scholarly journals An Improved Deep Mutual-Attention Learning Model for Person Re-Identification

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 358
Author(s):  
Miftah Bedru Jamal ◽  
Jiang Zhengang ◽  
Fang Ming
Keyword(s):  
Feature Extraction ◽  
Similarity Measure ◽  
Adaptive Learning ◽  
Large Scale ◽  
State Of The Art ◽  
Background Illumination ◽  
Vast Number ◽  
Proposed Model ◽  
Art Works ◽  
Human Pose

Person re-identification is the task of matching pedestrian images across a network of non-overlapping camera views. It poses aggregated challenges resulted from random human pose, clutter from the background, illumination variations, and other factors. There has been a vast number of studies in recent years with promising success. However, key challenges have not been adequately addressed and continue to result in sub-optimal performance. Attention-based person re-identification gains more popularity in identifying discriminatory features from person images. Its potential in terms of extracting features common to a pair of person images across the feature extraction pipeline has not been be fully exploited. In this paper, we propose a novel attention-based Siamese network driven by a mutual-attention module decomposed into spatial and channel components. The proposed mutual-attention module not only leads feature extraction to the discriminative part of individual images, but also fuses mutual features symmetrically across pairs of person images to get informative regions common to both input images. Our model simultaneously learns feature embedding for discriminative cues and the similarity measure. The proposed model is optimized with multi-task loss, namely classification and verification loss. It is further optimized by a learnable mutual-attention module to facilitate an efficient and adaptive learning. The proposed model is thoroughly evaluated on extensively used large-scale datasets, Market-1501 and Duke-MTMC-ReID. Our experimental results show competitive results with the state-of-the-art works and the effectiveness of the mutual-attention module.

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

Multistep Flow Prediction on Car-Sharing Systems: A Multi-Graph Convolutional Neural Network with Attention Mechanism

2019 ◽  
Vol 29 (11n12) ◽  
pp. 1727-1740 ◽  
Author(s):  
Hongming Zhu ◽  
Yi Luo ◽  
Qin Liu ◽  
Hongfei Fan ◽  
Tianyou Song ◽  
...  
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Spatial Relations ◽  
Attention Mechanism ◽  
Temporal Relations ◽  
Car Sharing ◽  
Flow Prediction ◽  
Proposed Model ◽  
Decoder Architecture ◽  
Deep Learning Model

Multistep flow prediction is an essential task for the car-sharing systems. An accurate flow prediction model can help system operators to pre-allocate the cars to meet the demand of users. However, this task is challenging due to the complex spatial and temporal relations among stations. Existing works only considered temporal relations (e.g. using LSTM) or spatial relations (e.g. using CNN) independently. In this paper, we propose an attention to multi-graph convolutional sequence-to-sequence model (AMGC-Seq2Seq), which is a novel deep learning model for multistep flow prediction. The proposed model uses the encoder–decoder architecture, wherein the encoder part, spatial and temporal relations are encoded simultaneously. Then the encoded information is passed to the decoder to generate multistep outputs. In this work, specific multiple graphs are constructed to reflect spatial relations from different aspects, and we model them by using the proposed multi-graph convolution. Attention mechanism is also used to capture the important relations from previous information. Experiments on a large-scale real-world car-sharing dataset demonstrate the effectiveness of our approach over state-of-the-art methods.

scholarly journals Local Feature-Aware Siamese Matching Model for Vehicle Re-Identification

2020 ◽  
Vol 10 (7) ◽  
pp. 2474
Author(s):  
Honglie Wang ◽  
Shouqian Sun ◽  
Lunan Zhou ◽  
Lilin Guo ◽  
Xin Min ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Feature Matching ◽  
State Of The Art ◽  
Intelligent Transportation ◽  
The State ◽  
Local Feature ◽  
Public Security ◽  
Matching Model ◽  
Image Deformation

Vehicle re-identification is attracting an increasing amount of attention in intelligent transportation and is widely used in public security. In comparison to person re-identification, vehicle re-identification is more challenging because vehicles with different IDs are generated by a unified pipeline and cannot only be distinguished based on the subtle differences in their features such as lights, ornaments, and decorations. In this paper, we propose a local feature-aware Siamese matching model for vehicle re-identification. A local feature-aware Siamese matching model focuses on the informative parts in an image and these are the parts most likely to differ among vehicles with different IDs. In addition, we utilize Siamese feature matching to better supervise our attention. Furthermore, a perspective transformer network, which can eliminate image deformation, has been designed for feature extraction. We have conducted extensive experiments on three large-scale vehicle re-ID datasets, i.e., VeRi-776, VehicleID, and PKU-VD, and the results show that our method is superior to the state-of-the-art methods.

scholarly journals Distant Supervision for Relation Extraction with Sentence Selection and Interaction Representation

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Tiantian Chen ◽  
Nianbin Wang ◽  
Hongbin Wang ◽  
Haomin Zhan
Keyword(s):  
Large Scale ◽  
Semantic Information ◽  
State Of The Art ◽  
Relation Extraction ◽  
Semantic Features ◽  
Distant Supervision ◽  
Word Level ◽  
Proposed Model ◽  
Relation Prediction ◽  
Better Than

Distant supervision (DS) has been widely used for relation extraction (RE), which automatically generates large-scale labeled data. However, there is a wrong labeling problem, which affects the performance of RE. Besides, the existing method suffers from the lack of useful semantic features for some positive training instances. To address the above problems, we propose a novel RE model with sentence selection and interaction representation for distantly supervised RE. First, we propose a pattern method based on the relation trigger words as a sentence selector to filter out noisy sentences to alleviate the wrong labeling problem. After clean instances are obtained, we propose the interaction representation using the word-level attention mechanism-based entity pairs to dynamically increase the weights of the words related to entity pairs, which can provide more useful semantic information for relation prediction. The proposed model outperforms the strongest baseline by 2.61 in F1-score on a widely used dataset, which proves that our model performs significantly better than the state-of-the-art RE systems.

scholarly journals Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering

2020 ◽  
Vol 34 (05) ◽  
pp. 7651-7658 ◽  
Author(s):  
Yang Deng ◽  
Wai Lam ◽  
Yuexiang Xie ◽  
Daoyuan Chen ◽  
Yaliang Li ◽  
...  
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
State Of The Art ◽  
Reading Difficulties ◽  
Text Summarization ◽  
Essential Information ◽  
Joint Learning ◽  
Community Question Answering ◽  
Proposed Model ◽  
Correlation Information

Community question answering (CQA) gains increasing popularity in both academy and industry recently. However, the redundancy and lengthiness issues of crowdsourced answers limit the performance of answer selection and lead to reading difficulties and misunderstandings for community users. To solve these problems, we tackle the tasks of answer selection and answer summary generation in CQA with a novel joint learning model. Specifically, we design a question-driven pointer-generator network, which exploits the correlation information between question-answer pairs to aid in attending the essential information when generating answer summaries. Meanwhile, we leverage the answer summaries to alleviate noise in original lengthy answers when ranking the relevancy degrees of question-answer pairs. In addition, we construct a new large-scale CQA corpus, WikiHowQA, which contains long answers for answer selection as well as reference summaries for answer summarization. The experimental results show that the joint learning method can effectively address the answer redundancy issue in CQA and achieves state-of-the-art results on both answer selection and text summarization tasks. Furthermore, the proposed model is shown to be of great transferring ability and applicability for resource-poor CQA tasks, which lack of reference answer summaries.

scholarly journals 3D Human Pose Estimation via Explicit Compositional Depth Maps

2020 ◽  
Vol 34 (07) ◽  
pp. 12378-12385
Author(s):  
Haiping Wu ◽  
Bin Xiao
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Superior Performance ◽  
Projection Model ◽  
Depth Maps ◽  
3D Camera ◽  
Camera Perspective ◽  
Monocular Image ◽  
Human Pose ◽  
Body Joints

In this work, we tackle the problem of estimating 3D human pose in camera space from a monocular image. First, we propose to use densely-generated limb depth maps to ease the learning of body joints depth, which are well aligned with image cues. Then, we design a lifting module from 2D pixel coordinates to 3D camera coordinates which explicitly takes the depth values as inputs, and is aligned with camera perspective projection model. We show our method achieves superior performance on large-scale 3D pose datasets Human3.6M and MPI-INF-3DHP, and sets the new state-of-the-art.

scholarly journals Shared Generative Latent Representation Learning for Multi-View Clustering

2020 ◽  
Vol 34 (04) ◽  
pp. 6688-6695
Author(s):  
Ming Yin ◽  
Weitian Huang ◽  
Junbin Gao
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Poor Performance ◽  
Representation Learning ◽  
Performance Criteria ◽  
Data Share ◽  
Proposed Model ◽  
Nonlinear Features ◽  
Fundamental Research ◽  
Mixture Of Gaussian

Clustering multi-view data has been a fundamental research topic in the computer vision community. It has been shown that a better accuracy can be achieved by integrating information of all the views than just using one view individually. However, the existing methods often struggle with the issues of dealing with the large-scale datasets and the poor performance in reconstructing samples. This paper proposes a novel multi-view clustering method by learning a shared generative latent representation that obeys a mixture of Gaussian distributions. The motivation is based on the fact that the multi-view data share a common latent embedding despite the diversity among the various views. Specifically, benefitting from the success of the deep generative learning, the proposed model can not only extract the nonlinear features from the views, but render a powerful ability in capturing the correlations among all the views. The extensive experimental results on several datasets with different scales demonstrate that the proposed method outperforms the state-of-the-art methods under a range of performance criteria.

scholarly journals Multi-Matching Network for Multiple Choice Reading Comprehension

2019 ◽  
Vol 33 ◽  
pp. 7088-7095 ◽  
Author(s):  
Min Tang ◽  
Jiaran Cai ◽  
Hankz Hankui Zhuo
Keyword(s):  
Reading Comprehension ◽  
Correct Answer ◽  
Large Scale ◽  
State Of The Art ◽  
Multiple Choice ◽  
Semantic Relationship ◽  
Matching Network ◽  
Empirical Results ◽  
Proposed Model ◽  
Machine Reading

Multiple-choice machine reading comprehension is an important and challenging task where the machine is required to select the correct answer from a set of candidate answers given passage and question. Existing approaches either match extracted evidence with candidate answers shallowly or model passage, question and candidate answers with a single paradigm of matching. In this paper, we propose Multi-Matching Network (MMN) which models the semantic relationship among passage, question and candidate answers from multiple different paradigms of matching. In our MMN model, each paradigm is inspired by how human think and designed under a unified compose-match framework. To demonstrate the effectiveness of our model, we evaluate MMN on a large-scale multiple choice machine reading comprehension dataset (i.e. RACE). Empirical results show that our proposed model achieves a significant improvement compared to strong baselines and obtains state-of-the-art results.

scholarly journals Identifying Malaria Infection in Red Blood Cells using Optimized Step-Increase Convolutional Neural Network Model

2019 ◽  
Vol 8 (9S) ◽  
pp. 813-818 ◽  
Keyword(s):  
Neural Network ◽  
Malaria Infection ◽  
State Of The Art ◽  
Machine Learning Techniques ◽  
Vast Number ◽  
Learning Techniques ◽  
Proposed Model ◽  
Blood Smears ◽  
Network Approaches

A vast number of image processing and neural network approaches are currently being utilized in the analysis of various medical conditions. Malaria is a disease which can be diagnosed by examining blood smears. But when it is examined manually by the microscopist, the accuracy of diagnosis can be error-prone because it depends upon the quality of the smear and the expertise of microscopist in examining the smears. Among the various machine learning techniques, convolutional neural networks (CNN) promise relatively higher accuracy. We propose an Optimized Step-Increase CNN (OSICNN) model to classify red blood cell images taken from thin blood smear samples into infected and non-infected with the malaria parasite. The proposed OSICNN model consists of four convolutional layers and is showing comparable results when compared with other state of the art models. The accuracy of identifying parasite in RBC has been found to be 98.3% with the proposed model.

scholarly journals A Deep Learning Enabled Multi-Class Plant Disease Detection Model Based on Computer Vision

AI ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 413-428
Author(s):  
Arunabha M. Roy ◽  
Jayabrata Bhaduri
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Large Scale ◽  
Plant Disease ◽  
State Of The Art ◽  
Plant Diseases ◽  
Disease Detection ◽  
Detection Model ◽  
Proposed Model ◽  
Apple Plant

In this paper, a deep learning enabled object detection model for multi-class plant disease has been proposed based on a state-of-the-art computer vision algorithm. While most existing models are limited to disease detection on a large scale, the current model addresses the accurate detection of fine-grained, multi-scale early disease detection. The proposed model has been improved to optimize for both detection speed and accuracy and applied to multi-class apple plant disease detection in the real environment. The mean average precision (mAP) and F1-score of the detection model reached up to 91.2% and 95.9%, respectively, at a detection rate of 56.9 FPS. The overall detection result demonstrates that the current algorithm significantly outperforms the state-of-the-art detection model with a 9.05% increase in precision and 7.6% increase in F1-score. The proposed model can be employed as an effective and efficient method to detect different apple plant diseases under complex orchard scenarios.

Sign in / Sign up

Export Citation Format

Share Document