CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Hierarchically Supervised Deconvolutional Network for Semantic Video Segmentation
Wang, Yuhang1,2; Liu, Jing1; Li, Yong1,2; Fu, Jun1,2; Xu, Min3; Lu, Hanqing1; Jing Liu
AbstractSemantic video segmentation is a challenging task of fine-grained semantic understanding of video data. In this paper, we present a jointly trained deep learning framework to make the best use of spatial and temporal information for semantic video segmentation. Along the spatial dimension, a hierarchically supervised deconvolutional neural network (HDCNN) is proposed to conduct pixel-wise semantic interpretation for single video frames. HDCNN is constructed with convolutional layers in VGG-net and their mirrored deconvolutional structure, where all fully connected layers are removed. And hierarchical classification layers are added to multi scale deconvolutional features to introduce more contextual information for pixel-wise semantic interpretation. Besides, a coarse-to-fine training strategy is adopted to enhance the performance of foreground object segmentation in videos. Along the temporal dimension, we introduce Transition Layers upon the structure of HDCNN to make the pixel-wise label prediction consist with adjacent, pixels across space and time domains. The learning process of the Transition Layers can be implemented as a set of extra convolutional calculations connected with HDCNN. These two parts are jointly trained as a unified deep network in our approach. Thorough evaluations are performed on two challenging video datasets, i.e., CamVid and GATECH. Our approach achieves state-of-the-art performance on both of the two datasets.
KeywordSemantic Video Segmentation Deconvolutional Neural Network Coarse-to-fine Training Spatio-temporal Consistence
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding Organization863 Program(2014AA015104) ; National Natural Science Foundation of China(61332016 ; 61272329 ; 61472422)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000392682400036
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Document Type期刊论文
Corresponding AuthorJing Liu
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Univ Technol, Sydney, NSW, Australia
Recommended Citation
GB/T 7714
Wang, Yuhang,Liu, Jing,Li, Yong,et al. Hierarchically Supervised Deconvolutional Network for Semantic Video Segmentation[J]. PATTERN RECOGNITION,2017,64(1):437-445.
APA Wang, Yuhang.,Liu, Jing.,Li, Yong.,Fu, Jun.,Xu, Min.,...&Jing Liu.(2017).Hierarchically Supervised Deconvolutional Network for Semantic Video Segmentation.PATTERN RECOGNITION,64(1),437-445.
MLA Wang, Yuhang,et al."Hierarchically Supervised Deconvolutional Network for Semantic Video Segmentation".PATTERN RECOGNITION 64.1(2017):437-445.
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