DeepNav: Learning to Navigate Large Cities

We present DeepNav, a Convolutional Neural Network (CNN) based algorithm for navigating large cities using locally visible street-view images. The DeepNav agent learns to reach its destination quickly by making the correct navigation decisions at intersections. We collect a large-scale dataset of street-view images organized in a graph where nodes are connected by roads. This dataset contains 10 city graphs and more than 1 million street-view images. We propose 3 supervised learning approaches for the navigation task and show how A* search in the city graph can be used to generate supervision for the learning. Our annotation process is fully automated using publicly available mapping services and requires no human input. We evaluate the proposed DeepNav models on 4 held-out cities for navigating to 5 different types of destinations. Our algorithms outperform previous work that uses hand-crafted features and Support Vector Regression (SVR)[19].Comment: CVPR 2017 camera ready versio

SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis

Synthesizing realistic images from human drawn sketches is a challenging problem in computer graphics and vision. Existing approaches either need exact edge maps, or rely on retrieval of existing photographs. In this work, we propose a novel Generative Adversarial Network (GAN) approach that synthesizes plausible images from 50 categories including motorcycles, horses and couches. We demonstrate a data augmentation technique for sketches which is fully automatic, and we show that the augmented data is helpful to our task. We introduce a new network building block suitable for both the generator and discriminator which improves the information flow by injecting the input image at multiple scales. Compared to state-of-the-art image translation methods, our approach generates more realistic images and achieves significantly higher Inception Scores.Comment: Accepted to CVPR 201

Complex Event Recognition from Images with Few Training Examples

We propose to leverage concept-level representations for complex event recognition in photographs given limited training examples. We introduce a novel framework to discover event concept attributes from the web and use that to extract semantic features from images and classify them into social event categories with few training examples. Discovered concepts include a variety of objects, scenes, actions and event sub-types, leading to a discriminative and compact representation for event images. Web images are obtained for each discovered event concept and we use (pretrained) CNN features to train concept classifiers. Extensive experiments on challenging event datasets demonstrate that our proposed method outperforms several baselines using deep CNN features directly in classifying images into events with limited training examples. We also demonstrate that our method achieves the best overall accuracy on a dataset with unseen event categories using a single training example.Comment: Accepted to Winter Applications of Computer Vision (WACV'17

A Numerical Treatment of Melt/Solid Segregation: Size of the Eucrite Parent Body and Stability of the Terrestrial Low-Velocity Zone

Crystal sinking to form cumulates and melt percolation toward segregation in magma pools can be treated with modifications of Stokes' and Darcy's laws, respectively. The velocity of crystals and melt depends, among other things, on the force of gravity (g) driving the separations and the cooling time of the environment. The increase of g promotes more efficient differentiation, whereas the increase of cooling rate limits the extent to which crystals and liquid can separate. The rate at which separation occurs is strongly dependent on the proportion of liquid that is present. As a result, cumulate formation is a process with a negative feedback; the more densely aggregated the crystals become, the slower the process can proceed. In contrast, melt accumulation is a process with a positive feedback; partial accumulation of melt leads to more rapid accumulation of subsequent melt. This positive feedback can cause melt accumulation to run rapidly to completion once a critical stability limit is passed. The observation of cumulates and segregated melts among the eucrite meteorites is used as a basis for calculating the g (and planet size) required to perform these differentiations. The eucrite parent body was probably at least 10-100 km in radius. The earth's low velocity zone (LVZ) is shown to be unstable with respect to draining itself of excess melt if the melt forms an interconnecting network. A geologically persistent LVZ with a homogeneous distribution of melt can be maintained with melt fractions only on the order of 0.1% or less