scholarly journals DeepHMap++: Combined Projection Grouping and Correspondence Learning for Full DoF Pose Estimation

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1032 ◽  
Author(s):  
Mingliang Fu ◽  
Weijia Zhou
Keyword(s):  
Pose Estimation ◽  
State Of The Art ◽  
Relevant Literature ◽  
Direct Methods ◽  
Control Points ◽  
Two Stage ◽  
Local Maxima ◽  
Confidence Distribution ◽  
Local Patch ◽  
Public Datasets

In recent years, estimating the 6D pose of object instances with convolutional neural network (CNN) has received considerable attention. Depending on whether intermediate cues are used, the relevant literature can be roughly divided into two broad categories: direct methods and two-stage pipelines. For the latter, intermediate cues, such as 3D object coordinates, semantic keypoints, or virtual control points instead of pose parameters are regressed by CNN in the first stage. Object pose can then be solved by correspondence constraints constructed with these intermediate cues. In this paper, we focus on the postprocessing of a two-stage pipeline and propose to combine two learning concepts for estimating object pose under challenging scenes: projection grouping on one side, and correspondence learning on the other. We firstly employ a local-patch based method to predict projection heatmaps which denote the confidence distribution of projection of 3D bounding box’s corners. A projection grouping module is then proposed to remove redundant local maxima from each layer of heatmaps. Instead of directly feeding 2D–3D correspondences to the perspective-n-point (PnP) algorithm, multiple correspondence hypotheses are sampled from local maxima and its corresponding neighborhood and ranked by a correspondence–evaluation network. Finally, correspondences with higher confidence are selected to determine object pose. Extensive experiments on three public datasets demonstrate that the proposed framework outperforms several state of the art methods.

scholarly journals A New Edge Patch with Rotation Invariance for Object Detection and Pose Estimation

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 887 ◽  
Author(s):  
Xunwei Tong ◽  
Ruifeng Li ◽  
Lianzheng Ge ◽  
Lijun Zhao ◽  
Ke Wang
Keyword(s):  
Object Detection ◽  
Pose Estimation ◽  
Rotation Invariance ◽  
Depth Image ◽  
Detection Accuracy ◽  
Edge Pixel ◽  
Local Patch ◽  
Background Clutter ◽  
Public Datasets ◽  
Local Sampling

Local patch-based methods of object detection and pose estimation are promising. However, to the best of the authors’ knowledge, traditional red-green-blue and depth (RGB-D) patches contain scene interference (foreground occlusion and background clutter) and have little rotation invariance. To solve these problems, a new edge patch is proposed and experimented with in this study. The edge patch is a local sampling RGB-D patch centered at the edge pixel of the depth image. According to the normal direction of the depth edge, the edge patch is sampled along a canonical orientation, making it rotation invariant. Through a process of depth detection, scene interference is eliminated from the edge patch, which improves the robustness. The framework of the edge patch-based method is described, and the method was evaluated on three public datasets. Compared with existing methods, the proposed method achieved a higher average F1-score (0.956) on the Tejani dataset and a better average detection rate (62%) on the Occlusion dataset, even in situations of serious scene interference. These results showed that the proposed method has higher detection accuracy and stronger robustness.

scholarly journals A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network

2021 ◽  
Vol 11 (9) ◽  
pp. 4241
Author(s):  
Jiahua Wu ◽  
Hyo Jong Lee
Keyword(s):  
Pose Estimation ◽  
Human Body ◽  
State Of The Art ◽  
Estimation Method ◽  
Bottom Up ◽  
Center Point ◽  
Novel Approach ◽  
Body Joints

In bottom-up multi-person pose estimation, grouping joint candidates into the appropriately structured corresponding instance of a person is challenging. In this paper, a new bottom-up method, the Partitioned CenterPose (PCP) Network, is proposed to better cluster the detected joints. To achieve this goal, we propose a novel approach called Partition Pose Representation (PPR) which integrates the instance of a person and its body joints based on joint offset. PPR leverages information about the center of the human body and the offsets between that center point and the positions of the body’s joints to encode human poses accurately. To enhance the relationships between body joints, we divide the human body into five parts, and then, we generate a sub-PPR for each part. Based on this PPR, the PCP Network can detect people and their body joints simultaneously, then group all body joints according to joint offset. Moreover, an improved l1 loss is designed to more accurately measure joint offset. Using the COCO keypoints and CrowdPose datasets for testing, it was found that the performance of the proposed method is on par with that of existing state-of-the-art bottom-up methods in terms of accuracy and speed.

scholarly journals Social Media Adoption, Usage And Impact In Business-To-Business (B2B) Context: A State-Of-The-Art Literature Review

2021 ◽  
Author(s):  
Yogesh K. Dwivedi ◽  
Elvira Ismagilova ◽  
Nripendra P. Rana ◽  
Ramakrishnan Raman
Keyword(s):  
Social Media ◽  
Media Use ◽  
State Of The Art ◽  
Relevant Literature ◽  
Comprehensive Analysis ◽  
Social Media Use ◽  
Weight Analysis ◽  
Social Media Tools ◽  
Use Of Social Media ◽  
Business To Business

AbstractSocial media plays an important part in the digital transformation of businesses. This research provides a comprehensive analysis of the use of social media by business-to-business (B2B) companies. The current study focuses on the number of aspects of social media such as the effect of social media, social media tools, social media use, adoption of social media use and its barriers, social media strategies, and measuring the effectiveness of use of social media. This research provides a valuable synthesis of the relevant literature on social media in B2B context by analysing, performing weight analysis and discussing the key findings from existing research on social media. The findings of this study can be used as an informative framework on social media for both, academic and practitioners.

Information processing by elderly consumers: a five-decade review

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Gianluigi Guido ◽  
Marco Pichierri ◽  
Cristian Rizzo ◽  
Verdiana Chieffi ◽  
George Moschis
Keyword(s):  
Information Processing ◽  
Design Methodology ◽  
State Of The Art ◽  
Relevant Literature ◽  
Academic Research ◽  
The Elderly ◽  
Marketing Strategies ◽  
Content Type ◽  
Processing Activity ◽  
Elderly Consumers

Purpose The purpose of this study is to review scholarly research on elderly consumers’ information processing and suggest implications for services marketing. Design/methodology/approach The review encompasses a five-decade period (1970–2018) of academic research and presents relevant literature in four main areas related to information processing: sensation, attention, interpretation and memory. Findings The study illustrates how each of the aforementioned phases of the information processing activity may affect how elderly individuals buy and consume products and services, emphasizing the need for a better comprehension of the elderly to develop effectual marketing strategies. Originality/value The study provides readers with detailed state-of-the-art knowledge about older consumers’ information processing, offering a comprehensive review of academic research that companies can use to improve the effectiveness of their marketing efforts that target the elderly market.

scholarly journals Knowing What, How and Why: A Near Complete Solution for Aspect-Based Sentiment Analysis

2020 ◽  
Vol 34 (05) ◽  
pp. 8600-8607
Author(s):  
Haiyun Peng ◽  
Lu Xu ◽  
Lidong Bing ◽  
Fei Huang ◽  
Wei Lu ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
State Of The Art ◽  
Complete Solution ◽  
Unified Model ◽  
Two Stage ◽  
Fine Grained ◽  
Aspect Extraction ◽  
Second Stage ◽  
Opinion Extraction ◽  
Complete Story

Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not limited to aspect extraction, aspect sentiment classification, and opinion extraction. There exist many solvers of the above individual subtasks or a combination of two subtasks, and they can work together to tell a complete story, i.e. the discussed aspect, the sentiment on it, and the cause of the sentiment. However, no previous ABSA research tried to provide a complete solution in one shot. In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). Particularly, a solver of this task needs to extract triplets (What, How, Why) from the inputs, which show WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e. opinion reasons). For instance, one triplet from “Waiters are very friendly and the pasta is simply average” could be (‘Waiters’, positive, ‘friendly’). We propose a two-stage framework to address this task. The first stage predicts what, how and why in a unified model, and then the second stage pairs up the predicted what (how) and why from the first stage to output triplets. In the experiments, our framework has set a benchmark performance in this novel triplet extraction task. Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art related methods.

Reliability Analysis of Great Lakes Marine Diesels: State of the Art and Current Modeling

1990 ◽  
Vol 27 (04) ◽  
pp. 237-249
Author(s):  
Anastassios N. Perakis ◽  
Bahadir Inozu
Keyword(s):  
Great Lakes ◽  
Diesel Engines ◽  
Failure Modes ◽  
State Of The Art ◽  
Relevant Literature ◽  
Fault Tree Analysis ◽  
Reliability Engineering ◽  
Marine Industry ◽  
Marine Diesel ◽  
Basic Concepts

Some essential steps for the application of reliability, availability, and maintainability (RAM) techniques to marine diesel engines are presented. The paper begins with a summary of the basic concepts of reliability engineering, followed by a survey of the relevant literature on RAM applications to the marine industry and to marine diesel engines in particular. Next, the results of an informal survey of the reliability, maintenance, and replacement practices of Great Lakes operators are presented. Finally, the first two steps for a RAM application, failure modes and effects analysis and fault tree analysis, are introduced and applied for a prototype Colt-Pielstick marine diesel engine.

scholarly journals Sentence Generation for Entity Description with Content-Plan Attention

2020 ◽  
Vol 34 (05) ◽  
pp. 9057-9064
Author(s):  
Bayu Trisedya ◽  
Jianzhong Qi ◽  
Rui Zhang
Keyword(s):  
State Of The Art ◽  
Neural Models ◽  
Time Step ◽  
Two Stage ◽  
Sentence Generation ◽  
Neural Data ◽  
Attention Model ◽  
Linear Sequence ◽  
Proper Order ◽  
Real World Datasets

We study neural data-to-text generation. Specifically, we consider a target entity that is associated with a set of attributes. We aim to generate a sentence to describe the target entity. Previous studies use encoder-decoder frameworks where the encoder treats the input as a linear sequence and uses LSTM to encode the sequence. However, linearizing a set of attributes may not yield the proper order of the attributes, and hence leads the encoder to produce an improper context to generate a description. To handle disordered input, recent studies propose two-stage neural models that use pointer networks to generate a content-plan (i.e., content-planner) and use the content-plan as input for an encoder-decoder model (i.e., text generator). However, in two-stage models, the content-planner may yield an incomplete content-plan, due to missing one or more salient attributes in the generated content-plan. This will in turn cause the text generator to generate an incomplete description. To address these problems, we propose a novel attention model that exploits content-plan to highlight salient attributes in a proper order. The challenge of integrating a content-plan in the attention model of an encoder-decoder framework is to align the content-plan and the generated description. We handle this problem by devising a coverage mechanism to track the extent to which the content-plan is exposed in the previous decoding time-step, and hence it helps our proposed attention model select the attributes to be mentioned in the description in a proper order. Experimental results show that our model outperforms state-of-the-art baselines by up to 3% and 5% in terms of BLEU score on two real-world datasets, respectively.

scholarly journals A recommendations model with multiaspect awareness and hierarchical user-product attention mechanisms

2020 ◽  
Vol 17 (3) ◽  
pp. 849-865
Author(s):  
Zhongqin Bi ◽  
Shuming Dou ◽  
Zhe Liu ◽  
Yongbin Li
Keyword(s):  
State Of The Art ◽  
Weight Vector ◽  
User Preferences ◽  
The Other ◽  
Attention Networks ◽  
Proposed Model ◽  
Network Methods ◽  
Public Datasets ◽  
Novel Model ◽  
Attention Weight

Neural network methods have been trained to satisfactorily learn user/product representations from textual reviews. A representation can be considered as a multiaspect attention weight vector. However, in several existing methods, it is assumed that the user representation remains unchanged even when the user interacts with products having diverse characteristics, which leads to inaccurate recommendations. To overcome this limitation, this paper proposes a novel model to capture the varying attention of a user for different products by using a multilayer attention framework. First, two individual hierarchical attention networks are used to encode the users and products to learn the user preferences and product characteristics from review texts. Then, we design an attention network to reflect the adaptive change in the user preferences for each aspect of the targeted product in terms of the rating and review. The results of experiments performed on three public datasets demonstrate that the proposed model notably outperforms the other state-of-the-art baselines, thereby validating the effectiveness of the proposed approach.

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