28 citations · 37 across the 17 of their papers we have counts for
19 papers
Toward System-of-Systems Integration for Composable Cloud-HPC-Edge AI Platforms
Sumit Rakesh, Rajkumar Saini
Modern AI platforms increasingly combine infrastructure stacks and operating models designed around different assumptions, including cloud-style service platforms, HPC workload-man…
Modified TSception for Analyzing Driver Drowsiness and Mental Workload from EEG
Gourav Siddhad, Anurag Singh, Rajkumar Saini +1
Driver drowsiness is a leading cause of traffic accidents, necessitating real-time, reliable detection systems to ensure road safety. This study proposes a Modified TSception archi…
LCM: Log Conformal Maps for Robust Representation Learning to Mitigate Perspective Distortion
Meenakshi Subhash Chippa, Prakash Chandra Chhipa, Kanjar De +2
Perspective distortion (PD) leads to substantial alterations in the shape, size, orientation, angles, and spatial relationships of visual elements in images. Accurately determining…
Giving each task what it needs -- leveraging structured sparsity for tailored multi-task learning
Richa Upadhyay, Ronald Phlypo, Rajkumar Saini +1
In the Multi-task Learning (MTL) framework, every task demands distinct feature representations, ranging from low-level to high-level attributes. It is vital to address the specifi…
Open-Vocabulary Object Detectors: Robustness Challenges under Distribution Shifts
Prakash Chandra Chhipa, Kanjar De, Meenakshi Subhash Chippa +2
The challenge of Out-Of-Distribution (OOD) robustness remains a critical hurdle towards deploying deep vision models. Vision-Language Models (VLMs) have recently achieved groundbre…
Möbius Transform for Mitigating Perspective Distortions in Representation Learning
Prakash Chandra Chhipa, Meenakshi Subhash Chippa, Kanjar De +3
Perspective distortion (PD) causes unprecedented changes in shape, size, orientation, angles, and other spatial relationships of visual concepts in images. Precisely estimating cam…
Vehicle Detection Performance in Nordic Region
Hamam Mokayed, Rajkumar Saini, Oluwatosin Adewumi +5
This paper addresses the critical challenge of vehicle detection in the harsh winter conditions in the Nordic regions, characterized by heavy snowfall, reduced visibility, and low…
Can Self-Supervised Representation Learning Methods Withstand Distribution Shifts and Corruptions?
Prakash Chandra Chhipa, Johan Rodahl Holmgren, Kanjar De +2
Self-supervised learning in computer vision aims to leverage the inherent structure and relationships within data to learn meaningful representations without explicit human annotat…
Less is More -- Towards parsimonious multi-task models using structured sparsity
Richa Upadhyay, Ronald Phlypo, Rajkumar Saini +1
Model sparsification in deep learning promotes simpler, more interpretable models with fewer parameters. This not only reduces the model's memory footprint and computational needs…
Performance of data-driven inner speech decoding with same-task EEG-fMRI data fusion and bimodal models
Holly Wilson, Scott Wellington, Foteini Simistira Liwicki +13
Decoding inner speech from the brain signal via hybridisation of fMRI and EEG data is explored to investigate the performance benefits over unimodal models. Two different bimodal f…
Robust and Fast Vehicle Detection using Augmented Confidence Map
Hamam Mokayed, Palaiahnakote Shivakumara, Lama Alkhaled +4
Vehicle detection in real-time scenarios is challenging because of the time constraints and the presence of multiple types of vehicles with different speeds, shapes, structures, et…
Functional Knowledge Transfer with Self-supervised Representation Learning
Prakash Chandra Chhipa, Muskaan Chopra, Gopal Mengi +7
This work investigates the unexplored usability of self-supervised representation learning in the direction of functional knowledge transfer. In this work, functional knowledge tra…
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning Conventions
Gustav Grund Pihlgren, Konstantina Nikolaidou, Prakash Chandra Chhipa +4
In recent years, deep perceptual loss has been widely and successfully used to train machine learning models for many computer vision tasks, including image synthesis, segmentation…
Depth Contrast: Self-Supervised Pretraining on 3DPM Images for Mining Material Classification
Prakash Chandra Chhipa, Richa Upadhyay, Rajkumar Saini +4
This work presents a novel self-supervised representation learning method to learn efficient representations without labels on images from a 3DPM sensor (3-Dimensional Particle Mea…
Multi-Task Meta Learning: learn how to adapt to unseen tasks
Richa Upadhyay, Prakash Chandra Chhipa, Ronald Phlypo +2
This work proposes Multi-task Meta Learning (MTML), integrating two learning paradigms Multi-Task Learning (MTL) and meta learning, to bring together the best of both worlds. In pa…
Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images
Prakash Chandra Chhipa, Richa Upadhyay, Gustav Grund Pihlgren +3
This work presents a novel self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors. O…
Sharing to learn and learning to share; Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning: A meta review
Richa Upadhyay, Ronald Phlypo, Rajkumar Saini +1
Integrating knowledge across different domains is an essential feature of human learning. Learning paradigms such as transfer learning, meta-learning, and multi-task learning refle…
ICDAR 2019 Historical Document Reading Challenge on Large Structured Chinese Family Records
Rajkumar Saini, Derek Dobson, Jon Morrey +2
We propose a Historical Document Reading Challenge on Large Chinese Structured Family Records, in short ICDAR2019 HDRC CHINESE. The objective of the proposed competition is to reco…
Recognizing Gender from Human Facial Regions using Genetic Algorithm
Avirup Bhattacharyya, Rajkumar Saini, Partha Pratim Roy +2
Recently, recognition of gender from facial images has gained a lot of importance. There exist a handful of research work that focus on feature extraction to obtain gender specific…