2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)最新文献_第4页

Summary of the 2022 Low-Power Deep Learning Semantic Segmentation Model Compression Competition for Traffic Scene In Asian Countries 2022亚洲国家交通场景低功耗深度学习语义分割模型压缩竞赛综述

2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) Pub Date : 2022-07-18 DOI: 10.1109/ICMEW56448.2022.9859367

Yu-Shu Ni, Chia-Chi Tsai, Chih-Cheng Chen, Po-Yu Chen, Hsien-Kai Kuo, Man-Yu Lee, Kuo Chin-Chuan, Zhe-Ln Hu, Po-Chi Hu, Ted T. Kuo, Jenq-Neng Hwang, Jiun-In Guo

{"title":"Summary of the 2022 Low-Power Deep Learning Semantic Segmentation Model Compression Competition for Traffic Scene In Asian Countries","authors":"Yu-Shu Ni, Chia-Chi Tsai, Chih-Cheng Chen, Po-Yu Chen, Hsien-Kai Kuo, Man-Yu Lee, Kuo Chin-Chuan, Zhe-Ln Hu, Po-Chi Hu, Ted T. Kuo, Jenq-Neng Hwang, Jiun-In Guo","doi":"10.1109/ICMEW56448.2022.9859367","DOIUrl":"https://doi.org/10.1109/ICMEW56448.2022.9859367","url":null,"abstract":"The 2022 low-power deep learning semantic segmentation model compression competition for traffic scene in Asian countries held in IEEE ICME2022 Grand Challenges focuses on the semantic segmentation technologies in autonomous driving scenarios. The competition aims to semantically segment objects in traffic with low power and high mean intersection over union (mIOU) in the Asia countries (e.g., Taiwan), which contain several harsh driving environments. The target segmented objects include dashed white line, dashed yellow line, single white line, single yellow line, double dashed white line, double white line, double yellow line, main lane, and alter lane. There are 35,500 annotated images provided for model training revised from Berkeley Deep Drive 100K and 130 annotated images provided for example from Asian road conditions. Additional 2,012 testing images are used in the contest evaluation process, in which 1,200 of them are used in the qualification stage competition, and the rest are used in the final stage competition. There are in total 203 registered teams joining this competition, and the top 15 teams with the highest mIOU entered the final stage competition, from which 8 teams submitted the final results. The overall best model belongs to team “okt2077”, followed by team “asdggg” and team “AVCLab.” A special award for the best INT8 model development award is absent.","PeriodicalId":106759,"journal":{"name":"2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121533987","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: 2

Local to Global Transformer for Video Based 3d Human Pose Estimation 基于视频的三维人体姿态估计的局部到全局变换

2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) Pub Date : 2022-07-18 DOI: 10.1109/ICMEW56448.2022.9859482

Haifeng Ma, Ke Lu, Jian Xue, Zehai Niu, Pengcheng Gao

引用次数: 0

Watermarking Protocol for Deep Neural Network Ownership Regulation in Federated Learning 联邦学习中深度神经网络所有权调节的水印协议

2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) Pub Date : 2022-07-18 DOI: 10.1109/ICMEW56448.2022.9859395

Fangqi Li, Shilin Wang, Alan Wee-Chung Liew

引用次数: 4

Exploring Multisensory Feedback for Virtual Reality Relaxation 探索虚拟现实放松的多感官反馈

2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) Pub Date : 2022-07-18 DOI: 10.1109/ICMEW56448.2022.9859362

Jing-Yuan Huang, Grace Theodore, You-Shin Tsai, Jerry Chin-Han Goh, Mu-Hang Lin, Kuan-Wei Tseng, Y. Hung

引用次数: 0

Emotional Quality Evaluation for Generated Music Based on Emotion Recognition Model 基于情感识别模型的生成音乐情感质量评价

2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) Pub Date : 2022-07-18 DOI: 10.1109/ICMEW56448.2022.9859459

Hongfei Wang, Wei Zhong, Lin Ma, Long Ye, Qin Zhang

引用次数: 1

Conditional Sentence Rephrasing without Parallel Training Corpus 无平行训练语料库的条件句改写

2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) Pub Date : 2022-07-18 DOI: 10.1109/ICMEW56448.2022.9859385

Yen-Ting Lee, Cheng-te Li, Shou-De Lin

引用次数: 0

Music Question Answering:Cognize and Perceive Music 音乐问答:认知和感知音乐

2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) Pub Date : 2022-07-18 DOI: 10.1109/ICMEW56448.2022.9859499

Wenhao Gao, Xiaobing Li, Cong Jin, Tie Yun

引用次数: 2

Augmented-Training-Aware Bisenet for Real-Time Semantic Segmentation 用于实时语义分割的增强训练感知双组

2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) Pub Date : 2022-07-18 DOI: 10.1109/ICMEW56448.2022.9859497

Chih-Chung Hsu, Cheih Lee, Shen-Chieh Tai, Yun Jiang

{"title":"Augmented-Training-Aware Bisenet for Real-Time Semantic Segmentation","authors":"Chih-Chung Hsu, Cheih Lee, Shen-Chieh Tai, Yun Jiang","doi":"10.1109/ICMEW56448.2022.9859497","DOIUrl":"https://doi.org/10.1109/ICMEW56448.2022.9859497","url":null,"abstract":"Semantic segmentation techniques have become an attractive research field for autonomous driving. However, it is well-known that the computational complexity of the conventional semantic segmentation is relatively high compared to other computer vision applications. Fast inference of the semantic segmentation for autonomous driving is highly desired. A lightweight convolutional neural network, the Bilateral segmentation network (BiSeNet), is adopted in this paper. However, the performance of the conventional BiSeNet is not so reliable that the model quantization could lead to an even worse result. Therefore, we proposed an augmented training strategy to significantly improve the semantic segmentation task’s performance. First, heavy data augmentation, including CutOut, deformable distortion, and step-wise hard example mining, is used in the training phase to boost the performance of the feature representation learning. Second, the L1 and L2 norm regularization are also used in the model training to prevent the possible overfitting issue. Then, the post-quantization is performed on the TensorFlow-Lite model to significantly reduce the model size and computational complexity. The comprehensive experiments verified that the proposed method is effective and efficient for autonomous driving applications over other state-of-the-art methods.","PeriodicalId":106759,"journal":{"name":"2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128703239","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

A Unified Video Summarization for Video Anomalies Through Deep Learning 基于深度学习的视频异常统一摘要

2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) Pub Date : 2022-07-18 DOI: 10.1109/ICMEW56448.2022.9859320

K. Muchtar, Muhammad Rizky Munggaran, Adhiguna Mahendra, Khairul Anwar, Chih-Yang Lin

{"title":"A Unified Video Summarization for Video Anomalies Through Deep Learning","authors":"K. Muchtar, Muhammad Rizky Munggaran, Adhiguna Mahendra, Khairul Anwar, Chih-Yang Lin","doi":"10.1109/ICMEW56448.2022.9859320","DOIUrl":"https://doi.org/10.1109/ICMEW56448.2022.9859320","url":null,"abstract":"Over the last ten years, integrated video surveillance systems have become increasingly important in protecting public safety. Because a single surveillance camera continuously collects events in a specific field of view at all times of day and night, a system that can create a summary that concisely captures key elements of the incoming frames is required. To be more specific, due to time constraints, the enormous amount of video footage cannot be properly examined for analysis. As a result, it is vital to compile a summary of what happened on the scene and look for anomalous events in the footage. A unified approach for detecting and summarizing anomalous events is proposed. To detect the event and compute the anomaly scores, a 3D deep learning approach is used. Afterward, the scores are utilized to visualize and localize the anomalous regions. Finally, the blob analysis technique is used to extract the anomalous regions. To verify the results, quantitative and qualitative evaluations are provided. Experiments indicate that the proposed summarizing method keeps crucial information while producing competitive results. More qualitative results can be found through our project channel: https://youtu.be/eMPMjiGlCQI","PeriodicalId":106759,"journal":{"name":"2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115990454","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

Quantification of Artist Representativity within an Art Movement 艺术运动中艺术家代表性的量化

2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) Pub Date : 2022-07-18 DOI: 10.1109/ICMEW56448.2022.9859412

Yu-xin Zhang, Fan Tang, Weiming Dong, Changsheng Xu

引用次数: 0