SeGAN: Segmenting and Generating the Invisible

Objects often occlude each other in scenes; Inferring their appearance beyond their visible parts plays an important role in scene understanding, depth estimation, object interaction and manipulation. In this paper, we study the challenging problem of completing the appearance of occluded objects. Doing so requires knowing which pixels to paint (segmenting the invisible parts of objects) and what color to paint them (generating the invisible parts). Our proposed novel solution, SeGAN, jointly optimizes for both segmentation and generation of the invisible parts of objects. Our experimental results show that: (a) SeGAN can learn to generate the appearance of the occluded parts of objects; (b) SeGAN outperforms state-of-the-art segmentation baselines for the invisible parts of objects; (c) trained on synthetic photo realistic images, SeGAN can reliably segment natural images; (d) by reasoning about occluder occludee relations, our method can infer depth layering.Comment: Accepted to CVPR18 as spotligh

LCNN: Lookup-based Convolutional Neural Network

Porting state of the art deep learning algorithms to resource constrained compute platforms (e.g. VR, AR, wearables) is extremely challenging. We propose a fast, compact, and accurate model for convolutional neural networks that enables efficient learning and inference. We introduce LCNN, a lookup-based convolutional neural network that encodes convolutions by few lookups to a dictionary that is trained to cover the space of weights in CNNs. Training LCNN involves jointly learning a dictionary and a small set of linear combinations. The size of the dictionary naturally traces a spectrum of trade-offs between efficiency and accuracy. Our experimental results on ImageNet challenge show that LCNN can offer 3.2x speedup while achieving 55.1% top-1 accuracy using AlexNet architecture. Our fastest LCNN offers 37.6x speed up over AlexNet while maintaining 44.3% top-1 accuracy. LCNN not only offers dramatic speed ups at inference, but it also enables efficient training. In this paper, we show the benefits of LCNN in few-shot learning and few-iteration learning, two crucial aspects of on-device training of deep learning models.Comment: CVPR 1

Improved Treatment of Epistemic Uncertainty in Seismic Hazard Modeling with Focus on Central and Eastern North America

In this study, I take advantage of up-to-date studies and recently compiled datasets to solve some important problems faced in seismic hazard modeling in the Central and Eastern North America (CENA) as well as in Iran through four separate chapters. In these chapters, I provide useful information to improve treatment of epistemic uncertainty in seismic hazard modeling for CENA and Iran. Seismic hazard modelers in both CENA and Iran may use this study when performing earthquake hazard evaluations. In the first chapter, I assess the applicability of ground-motion models (GMMs) to propose a set of models that can be confidently used for induced seismicity applications within the CENA. This study is the first or one of the earliest studies of this kind that focused on GMMs for induced earthquakes. In the second chapter, I use the same methodology to come up with a shortlist of suitable GMMs for the probabilistic seismic hazard assessment (PSHA) in Iran. The need for assessing models relative performances prior to carrying out seismic hazard studies is crucial for the tectonic region of Iran due to the shortage of experienced domestic experts. In the last two chapters, because of the need of the earthquake engineering community in CENA to predict ground-motion intensity measures (GMIMs) other than the horizontal ground-motion component, I develop a set of new GMMs based on the NGA-East database. I develop these models using the referenced empirical approach. In the third chapter of the present study, I establish a referenced empirical ground-motion model for estimating Arias Intensity (AI) and cumulative absolute velocity (CAV) for CENA using Campbell and Bozorgnia (2019) as the reference model. AI and CAV have extensive applications in assessing the impact of strong-motion duration on slope stability, soil liquefaction, building damage, and seismic response of bridges. In the fourth chapter, I develop three referenced empirical models considering the Bozorgnia and Campbell (2016), Glerce et al. (2017), and Stewart et al. (2016) models. The effect of the vertical component is significant for the design of ordinary highway bridges and vital structures such as nuclear power plants and dams

Toward a Taxonomy and Computational Models of Abnormalities in Images

The human visual system can spot an abnormal image, and reason about what makes it strange. This task has not received enough attention in computer vision. In this paper we study various types of atypicalities in images in a more comprehensive way than has been done before. We propose a new dataset of abnormal images showing a wide range of atypicalities. We design human subject experiments to discover a coarse taxonomy of the reasons for abnormality. Our experiments reveal three major categories of abnormality: object-centric, scene-centric, and contextual. Based on this taxonomy, we propose a comprehensive computational model that can predict all different types of abnormality in images and outperform prior arts in abnormality recognition.Comment: To appear in the Thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016