Kaiyu Yang will present his General Exam on Monday, January 20, 2020 at 1:30pm in CS 402. The members of his committee are as follows: Jia Deng (adviser), Szymon Rusinkiewicz, and Olga Russakovsky 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. Title: SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition Abstract: Understanding the spatial relations between objects in images is a surprisingly challenging task. A chair may be "behind" a person even if it appears to the left of the person in the image (depending on which way the person is facing). Two students that appear close to each other in the image may not in fact be "next to" each other if there is a third student between them. We introduce SpatialSense, a dataset specializing in spatial relation recognition which captures a broad spectrum of such challenges, allowing for proper benchmarking of computer vision techniques. SpatialSense is constructed through adversarial crowdsourcing, in which human annotators are tasked with finding spatial relations that are difficult to predict using simple cues such as 2D spatial configuration or language priors. Adversarial crowdsourcing significantly reduces dataset bias and samples more interesting relations in the long tail compared to existing datasets. On SpatialSense, state-of-the-art recognition models perform comparably to simple baselines, suggesting that they rely on straightforward cues instead of fully reasoning about this complex task. The SpatialSense benchmark provides a path forward to advancing the spatial reasoning capabilities of computer vision systems. Textbook: [1] Szeliski, Richard. Computer vision: algorithms and applications . Springer Science & Business Media, 2010. Papers: [2] 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. [3] Kuznetsova, Alina, et al. "The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale." arXiv preprint arXiv:1811.00982 (2018). [4] Lu, Cewu, et al. "Visual relationship detection with language priors." European Conference on Computer Vision . Springer, Cham, 2016. [4] Torralba, Antonio, and Alexei A. Efros. "Unbiased look at dataset bias." CVPR . Vol. 1. No. 2. 2011. [5] Sadeghi, Mohammad Amin, and Ali Farhadi. "Recognition using visual phrases." CVPR 2011 . IEEE, 2011. [6] Dai, Bo, Yuqi Zhang, and Dahua Lin. "Detecting visual relationships with deep relational networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2017. [7] Xu, Danfei, et al. "Scene graph generation by iterative message passing." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2017. [8] Peyre, Julia, et al. "Weakly-supervised learning of visual relations." Proceedings of the IEEE International Conference on Computer Vision . 2017. [9] Goyal, Yash, et al. "Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2017. [10] Zhang, Peng, et al. "Yin and yang: Balancing and answering binary visual questions." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2016. [11] Attenberg, Josh M., Pagagiotis G. Ipeirotis, and Foster Provost. "Beat the machine: Challenging workers to find the unknown unknowns." Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence . 2011.