Alejandro Newell will present his General Exam "Beyond Objects: Image Understanding as Pixels to Graphs" on Friday, January 17, 2020 at 12pm in CS 401
Alejandro Newell will present his General Exam "Beyond Objects: Image Understanding as Pixels to Graphs" on Friday, January 17, 2020 at 12pm in CS 401. The members of his committee are as follows: Jia Deng (adviser), Olga Russakovsky, and Szymon Rusinkiewicz. Everyone is invited to attend his talk, and those faculty wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below. Abstract: Tremendous progress has been made teaching computer vision systems to recognize objects in images. Each year, these systems can more reliably classify objects and better pinpoint their location in a scene. But a collection of objects is not enough. For full scene understanding, we must understand the connections and relationships between objects. Graphs are an effective way of representing these connections, but are difficult to express given standard approaches for training deep convolutional networks. In this presentation, I will discuss how to supervise a network to predict graphs. I will go over the details of our approach including its application to relationship detection and advantages over existing methods. Reading List: Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012. Toshev, Alexander, and Christian Szegedy. "Deeppose: Human pose estimation via deep neural networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2014. Tompson, Jonathan J., et al. "Joint training of a convolutional network and a graphical model for human pose estimation." Advances in neural information processing systems. 2014. Johnson, Justin, et al. "Image retrieval using scene graphs." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. Cao, Zhe, et al. "Realtime multi-person 2d pose estimation using part affinity fields." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. He, Kaiming, et al. "Mask r-cnn." Proceedings of the IEEE international conference on computer vision. 2017. Krishna, Ranjay, et al. "Visual genome: Connecting language and vision using crowdsourced dense image annotations." International Journal of Computer Vision 123.1 (2017): 32-73. Lu, Cewu, et al. "Visual relationship detection with language priors." European Conference on Computer Vision. Springer, Cham, 2016. Xu, Danfei, et al. "Scene graph generation by iterative message passing." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. Oord, Aaron van den, Yazhe Li, and Oriol Vinyals. "Representation learning with contrastive predictive coding." arXiv preprint arXiv:1807.03748 (2018).
participants (1)
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Nicki Mahler