13 citations · 23 across the 5 of their papers we have counts for
6 papers
Towards Exploring Fairness in Visual Transformer based Natural and GAN Image Detection Systems
Manjary P. Gangan, Anoop Kadan, Lajish V L
Image forensics research has recently witnessed a lot of advancements towards developing computational models capable of accurately detecting natural images captured by cameras and…
A Robust Image Forensic Framework Utilizing Multi-Colorspace Enriched Vision Transformer for Distinguishing Natural and Computer-Generated Images
Manjary P. Gangan, Anoop Kadan, Lajish V L
The digital image forensics based research works in literature classifying natural and computer generated images primarily focuses on binary tasks. These tasks typically involve th…
Blacks is to Anger as Whites is to Joy? Understanding Latent Affective Bias in Large Pre-trained Neural Language Models
Anoop Kadan, Deepak P., Sahely Bhadra +2
Groundbreaking inventions and highly significant performance improvements in deep learning based Natural Language Processing are witnessed through the development of transformer ba…
REDAffectiveLM: Leveraging Affect Enriched Embedding and Transformer-based Neural Language Model for Readers' Emotion Detection
Anoop Kadan, Deepak P., Manjary P. Gangan +2
Technological advancements in web platforms allow people to express and share emotions towards textual write-ups written and shared by others. This brings about different interesti…
Towards an Enhanced Understanding of Bias in Pre-trained Neural Language Models: A Survey with Special Emphasis on Affective Bias
Anoop K., Manjary P. Gangan, Deepak P. +1
The remarkable progress in Natural Language Processing (NLP) brought about by deep learning, particularly with the recent advent of large pre-trained neural language models, is bro…
Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet
Manjary P Gangan, Anoop K, Lajish V L
The problem of distinguishing natural images from photo-realistic computer-generated ones either addresses natural images versus computer graphics or natural images versus GAN imag…