Abstract

Defocus magnification is the process of rendering a shallow depth-of-field in an image captured using a camera with a narrow aperture. Defocus magnification is a useful tool in photography for emphasis on the subject and for highlighting background bokeh. Estimating the per-pixel blur kernel or the depth-map of the scene followed by spatially-varying re-blurring is the standard approach to defocus magnification. We propose a single-step approach that directly converts a narrow-aperture image to a wide-aperture image. We use a conditional adversarial network trained on multiaperture images created from light-fields. We use a novel loss term based on a composite focus measure to improve generalization and show high quality defocus magnification.

Abstract

Intrinsic Image Decomposition (IID) is a challenging and interesting computer vision problem with various applications in several fields. We present novel semantic priors and an integrated approach for single image IID that involves analyzing image at three hierarchical context levels. Local context priors capture scene properties at each pixel within a small neighbourhood. Mid-level context priors encode object level semantics. Global context priors establish correspondences at the scene level. Our semantic priors are designed on both fixed and flexible regions, using selective search method and Convolutional Neural Network features. Our IID method is an iterative multistage optimization scheme and consists of two complementary formulations: L2 smoothing for shading and L1 sparsity for reflectance. Experiments and analysis of our method indicate the utility of our semantic priors and structured hierarchical analysis in an IID framework. We compare our method with other contemporary IID solutions and show results with lesser artifacts. Finally, we highlight that proper choice and encoding of prior knowledge can produce competitive results even when compared to end-to-end deep learning IID methods, signifying the importance of such priors. We believe that the insights and techniques presented in this paper would be useful in the future IID research.

Abstract

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Abstract

Photo realism in computer generated imagery is crucially dependent on how well an artist is able to recreate real-world materials in the scene. The workflow for material modeling and editing typically involves manual tweaking of material parameters and uses a standard path tracing engine for visual feedback. A lot of time may be spent in iterative selection and rendering of materials at an appropriate quality. In this work, we propose a convolutional neural network based workflow which quickly generates high-quality ray traced material visualizations on a shaderball. Our novel architecture allows for control over environment lighting and assists material selection along with the ability to render spatially-varying materials. Additionally, our network enables control over environment lighting which gives an artist more freedom and provides better visualization of the rendered material. Comparison with state-of-the-art denoising and neural rendering techniques suggests that our neural renderer performs faster and better. We provide a interactive visualization tool and release our training dataset to foster further research in this area.

Abstract

This document describes the machine translation system used in the submissions of IIIT-Hyderabad (CVIT-MT) for the WAT-2018 English-Hindi translation task. Performance is evaluated on the associated corpus provided by the organizers. We experimented with convolutional sequence to sequence architectures. We also train with additional data obtained through backtranslation.

Abstract

Abstract— For an autonomous corridor following task where the environment is continuously changing, several forms of environmental noise prevent an automated feature extraction procedure from performing reliably. Moreover, in cases where pre-defined features are absent from the captured data, a well defined control signal for performing the servoing task fails to get produced. In order to overcome these drawbacks, we present in this work, using a convolutional neural network (CNN) to directly estimate the required control signal from an image, encompassing feature extraction and control law computation into one single end-to-end framework. In particular, we study the task of autonomous corridor following using a CNN and present clear advantages in cases where a traditional method used for performing the same task fails to give a reliable outcome. We evaluate the performance of our method on this task on a Wheelchair Platform developed at our institute for this purpose.

Abstract

Current visual question answering datasets do not consider the rich semantic information conveyed by text within an image. In this work, we present a new dataset, ST-VQA, that aims to highlight the importance of exploiting high-level semantic information present in images as textual cues in the VQA process. We use this dataset to define a series of tasks of increasing difficulty for which reading the scene text in the context provided by the visual information is necessary to reason and generate an appropriate answer. We propose a new evaluation metric for these tasks to account both for reasoning errors as well as shortcomings of the text recognition module. In addition we put forward a series of baseline methods, which provide further insight to the newly released dataset, and set the scene for further research

Abstract

Understanding videos of people speaking across internationalborders is hard as audiences from different demographies donot understand the language. Such speech videos are oftensupplemented with language subtitles. However, these ham-per the viewing experience as the attention is shared. Simpleaudio dubbing in a different language makes the video appearunnatural due to unsynchronized lip motion. In this paper, wepropose a system for automated cross-language lip synchro-nization for re-dubbed videos. Our model generates superiorphotorealistic lip-synchronization over original video in com-parison to the current re-dubbing method. With the help of auser-based study, we verify that our method is preferred overunsynchronized videos

Abstract

Abstract— Driver inattention is one of the leading causesof vehicle crashes and incidents worldwide. Driver inattentionincludes driver fatigue leading to drowsiness and driver distrac-tion, say due to use of cellphone or rubbernecking, all of whichleads to a lack of situational awareness. Hitherto, techniquespresented to monitor driver attention evaluated factors suchas fatigue and distraction independently. However, in orderto develop a robust driver attention monitoring system allthe factors affecting driver’s attention needs to be analyzedholistically. In this paper, we presentAutoRate, a system thatleverages front camera of a windshield-mounted smartphoneto monitor driver’s attention by combining several features.We derive a driver attention rating by fusing spatio-temporalfeatures based on the driver state and behavior such as headpose, eye gaze, eye closure, yawns, use of cellphones, etc.We perform extensive evaluation ofAutoRateon real-world driving data and also data from controlled, static vehiclesettings with 30 drivers in a large city. We compareAutoRate’sautomatically-generated rating with the scores given by 5human annotators. Further, we compute the agreement betweenAutoRate’s rating and human annotator rating using kappacoefficient.AutoRate’s automatically-generated rating has anoverall agreement of 0.87 with the ratings provided by 5 humanannotators on the static dataset.

Abstract

AbstractFully supervised deep learning-based methods have created a profound im-pact in various fields of computer science. Compared to classical methods, superviseddeep learning-based techniques face scalability issues as they require huge amountsof labeled data and, more significantly, are unable to generalize to multiple domainsand tasks. In recent years, a lot of research has been targeted towards addressingthese issues within the deep learning community. Although there have been extensivesurveys on learning paradigms such as semi-supervised and unsupervised learning,there are few timely reviews after the emergence of deep learning. In this paper, weprovide an overview of the contemporary literature surrounding alternatives to fullysupervised learning in the deep learning context. First, we summarize the relevanttechniques that fall between the paradigm of supervised and unsupervised learning.Second, we take autonomous navigation as a running example to explain and com-pare different models. Finally, we highlight some shortcomings of current methodsand suggest future directions.