Abstract

As vision-based autonomous systems, such as selfdriving vehicles, become a reality, there is an increasing need for large annotated datasets for developing solutions to vision tasks. One important task that has seen significant interest in recent years is semantic segmentation. However, the cost of annotating every pixel for semantic segmentation is immense, and can be prohibitive in scaling to various settings and locations. In this paper, we propose a region-based active learning method for efficient labeling in semantic segmentation. Using the proposed active learning strategy, we show that we are able to judiciously select the regions for annotation such that we obtain 93.8% of the baseline performance (when all pixels are labeled) with labeling of 10% of the total number of pixels. Further, we show that this approach can be used to transfer annotations from a model trained on a given dataset (Cityscapes) to a different dataset (Mapillary), thus highlighting its promise and potential

Abstract

Do we know what the different filters of a face network represent? Can we use this filter information to train other tasks without transfer learning? For instance, can age, head pose, emotion and other face related tasks be learned from face recognition network without transfer learning? Understanding the role of these filters allows us to transfer knowledge across tasks and take advantage of large data sets in related tasks. Given a pretrained network, we can infer which tasks the network generalizes for and the best way to transfer the information to a new task. We demonstrate a computationally inexpensive algorithm to reuse the filters of a face network for a task it was not trained for. Our analysis proves these attributes can be extracted with an accuracy comparable to what is obtained with transfer learning, but 10 times faster. We show that the information about other tasks is present in relatively small number of filters. We use these insights to do task specific pruning of a pretrained network. Our method gives significant compression ratios with reduction in size of 95% and computational reduction of 60%

Abstract

Our goal is to spot words in silent speech videos without explicitly recognizing the spoken words, where the lip motion of the speaker is clearly visible and audio is absent. Existing work in this domain has mainly focused on recognizing a fixed set of words in word-segmented lip videos, which limits the applicability of the learned model due to limited vocabulary and high dependency on the model’s recognition performance. Our contribution is twofold: (1) we develop a pipeline for recognition-free retrieval and show its performance against recognition-based retrieval on a large-scale dataset and another set of out-of-vocabulary words. (2) We introduce a query expansion technique using pseudo-relevant feedback and propose a novel re-ranking method based on maximizing the correlation between spatiotemporal landmarks of the query and the top retrieval candidates. Our word spotting method achieves 35% higher mean average precision over recognition-based method on large-scale LRW dataset. We also demonstrate the application of the method by word spotting in a popular speech video (“The great dictator” by Charlie Chaplin) where we show that the word retrieval can be used to understand what was spoken perhaps in the silent movies. Finally, we compare our model against ASR in a noisy environment and analyze the effect of the performance of underlying lip-reader and input video quality on the proposed word spotting pipeline

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