发布求助
文献互助 智能选刊 最新文献

2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR)最新文献

筛选
英文 中文
Personalized Prediction of Indoor Comfort Using Graph Convolutional Matrix Completion 使用图卷积矩阵完成的室内舒适度个性化预测
2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2022-08-01 DOI: 10.1109/MIPR54900.2022.00053
Junyi Liu, E. Naidu, Jialian Wu, Shira Gabriel, E. Steinfeld, Junsong Yuan
{"title":"Personalized Prediction of Indoor Comfort Using Graph Convolutional Matrix Completion","authors":"Junyi Liu, E. Naidu, Jialian Wu, Shira Gabriel, E. Steinfeld, Junsong Yuan","doi":"10.1109/MIPR54900.2022.00053","DOIUrl":"https://doi.org/10.1109/MIPR54900.2022.00053","url":null,"abstract":"Recent progress in environment sensing technology focuses more on measuring the physical properties of the environment, e.g., temperature and noise, but lacks the ability to understand subjective responses, or feelings about the environment, e.g., indoor comfort. Feelings depend on both environmental conditions and individual needs and preferences. Different people may feel differently in the same room experiencing the same conditions. In this work, we apply a crowdsensing based approach to predict personalized indoor comfort. We assume that similar users share similar feelings about comfort, and that indoor comfort is related to a fixed set of conditions, e.g., space, humidity, temperature. We surveyed existing users of a case study building and used their responses to learn how to predict the personal responses of new users. Technically, we apply a graph convolutional matrix completion (GC-MC) method to predict the comfort of other users, by learning the dependency between the user profiles and their ratings to a fixed set of survey questions. We collect a kitchen survey dataset of 59 questions and in total 29 users of diverse profiles.","PeriodicalId":228640,"journal":{"name":"2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"264 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124305406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
MOAC: Multi-level Perception Optimizer Based on Dual Augmented Cost for Structure- from-Motion 基于双增广代价的结构自运动多层级感知优化器
2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2022-08-01 DOI: 10.1109/MIPR54900.2022.00031
Pei-Chen Wu, Ge Li, Thomas H. Li
{"title":"MOAC: Multi-level Perception Optimizer Based on Dual Augmented Cost for Structure- from-Motion","authors":"Pei-Chen Wu, Ge Li, Thomas H. Li","doi":"10.1109/MIPR54900.2022.00031","DOIUrl":"https://doi.org/10.1109/MIPR54900.2022.00031","url":null,"abstract":"An increasing number of methods use learning-based optimization to solve Structure-from-Motion. However, the conventional photometric cost in existing methods is susceptible to noise due to insufficient spatial information. In this paper, we attempt to improve the robustness and accuracy of the optimization and propose a novel end-to-end optimization framework named MOAC. Specifically, we introduce a grouped dual cost augmentation module, which makes the optimization more robust to noise by augmenting the spatial semantic information and channel relationship of the cost. In addition, we design a multi-level perception optimizer that efficiently improves the prediction per-formance of large objects. Experiments conducted on the KITTI dataset demonstrate the state-of-the-art performance of MOAC, both qualitatively and quantitatively.","PeriodicalId":228640,"journal":{"name":"2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"2000 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128277217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Information-Seeking in Localization and Mission Planning of Multi-Agent Systems 多智能体系统定位与任务规划中的信息搜索
2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2022-08-01 DOI: 10.1109/MIPR54900.2022.00021
Kyriakos Lite, B. Rinner
{"title":"Information-Seeking in Localization and Mission Planning of Multi-Agent Systems","authors":"Kyriakos Lite, B. Rinner","doi":"10.1109/MIPR54900.2022.00021","DOIUrl":"https://doi.org/10.1109/MIPR54900.2022.00021","url":null,"abstract":"Real-time and accurate position estimation is critical for various multi-robot applications and serves as a prerequisite for location-based multi-sensor data analysis. However, it is often impeded by energy, sensing, and processing limitations. In this work, we study the problem of information-seeking in localization and navigation in multi-agent systems, which aims to navigate mobile agents while reducing position errors. We formalize information-seeking as reducing spatial uncertainty and introduce an efficient motion controller based on artificial potential fields superimposing attractive, repulsive, and information-seeking forces. We evaluate the effect of information-seeking on localization and mission planning in a simulation study with non-collaborative and collaborative localization approaches.","PeriodicalId":228640,"journal":{"name":"2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"301 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128622700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
ADAPTIVE ACQUISITION OF AIRBORNE LIDAR POINT CLOUD BASED ON DEEP REINFORCEMENT LEARNING 基于深度强化学习的机载激光雷达点云自适应获取
2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2022-08-01 DOI: 10.1109/MIPR54900.2022.00072
C. Huang, Dalei Wu, Yu Liang
{"title":"ADAPTIVE ACQUISITION OF AIRBORNE LIDAR POINT CLOUD BASED ON DEEP REINFORCEMENT LEARNING","authors":"C. Huang, Dalei Wu, Yu Liang","doi":"10.1109/MIPR54900.2022.00072","DOIUrl":"https://doi.org/10.1109/MIPR54900.2022.00072","url":null,"abstract":"Human experience involvement in existing operations of airborne Light Detection and Ranging (LIDAR) systems and off-line processing of collected LIDAR data make the acquisition process of airborne LIDAR point cloud less adaptable to environment conditions. This work develops a deep reinforcement learning-enabled framework for adaptive airborne LIDAR point cloud acquisition. Namely, the optimization of the airborne LIDAR operation is modeled as a Markov decision process (MDP). A set of LIDAR point cloud processing methods are proposed to derive the state space, action space, and reward function of the MDP model. A DRL algorithm, Deep Q-Network (DQN), is used to solve the MDP. The DRL model is trained in a flexible virtual environment by using simulator AirSim. Extensive simulation demonstrates the efficiency of the proposed framework.","PeriodicalId":228640,"journal":{"name":"2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127303870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Disparity-Guided Light Field Video Synthesis with Temporal Consistency 具有时间一致性的差导光场视频合成
2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2022-08-01 DOI: 10.1109/MIPR54900.2022.00038
Chin-Chia Yang, Yi-Chou Chen, Shan-Ling Chen, Homer H. Chen
{"title":"Disparity-Guided Light Field Video Synthesis with Temporal Consistency","authors":"Chin-Chia Yang, Yi-Chou Chen, Shan-Ling Chen, Homer H. Chen","doi":"10.1109/MIPR54900.2022.00038","DOIUrl":"https://doi.org/10.1109/MIPR54900.2022.00038","url":null,"abstract":"Light field has great applications in AR/VR. It is particu-larly usefulfor resolving the vergence-accommodation con-flict (VAC) and creating correct depth cues for AR/VR dis-plays. However, the source data, especially light field video, are not widely available yet. To resolve the scarcity is-sue, one may resort to data such as stereo image sequences that are commonly available. In this paper, we propose an end-to-end deep learning framework for synthesizing light field sequences from stereo image sequences. Our frame-work consists of a disparity estimation network, a guided synthesis network, and a refinement network and is able to resolve the flickering issue caused by temporal incon-sistency, an artifact that is commonly seen in synthesized light field videos. Our experimental results are quantitatively and qualitatively better than the results of existing light field synthesis algorithms that were originally developed for static light fields.","PeriodicalId":228640,"journal":{"name":"2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121370836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Contextual Active Learning for Person Re- Identification 情境主动学习对人物再认同的影响
2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2022-08-01 DOI: 10.1109/MIPR54900.2022.00052
Vineet K. Joshi, A. Subramanyam
{"title":"Contextual Active Learning for Person Re- Identification","authors":"Vineet K. Joshi, A. Subramanyam","doi":"10.1109/MIPR54900.2022.00052","DOIUrl":"https://doi.org/10.1109/MIPR54900.2022.00052","url":null,"abstract":"Active learning has been recently investigated in the field of Person Re-identijication to obtain informative samples for training. However, the current methods incur a fixed annotation cost as they do not explicitly incorporate the re-identification model’ confidence. To this end, we propose a novel human-in-the-loop context aware active learning method that helps the re-identification model improve with progressively collected data while annotating a few but effective samples. In our proposed method, a contex-tual bandit agent is trained to learn a policy to obtain the training samples about which the model is least confident and thus needs annotations. A binary reward is provided to the agent based on the actions and the confidence of the model given the current query image. On an average, our model achieves a boost of 9.13% mAP, 5.64% rank-1 improvement over the baseline and uses 32.3% less anno-tations compared to the previous best active learning approach on DukeMTMC-reID.","PeriodicalId":228640,"journal":{"name":"2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128812169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Fine-tuning the Robust Temporal Feature Magnitude Model for Enhancing the Accuracy of Anomaly Detection 改进鲁棒时间特征数量级模型以提高异常检测的精度
2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2022-08-01 DOI: 10.1109/MIPR54900.2022.00082
Charan Charupalli, Karthick Seshadri
{"title":"Fine-tuning the Robust Temporal Feature Magnitude Model for Enhancing the Accuracy of Anomaly Detection","authors":"Charan Charupalli, Karthick Seshadri","doi":"10.1109/MIPR54900.2022.00082","DOIUrl":"https://doi.org/10.1109/MIPR54900.2022.00082","url":null,"abstract":"Surveillance videos can capture a variety of realistic events and also anomalies. Due to an increase in the crime rate in public areas, surveillance cameras are adopted in a very large number. But as these crimes/public disputes are rare to occur at a specific location, human monitors are idle most of the time. Hence, there is a justified need to develop intelligent systems for anomaly detection. There are several seminal deep-neural architectures proposed in this field of anomaly detection ranging from using deep learning as a feature extraction tool to complete end-to-end deep-learning-based anomaly detection models. Any practical anomaly detection model must be generic in detecting a spectrum of anomalous events; however, several models can detect only specific types of anomalies. Further, several models are not amenable to distributed training over many machines on large streaming data, which is typical in a video surveillance system. In this paper, we discuss the techniques to detect anomalies in real-time by exploring recent architectures in the literature and analyze and explore ways we can improve the detection accuracy of the model. We propose a batching methodology that improves the existing model's area under the curve by 2%.","PeriodicalId":228640,"journal":{"name":"2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128745262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
RE2L: A Real-World Dataset for Outdoor Low-Light Image Enhancement RE2L:室外低光图像增强的真实世界数据集
2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2022-08-01 DOI: 10.1109/MIPR54900.2022.00030
Yuxin Lin, J. Li, Lanqing Guo, B. Wen
{"title":"RE2L: A Real-World Dataset for Outdoor Low-Light Image Enhancement","authors":"Yuxin Lin, J. Li, Lanqing Guo, B. Wen","doi":"10.1109/MIPR54900.2022.00030","DOIUrl":"https://doi.org/10.1109/MIPR54900.2022.00030","url":null,"abstract":"Low- and normal light image pairs are usually required for training low-light image enhancement (LLIE) deep learning models. Although such training pairs can be synthesized by adjusting exposure, there is currently no outdoor real-world LLIE benchmark. Therefore, it is un-clear how to optimize various LLIE models for real out-door scenes. To fill the gap and benchmark outdoor LLIE tasks, we propose the REal-world two-dimensional Low-light dataset, dubbed RE2L. Natural-light illumination variations over both time and exposure level are fully captured in RE2L, which provides more information on illumination as restoration guidance during model training. We bench-marked the state-of-the-art LLIE methods over the proposed RE2L and its derivation RE2L-P datasets. Furthermore, we showed that using RE2L and RE2L-P for training LLIE models can improve their effectiveness both quantitatively and visually.","PeriodicalId":228640,"journal":{"name":"2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129093967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Improving Angular Estimation Using a Deep CNN network in 6D pose estimation 在6D姿态估计中使用深度CNN网络改进角度估计
2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2022-08-01 DOI: 10.1109/MIPR54900.2022.00017
Ju Wang, Wookin Choi, Igor Schtau, Taylor Ferro, Weibo Chen, Cutrell Trott, Grant Patterson
{"title":"Improving Angular Estimation Using a Deep CNN network in 6D pose estimation","authors":"Ju Wang, Wookin Choi, Igor Schtau, Taylor Ferro, Weibo Chen, Cutrell Trott, Grant Patterson","doi":"10.1109/MIPR54900.2022.00017","DOIUrl":"https://doi.org/10.1109/MIPR54900.2022.00017","url":null,"abstract":"We investigate a deep learning-based method to estimate 6D pose information of target objects from one or multiple images. We use a modified YOLO2 as the backbone network for feature extraction and a detection network to detect the target's 3D bounding box. The network is trained using 3D errors in addition to the usual 2D pixel errors. Our method also uses a perspective-aware method to select the best keypoints to estimate the 6D pose. Synthesis data experiments show significant angular accuracy improvement in the estimated 6D pose.","PeriodicalId":228640,"journal":{"name":"2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125322296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Malware and Piracy Detection in Android Applications Android应用程序中的恶意软件和盗版检测
2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2022-08-01 DOI: 10.1109/MIPR54900.2022.00061
N. Kumari, Min Chen
{"title":"Malware and Piracy Detection in Android Applications","authors":"N. Kumari, Min Chen","doi":"10.1109/MIPR54900.2022.00061","DOIUrl":"https://doi.org/10.1109/MIPR54900.2022.00061","url":null,"abstract":"With the exploding growth in the number of Android applications, software malware and piracy incidents are among the major concerns to be addressed for a healthy marketplace. In this work, a permission-based malware detection system is developed to identify malware infected applications using the permissions accessed by applications. We also re-implement Juxtapp for malware and piracy detection based on the underlying opcodes. The performance of these approaches is evaluated on a large dataset containing original, pirated and malware infected applications extracted from AndroZoo and KuafuDet. Experimental results demonstrate that the permission-based malware detection system generally performs better than the opcode-based approach. In addition, Juxtapp can be used to detect software piracy, and may be used in conjunction with a permission-based malware detection system to accurately identify malware infected and pirated applications.","PeriodicalId":228640,"journal":{"name":"2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126235515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信