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: Authentication & Internet security. 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).

Improved Visual Relocalization by Discovering Anchor Points.

Abstract: We address the visual relocalization problem of predicting the location and camera orientation or pose (6DOF) of the given input scene. We propose a method based on how humans determine their location using the visible landmarks. We define anchor points uniformly across the route map and propose a deep learning architecture which predicts the most relevant anchor point present in the scene as well as the relative offsets with respect to it. The relevant anchor point need not be the nearest anchor point to the ground truth location, as it might not be visible due to the pose. Hence we propose a multi task loss function, which discovers the relevant anchor point, without needing the ground truth for it. We validate the effectiveness of our approach by experimenting on CambridgeLandmarks (large scale outdoor scenes) as well as 7 Scenes (indoor scenes) using variousCNN feature extractors. Our method improves the median error in indoor as well as outdoor localization datasets compared to the previous best deep learning model known as PoseNet (with geometric re-projection loss) using the same feature extractor. We improve the median error in localization in the specific case of Street scene, by over 8m.

Attentive neural architecture incorporating song features for music recommendation

Abstract: Recommender Systems are an integral part of music sharing platforms. Often the aim of these systems is to increase the time, the user spends on the platform and hence having a high commercial value. The systems which aim at increasing the average time a user spends on the platform often need to recommend songs which the user might want to listen to next at each point in time. This is different from recommendation systems which try to predict the item which might be of interest to the user at some point in the user lifetime but not necessarily in the very near future. Prediction of next song the user might like requires some kind of modeling of the user interests at the given point of time. Attentive neural networks have been exploiting the sequence in which the items were selected by the user to model the implicit short-term interests of the user for the task of next item prediction, however we feel that features of the songs occurring in the sequence could also convey some important information about the short-term user interest which only the items cannot. In this direction we propose a novel attentive neural architecture which in addition to the sequence of items selected by the user, uses the features of these items to better learn the user short-term preferences and recommend next song to the user.

Improving Small Object Detection

Abstract: While the problem of detecting generic objects in natural scene images has been the subject of research for a long time, the problem of detection of small objects has been largely ignored. While generic object detectors perform well on medium and large sized objects, they perform poorly for the overall task of recognition of small objects. This is because of the low resolution and simple shape of most small objects. In this work, we suggest a simple yet effective upsampling-based technique that performs better than the current state-of-the-art for end-to-end small object detection. Like most recent methods, we generate proposals and then classify them. We suggest improvements to both these steps for the case of small objects.

Joining Hands: Exploiting Monolingual Treebanks for Parsing of Code-mixing Data.

Abstract: In this paper, we propose efficient and less resource-intensive strategies for parsing of code-mixed data. These strategies are not constrained by in-domain annotations, rather they leverage pre-existing monolingual annotated resources for training. We show that these methods can produce significantly better results as compared to an informed baseline. Due to lack of an evaluation set for code-mixed structures, we also present a data set of 450 Hindi and English code-mixed tweets of Hindi multilingual speakers for evaluation.

An effective association rule mining scheme using a new generic basis

Abstract: Association rule mining among itemsets is a fundamental task and is of great importance in many data mining applications including attacks in network data, stock market, financial applications, bioinformatics to find genetic disorders, etc. However, association rule extraction from a reasonable-sized database produces a large number of rules. As a result, many of them are redundant to other rules, and they are practically useless. To overcome this issue, methods for mining non-redundant rules are essentially required. To address such problem, we initially propose a definition for redundancy in sense of minimal knowledge and then a compact representation of non-redundant association rules which we call as compact informative generic basis. We also provide an improved version of the existing DCI_CLOSED algorithm (DCI_PLUS) to find out the frequent closed itemsets (FCI) with their minimal representative generators in combination with BitTable which represents a compact database form in a single scan of the original database. We further introduce an algorithm for constructing the compact informative generic basis from the FCI and their generators in an efficient way. We finally present an inference mechanism in which all association rules can be generated without accessing the database. Experiments are performed on the proposed method. The experimental results show that the proposed method outperforms the other existing related methods.