International Institute of Information Technology, Hyderabad

About: International Institute of Information Technology, Hyderabad is a education organization based out in Hyderabad, India. It is known for research contribution in the topics: Computer science & Authentication. The organization has 2048 authors who have published 3677 publications receiving 45319 citations. The organization is also known as: IIIT Hyderabad & International Institute of Information Technology (IIIT).

Fine-grain annotation of cricket videos

Abstract: The recognition of human activities is one of the key problems in video understanding. Action recognition is challenging even for specific categories of videos, such as sports, that contain only a small set of actions. Interestingly, sports videos are accompanied by detailed commentaries available online, which could be used to perform action annotation in a weakly-supervised setting. For the specific case of Cricket videos, we address the challenge of temporal segmentation and annotation of actions with semantic descriptions. Our solution consists of two stages. In the first stage, the video is segmented into "scenes", by utilizing the scene category information extracted from text-commentary. The second stage consists of classifying videoshots as well as the phrases in the textual description into various categories. The relevant phrases are then suitably mapped to the video-shots. The novel aspect of this work is the fine temporal scale at which semantic information is assigned to the video. As a result of our approach, we enable retrieval of specific actions that last only a few seconds, from several hours of video. This solution yields a large number of labelled exemplars, with no manual effort, that could be used by machine learning algorithms to learn complex actions.

Conversion from Paninian Karakas to Universal Dependencies for Hindi Dependency Treebank

Abstract: Universal Dependencies (UD) are gaining much attention of late for systematic evaluation of cross-lingual techniques for crosslingual dependency parsing. In this paper we present our work in line with UD. Our contribution to this is manifold. We extend UD to Indian languages through conversion of Pānịnian Dependencies to UD for the Hindi Dependency Treebank (HDTB). We discuss the differences in annotation in both the schemes, present parsing experiments for both the formalisms and empirically evaluate their weaknesses and strengths for Hindi. We produce an automatically converted Hindi Treebank conforming to the international standard UD scheme, making it useful as a resource for multilingual language technology.

Top-of-Atmosphere Radiative Cooling with White Roofs: Experimental Verification and Model-based Evaluation

Abstract: We evaluate differences in clear-sky upwelling shortwave radiation reaching the top of the atmosphere in response to increasing the albedo of roof surfaces in an area of India with moderately high aerosol loading. Treated (painted white) and untreated (unpainted) roofs on two buildings in northeast India were analyzed on five cloudless days using radiometric imagery from the IKONOS satellite. Comparison of a radiative transfer model (RRTMG) and radiometric satellite observations shows good agreement (R2 = 0.927). Results show a mean increase of ∼50 W m−2 outgoing at the top of the atmosphere for each 0.1 increase of the albedo at the time of the observations and a strong dependence on atmospheric transmissivity.

Towards View-Invariant Intersection Recognition from Videos using Deep Network Ensembles

Abstract: This paper strives to answer the following question: Is it possible to recognize an intersection when seen from different road segments that constitute the intersection? An intersection or a junction typically is a meeting point of three or four road segments. Its recognition from a road segment that is transverse to or 180 degrees apart from its previous sighting is an extremely challenging and yet a very relevant problem to be addressed from the point of view of both autonomous driving as well as loop detection. This paper formulates this as a problem of video recognition and proposes a novel LSTM based Siamese style deep network for video recognition. For what is indeed a challenging problem and the limited annotated dataset available we show competitive results of recognizing intersections when approached from diverse viewpoints or road segments. Specifically, we tabulate effective recognition accuracy even as the approaches to the intersection being compared are disparate both in terms of viewpoints and weather/illumination conditions. We show competitive results on both synthetic yet highly realistic data mined from the gaming platform GTA as well as on real world data made available through Mapillary.

Aesthetic guideline driven photography by robots

Abstract: Robots depend on captured images for perceiving the environment. A robot can replace a human in capturing quality photographs for publishing. In this paper, we employ an iterative photo capture by robots (by repositioning itself) to capture good quality photographs. Our image quality assessment approach is based on few high level features of the image combined with some of the aesthetic guidelines of professional photography. Our system can also be used in web image search applications to rank images. We test our quality assessment approach on a large and diversified dataset and our system is able to achieve a classification accuracy of 79%. We assess the aesthetic error in the captured image and estimate the change required in orientation of the robot to retake an aesthetically better photograph. Our experiments are conducted on NAO robot with no stereo vision. The results demonstrate that our system can be used to capture professional photographs which are in accord with the human professional photography.