Department of Computer Science & Engineering

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IIT Jodhpur

A graph is called a sum graph if its vertices can be labelled by distinct positive integers such that there is an edge between two vertices if and only if the sum of their labels is the label of another vertex of the graph. Most papers on sum graphs consider combinatorial questions like the minimum number of isolated vertices that need to be added to a given graph to make it a sum graph. In this paper, we initiate the study of sum graphs from the viewpoint of computational complexity. Note that every n-vertex sum graph can be represented by a sorted list of n positive integers where edge queries can be answered in O(log n) time. Thus, limiting the size of the vertex labels upper-bounds the space complexity of storing the graph. We show that every n-vertex, m-edge, d-degenerate graph can be made a sum graph by adding at most m isolated vertices to it such that the size of each vertex label is at most O(n2d). This enables us to store the graph using O(mlog n) bits of memory. For sparse graphs (graphs with O(n) edges), this matches the trivial lower bound of Ω(nlog n). Since planar graphs and forests have constant degeneracy, our result implies an upper bound of O(n2) on their label size. The previously best known upper bound on the label size of general graphs with the minimum number of isolated vertices was O(4n), due to Kratochvíl, Miller &amp; Nguyen [23]. Furthermore, their proof was existential whereas our labelling can be constructed in polynomial time. © 2021, Springer Nature Switzerland AG.

World University root node

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

The Space Complexity of Sum Labelling

Terrain, representing features of an earth surface, plays a crucial role in many applications such as simulations, route planning, analysis of surface dynamics, computer graphics-based games, entertainment, films, to name a few. With recent advancements in digital technology, these applications demand the presence of high resolution details in the terrain. In this paper, we propose a novel fully convolutional neural network based super-resolution architecture to increase the resolution of low-resolution Digital Elevation Model (LRDEM) with the help of information extracted from the corresponding aerial image as a complementary modality. We perform the super-resolution of LRDEM using an attention based feedback mechanism named ‘Attentional Feedback Network’ (AFN), which selectively fuses the information from LRDEM and aerial image to enhance and infuse the high-frequency features and to produce the terrain realistically. We compare the proposed architecture with existing state-of-the-art DEM super-resolution methods and show that the proposed architecture outperforms enhancing the resolution of input LRDEM accurately and in a realistic manner. © 2021, Springer Nature Switzerland AG.

AFN: Attentional Feedback Network Based 3D Terrain Super-Resolution

Rectification-based Knowledge Retention for Continual Learning

arXiv preprint arXiv:2112.12385

DILF-EN framework for Class-Incremental Learning

Restricted category removal from model representations using limited data

RNNP: A Robust Few-Shot Learning Approach

Zero-shot learning (ZSL) is one of the most promising avenues of annotation-efficient machine learning. In the era of deep learning, ZSL techniques have achieved unprecedented success. However, the developments of ZSL methods have taken place mostly for natural images. ZSL for medical images has remained largely unexplored. We design a novel strategy for generalized zero-shot diagnosis of chest radiographs. In doing so, we leverage the potential of multi-view semantic embedding, a useful yet less-explored direction for ZSL. Our design also incorporates a self-training phase to tackle the problem of noisy labels alongside improving the performance for classes not seen during training. Through rigorous experiments, we show that our model trained on one dataset can produce consistent performance across test datasets from different sources including those with very different quality. Comparisons with a number of state-of-the-art techniques show the superiority of the proposed method for generalized zero-shot chest x-ray diagnosis. IEEE

IEEE Transactions on Medical Imaging

Generalized Zero-shot Chest X-ray Diagnosis through Trait-Guided Multi-view Semantic Embedding with Self-training

Presentation attacks are posing major challenges to most of the biometric modalities. Iris recognition, which is considered as one of the most accurate biometric modality for person identification, has also been shown to be vulnerable to advanced presentation attacks such as 3D contact lenses and textured lens. While in the literature, several presentation attack detection (PAD) algorithms are presented; a significant limitation is the generalizability against an unseen database, unseen sensor, and different imaging environment. To address this challenge, we propose a generalized deep learning-based PAD network, MVANet, which utilizes multiple representation layers. It is inspired by the simplicity and success of hybrid algorithm or fusion of multiple detection networks. The computational complexity is an essential factor in training deep neural networks; therefore, to reduce the computational complexity while learning multiple feature representation layers, a fixed base model has been u. The performance of the proposed network is demonstrated on multiple databases such as IIITD-WVU MUIPA and IIITD-CLI databases under cross-database training-testing settings, to assess the generalizability of the proposed algorithm.

Proceedings - International Conference on Pattern Recognition

Generalized Iris Presentation Attack Detection Algorithm under Cross-Database Settings

The non-intrusive nature and high accuracy of face recognition algorithms have led to their successful deployment across multiple applications ranging from border access to mobile unlocking and digital payments. However, their vulnerability against sophisticated and cost-effective presentation attack mediums raises essential questions regarding its reliability. In the literature, several presentation attack detection algorithms are presented; however, they are still far behind from reality. The major problem with existing work is the generalizability against multiple attacks both in the seen and unseen setting. The algorithms which are useful for one kind of attack (such as print) perform unsatisfactorily for another type of attack (such as silicone masks). In this research, we have proposed a deep learning-based network termed as MixNet to detect presentation attacks in cross-database and unseen attack settings. The proposed algorithm utilizes state-of-the-art convolutional neural network architectures and learns the feature mapping for each attack category. Experiments are performed using multiple challenging face presentation attack databases such as SMAD and Spoof In the Wild (SiW-M) databases. Extensive experiments and comparison with existing state of the art algorithms show the effectiveness of the proposed algorithm.

MixNet for Generalized Face Presentation Attack Detection

Cross-linked polyethylene (XLPE) is employed in high-voltage transmission cables, due to their distinguished insulation performance. Localized degradation due to AC voltages is a major cause of damage in these systems. These damages known as electrical trees vary in their shape and characteristics according to the cause of damage. This paper discusses the effect of lightning overvoltages in trees formed in these insulation systems. Initially experiments have been conducted to generate trees at 10 kV (tree-like tree) and 12 kV (bush branch tree) and the structure of the trees are visualized. This work is extended to study the effect of lightning overvoltages. It is crucial to detect the type of damage as accurately as possible. In this research work, a classification tree model, which is based on the Bag of Visual Words (BoVW), has been developed to predict the category of damage. The accuracy of the proposed model is evaluated using Receiver Operating Characteristics (ROCs) curves and confusion matrix. Finally, the validity of the developed model is verified by comparing it with the in-house collected experimental data and the other State-of-the-Art feature extraction techniques. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Electrical Engineering

A model based on bag of visual words to predict the category of damage in XLPE insulation under the application of combined AC and repeated lightning impulses of both polarities