Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild最新文献

Learning to Detect, Associate, and Recognize Human Actions and Surrounding Scenes in Untrimmed Videos 学习检测、关联和识别未修剪视频中的人类行为和周围场景

Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild Pub Date : 2018-10-15 DOI: 10.1145/3265987.3265989

Jungin Park, Sangryul Jeon, Seungryong Kim, Jiyoung Lee, Sunok Kim, K. Sohn

引用次数: 3

Session details: Session 1: Regular Track 会话详细信息:会话1:常规轨道

Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild Pub Date : 2018-10-15 DOI: 10.1145/3286585

K. Sohn

引用次数: 0

Session details: Keynote & Invited Talks 会议详情:主题演讲和特邀演讲

Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild Pub Date : 2018-10-15 DOI: 10.1145/3286584

K. Sohn

引用次数: 0

Video Understanding via Convolutional Temporal Pooling Network and Multimodal Feature Fusion 基于卷积时间池化网络和多模态特征融合的视频理解

Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild Pub Date : 2018-10-15 DOI: 10.1145/3265987.3265991

Heeseung Kwon, Suha Kwak, Minsu Cho

引用次数: 3

Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild 第一届野外综合视频理解研讨会论文集

Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild Pub Date : 2018-10-15 DOI: 10.1145/3265987

引用次数: 0

Joint Object Tracking and Segmentation with Independent Convolutional Neural Networks 基于独立卷积神经网络的联合目标跟踪与分割

Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild Pub Date : 2018-10-15 DOI: 10.1145/3265987.3265992

Hakjin Lee, Jongbin Ryu, Jongwoo Lim

{"title":"Joint Object Tracking and Segmentation with Independent Convolutional Neural Networks","authors":"Hakjin Lee, Jongbin Ryu, Jongwoo Lim","doi":"10.1145/3265987.3265992","DOIUrl":"https://doi.org/10.1145/3265987.3265992","url":null,"abstract":"Object tracking and segmentation are important research topics in computer vision. They provide the trajectory and boundary of an object based on their appearance and shape features. Most studies on tracking and segmentation focus on encoding methods for the feature of an object. However, the tracking trajectory and segmentation mask are acquired separately, although similar visual information is required for both methods. Therefore, in this paper, we propose a CNN-based joint object tracking and segmentation framework that provides a segmentation mask while improving the performance of object tacker. In our model, the tracking model determines the trajectory of the target object as a bounding box in each frame. Given the bounding box at each frame, the segmentation model predicts a dense mask of the target object in the bounding box. Then, the segmentation mask is used to refine the bounding box for the tracking model. We evaluate the performance of our algorithm on DAVIS benchmark dataset by AUC score and mean IoU. We showed that the performance of original tracker was improved by our proposed framework.","PeriodicalId":151362,"journal":{"name":"Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132144016","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: 3

New Feature-level Video Classification via Temporal Attention Model 基于时间注意模型的特征级视频分类

Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild Pub Date : 2018-10-15 DOI: 10.1145/3265987.3265990

Hongje Seong, Junhyuk Hyun, Suhyeon Lee, Suhan Woo, Hyunbae Chang, Euntai Kim

引用次数: 2

Stereo Vision aided Image Dehazing using Deep Neural Network 基于深度神经网络的立体视觉辅助图像去雾

Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild Pub Date : 2018-10-15 DOI: 10.1145/3265987.3265993

Jeong-Yun Na, Kuk-jin Yoon

{"title":"Stereo Vision aided Image Dehazing using Deep Neural Network","authors":"Jeong-Yun Na, Kuk-jin Yoon","doi":"10.1145/3265987.3265993","DOIUrl":"https://doi.org/10.1145/3265987.3265993","url":null,"abstract":"Deterioration of image due to haze is one of the factors that degrade the performance of computer vision algorithm. The haze component absorbs and reflects the reflected light from the object, distorting the original irradiance. The more the distance from the camera is, the more deteriorated it tends to be. Therefore, studies have been conducted to remove haze by estimating the distribution of haze along the distance. In this paper, we use convolution neural network to simultaneously perform depth estimation and haze removal based on stereo image, and depth information to help improve performance of haze removal. We propose a multitasking network in which the encoder learns depth information and dehazing features simultaneously by performing depth estimation and dehazing using two decoders. The learning of the network is based on a stereo image, and a large amount of left and right hazy images are required. However, existing hazy image data sets are inferior in reality because they are added to fog components in indoor images. Therefore, a data set composed of a haze component corresponding to the distance information was constructed and used in the KITTI road data set composed of a large amount of stereo outdoor driving images. Experimental results show that the proposed network has robust dehazing performance compared to existing methods for various levels of hazy images and improves the visibility by strengthening the contrast of boundaries in faint areas due to haze.","PeriodicalId":151362,"journal":{"name":"Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129080194","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: 5

Session details: Session 2: Challenge Track 会议详情:会议2:挑战赛道

Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild Pub Date : 2018-10-15 DOI: 10.1145/3286586

K. Sohn

引用次数: 0

Deep Video Understanding: Representation Learning, Action Recognition, and Language Generation 深度视频理解:表示学习、动作识别和语言生成

Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild Pub Date : 2018-10-15 DOI: 10.1145/3265987.3265994

Tao Mei

引用次数: 0