computing resources, so it is difficult to apply it to low-cost terminal equipment. This paper proposes a method of crop organ segmentation and disease recognition that is based on weakly supervised DCNN and lightweight model. While considering the actual situation in the greenhouse, we adopt a two-step strategy to reduce the interference of complex background. Firstly, we use generic instance segmentation architecture—Mask R-CNN to realize the instance segmentation of tomato organs based on weakly supervised learning, and then the disease recognition of tomato leaves is realized by depth separable multi-scale convolution. Instance segmentation algorithms usually require accurate pixel-level supervised labels, which are difficult to collect, so we propose a weakly supervised instance segmentation assignment to solve this problem. The lightweight model uses multi-scale convolution to expand the network width, which makes the extracted features richer, and depth separable convolution is adopted to reduce model parameters. The experimental results showed that our method reached higher recognition accuracy when compared with other methods, at the same time occupied less memory space, which can realize the real-time recognition of tomato diseases on low-performance terminals, and can be applied to the recognition of crop diseases in other similar application scenarios.

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