3 citations · 6 across the 6 of their papers we have counts for
6 papers
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…
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…
Nordic Vehicle Dataset (NVD): Performance of vehicle detectors using newly captured NVD from UAV in different snowy weather conditions
Hamam Mokayed, Amirhossein Nayebiastaneh, Kanjar De +3
Vehicle detection and recognition in drone images is a complex problem that has been used for different safety purposes. The main challenge of these images is captured at oblique a…
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…
Movie Recommendation System using Sentiment Analysis from Microblogging Data
Sudhanshu Kumar, Shirsendu Sukanta Halder, Kanjar De +1
Recommendation systems are important intelligent systems that play a vital role in providing selective information to users. Traditional approaches in recommendation systems includ…
Perceptual Conditional Generative Adversarial Networks for End-to-End Image Colourization
Shirsendu Sukanta Halder, Kanjar De, Partha Pratim Roy
Colours are everywhere. They embody a significant part of human visual perception. In this paper, we explore the paradigm of hallucinating colours from a given gray-scale image. Th…