Abstract

Given an image or a video captured from a monocular camera, amodal layout estimation is the task of predicting semantics and occupancy in bird's eye view. The term amodal implies we also reason about entities in the scene that are occluded or truncated in image space. While several recent efforts have tackled this problem, there is a lack of standardization in task specification, datasets, and evaluation protocols. We address these gaps with AutoLay, a dataset and benchmark for amodal layout estimation from monocular images. AutoLay encompasses driving imagery from two popular datasets: KITTI [1] and Argoverse [2]. In addition to fine-grained attributes such as lanes, sidewalks, and vehicles, we also provide semantically annotated 3D point clouds. We implement several baselines and bleeding edge approaches, and release our data and code

Abstract

With the growing use of social media and its availability, many instances of the use of offensive language have been observed across multiple languages and domains. This phenomenon has given rise to the growing need to detect the offensive language used in social media crosslingually. In OffensEval 2020, the organizers have released the multilingual Offensive Language Identification Dataset (mOLID), which contains tweets in five different languages, to detect offensive language. In this work, we introduce a cross-lingual inductive approach to identify the offensive language in tweets using the contextual word embedding XLM-RoBERTa (XLM-R). We show that our model performs competitively on all five languages, obtaining the fourth position in the English task with an F1-score of 0.919 and eighth position in the Turkish task with an F1-score of 0.781. Further experimentation proves that our model works competitively in a zero-shot learning environment, and is extensible to other languages.

Abstract

Internet of Things (IoT) is an emerging technology, which integrates all everyday objects and services into the com- mon network platform like Internet. With the help of IoT, every physical object is converted as smart object. Each of the physical object is identified uniquely and can sense the information in the environment and to take decisions based on that. IoT is useful in many of the applications, including home automation, retail, industry control, agriculture, health monitoring, etc. In healthcare domain, some of the IoT-based applications include, monitoring old people for fall detection, monitoring temperature conditions inside refrigerator for storing medicines, vaccines, and monitoring of patient’s condition in hospitals and home. Machine Learning applications in Healthcare also seems to be endless. In this paper, we have investigated IoT based applications in healthcare and performed an experimental analysis of he

Abstract

In recent years, the increased demand for wireless multimedia services has led to huge bandwidth crisis. The concept of Cognitive Radio Networks (CRN) has been proposed as a solution. In CRN, higher Primary User Activity leads to an increased number of Pre-empted Secondary Users (PSU). Since the PSUs are given priority over New Secondary Users (NSU), the NSUs might get starved. Due to this priority of PSUs less number low priority users can be served. The advent of the Internet of Things, the traffic pattern has become unpredictable. All this reasons required a Dynamic Cognitive Channel Allocation (DCCA) algorithm which has been proposed in this work which balances the service among the PSUs and NSUs as per priority of SU as well as avoiding starvation. Analysis of the proposed algorithm using Continuous Time Markov Chain Model shows an improvement over existing literature of 44.44% in NSU dropping probability and 22.8% improvement in NSU blocking probability with respect to arrival of PUs. Varying the arrival of SUs has resulted in an improvement of 58% and 45.75% in the dropping and blocking probabilities of NSUs respectively. The starvation of SUs has lowered by 32.52% with varying arrival rate of PU and 41.38% with varying arrival rate of SUs

Abstract

Unmanned Aerial Vehicle (UAV)-enabled mobile edge computing (MEC) is considered to offer computational capabilities to the resource-constraints end-users. In this paper, we study the task offloading strategy in UAV-enabled MEC systems, where end-users offload the computation-intensive tasks to the UAV to minimize the overall cost in terms of the weighted delay and energy consumption. The end-users either process the task by itself or offload the tasks to the UAV that acts as a computing access point. However, due to the computation bottleneck and limited channel capacity between UAV and the end-users, it becomes a challenging issue to offload the entire tasks to the UAV. Thus, to find the optimal offloading decision for the tasks generated by the end-users, we build a distributed deep neural network (DNN). In the proposed distributed DNN model, we train multiple DNNs in the same training instance, and finally, for validation, we select the DNN that gives the least training loss. For faster convergence of the training process, we use the optimal generated offloading decision using a Quadratically Constrained Linear Program (QCLP) with Semidefinite Relaxation (SDR). The extensive simulation results show that the offloading decision produced by the trained DNN can achieve near-optimal performance with numerous system parameter settings.

Abstract

With the rising need for reliable and real-time pose estimation in resource constrained environments such as smartphones, IoT devices, and head mounts, we need an efficient and compact pose estimation framework. To this end, we propose PoseFromGraph1, a light-weight 3D pose estimation framework. The inputs to PoseFromGraph are: a graph obtained by skeletonizing the 3D meshes using the prairie-fire analogy and the RGB image, and the output is the 3D pose of the object. The introduction of 3D shapes to the architecture makes our model category-agnostic. Unlike computationally expensive multi-view geometry and point-cloud based representations to estimate pose, our approach uses a message passing network to incorporate local neighborhood information at the same time maintaining global shape property in a graph by optimizing a neighborhood preserving objective. PoseFromGraph surpasses the state-of-the-art pose estimation methods in terms of accuracy achieving, 84.43% on the Pascal3D dataset, and at the same time yields 4 × reduction in the space and time complexity. The compact pose estimation models can then be used to facilitate on-device inference in applications in Augmented Reality and Robotics for 3D virtual model overlay.

Abstract

3While deep learning approaches have shown promising results in Natural Language Processing and Computer Vision domains, they have not yet been able to achieve impressive results in Ontology Alignment, and have typically performed worse than rule-based approaches. Some of the major reasons for this are: a) poor modelling of context, b) overfitting of standard DL models, and c) dataset sparsity, caused by class imbalance of positive alignment pairs wrt negative pairs. To mitigate these limitations, we propose a dual-attention based approach that uses a multi-faceted context representation to compute contextualized representations of concepts, which is then used to discover semantically equivalent concepts.

Abstract

Few-shot segmentation (FSS) methods perform image segmentation for a particular object class in a target (query) image, using a small set of (support) image-mask pairs. Recent deep neural network based FSS methods leverage high-dimensional feature similarity between the foreground features of the support images and the query image features. In this work, we demonstrate gaps in the utilization of this similarity information in existing methods, and present a framework - SimPropNet, to bridge those gaps. We propose to jointly predict the support and query masks to force the support features to share characteristics with the query features. We also propose to utilize similarities in the background regions of the query and support images using a novel foreground-background attentive fusion mechanism. Our method achieves stateof-the-art results for one-shot and five-shot segmentation on the PASCAL-5 i dataset. The paper includes detailed analysis and ablation studies for the proposed improvements and quantitative comparisons with contemporary methods.

Abstract

This paper describes the participation of team F1toF6 (LTRC, IIIT-Hyderabad) for the WMT 2020 task, similar language translation. We experimented with attention based recurrent neural network architecture (seq2seq) for this task. We explored the use of different linguistic features like POS and Morph along with back translation for Hindi-Marathi and Marathi-Hindi machine translation.

Abstract

The adoption of Machine Learning (ML) in software applications has increased in domains like healthcare, banking and others. leading to coining of the term MLWare applications. However, challenges like diverse code base, complex components, lack of expertise, etc. persist in development and maintenance of these applications. Application of software engineering patterns and practices for the development of MLWare applications can improve maintainability, extensibility, scalability and other software quality parameters. In this paper, we propose an approach for developing MLWare applications using a pattern oriented approach. We demonstrate the approach on a credit risk scorecard application that can helps loan officer identify risk patterns and make loan decisions.