Summarization and Classification of Wearable Camera Streams by Learning the Distributions over Deep Features of Out-of-Sample Image Sequences
暂无分享,去创建一个
[1] Jason Yosinski,et al. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] G. O'loughlin,et al. Using a wearable camera to increase the accuracy of dietary analysis. , 2013, American journal of preventive medicine.
[3] Nebojsa Jojic,et al. Multidimensional counting grids: Inferring word order from disordered bags of words , 2011, UAI.
[4] Sam T. Roweis,et al. Constrained Hidden Markov Models , 1999, NIPS.
[5] Jon M. Kleinberg,et al. Fast Algorithms for Large-State-Space HMMs with Applications to Web Usage Analysis , 2003, NIPS.
[6] Paul A. Viola,et al. Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[7] Byoung-Tak Zhang,et al. Dual-Memory Deep Learning Architectures for Lifelong Learning of Everyday Human Behaviors , 2016, IJCAI.
[8] Bolei Zhou,et al. Places: An Image Database for Deep Scene Understanding , 2016, ArXiv.
[9] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[10] Qingshan Liu,et al. Abnormal detection using interaction energy potentials , 2011, CVPR 2011.
[11] Petia Radeva,et al. Toward Storytelling From Visual Lifelogging: An Overview , 2015, IEEE Transactions on Human-Machine Systems.
[12] Alan F. Smeaton,et al. Experiences of Aiding Autobiographical Memory Using the SenseCam , 2012, Hum. Comput. Interact..
[13] Alessandro Perina,et al. A comparison of crowd commotion measures from generative models , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[14] Stefan Carlsson,et al. CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.
[15] Nebojsa Jojic,et al. Structural epitome: a way to summarize one's visual experience , 2010, NIPS.
[16] Trevor Darrell,et al. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.
[17] James M. Rehg,et al. Social interactions: A first-person perspective , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[18] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[19] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[20] Nanning Zheng,et al. Counting Grid Aggregation for Event Retrieval and Recognition , 2016, ArXiv.
[21] Nebojsa Jojic,et al. Spring Lattice Counting Grids: Scene Recognition Using Deformable Positional Constraints , 2012, ECCV.
[22] Baochang Zhang,et al. Location recognition on lifelog images via a discriminative combination of generative models , 2014, BMVC.
[23] Petia Radeva,et al. With whom do I interact? Detecting social interactions in egocentric photo-streams , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).
[24] Joo-Hwee Lim,et al. Multimodal Multi-Stream Deep Learning for Egocentric Activity Recognition , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[25] Robert B. Fisher,et al. The BEHAVE video dataset: ground truthed video for multi-person behavior classification , 2010 .
[26] Nebojsa Jojic,et al. Capturing Spatial Interdependence in Image Features: The Counting Grid, an Epitomic Representation for Bags of Features , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[27] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[28] Kristen Grauman,et al. Story-Driven Summarization for Egocentric Video , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[29] William M. Wells,et al. Efficient Synthesis of Gaussian Filters by Cascaded Uniform Filters , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..