41 citations · 50 across the 13 of their papers we have counts for
23 papers
Few-shot Prompting for Pairwise Ranking: An Effective Non-Parametric Retrieval Model
Nilanjan Sinhababu, Andrew Parry, Debasis Ganguly +2
A supervised ranking model, despite its advantage of being effective, usually involves complex processing - typically multiple stages of task-specific pre-training and fine-tuning.…
MLSD-GAN -- Generating Strong High Quality Face Morphing Attacks using Latent Semantic Disentanglement
Aravinda Reddy PN, Raghavendra Ramachandra, Krothapalli Sreenivasa Rao +1
Face-morphing attacks are a growing concern for biometric researchers, as they can be used to fool face recognition systems (FRS). These attacks can be generated at the image level…
Graph Expansion in Pruned Recurrent Neural Network Layers Preserve Performance
Suryam Arnav Kalra, Arindam Biswas, Pabitra Mitra +1
Expansion property of a graph refers to its strong connectivity as well as sparseness. It has been reported that deep neural networks can be pruned to a high degree of sparsity whi…
ExtSwap: Leveraging Extended Latent Mapper for Generating High Quality Face Swapping
Aravinda Reddy PN, K. Sreenivasa Rao, Raghavendra Ramachandra +1
We present a novel face swapping method using the progressively growing structure of a pre-trained StyleGAN. Previous methods use different encoder decoder structures, embedding in…
Optimizing Odia Braille Literacy: The Influence of Speed on Error Reduction and Enhanced Comprehension
Monnie Parida, Manjira Sinha, Anupam Basu +1
This study aims to conduct an extensive detailed analysis of the Odia Braille reading comprehension among students with visual disability. Specifically, the study explores their re…
Texture Aware Autoencoder Pre-training And Pairwise Learning Refinement For Improved Iris Recognition
Manashi Chakraborty, Aritri Chakraborty, Prabir Kumar Biswas +1
This paper presents a texture aware end-to-end trainable iris recognition system, specifically designed for datasets like iris having limited training data. We build upon our previ…