65 citations · 207 across the 20 of their papers we have counts for
12 papers · 1 filter
Forget Less by Learning Together through Concept Consolidation
Arjun Ramesh Kaushik, Naresh Kumar Devulapally, Vishnu Suresh Lokhande +2
Custom Diffusion Models (CDMs) have gained significant attention due to their remarkable ability to personalize generative processes. However, existing CDMs suffer from catastrophi…
Learning Action Hierarchies via Hybrid Geometric Diffusion
Arjun Ramesh Kaushik, Nalini K. Ratha, Venu Govindaraju
Temporal action segmentation is a critical task in video understanding, where the goal is to assign action labels to each frame in a video. While recent advances leverage iterative…
Forget Less by Learning from Parents Through Hierarchical Relationships
Arjun Ramesh Kaushik, Naresh Kumar Devulapally, Vishnu Suresh Lokhande +2
Custom Diffusion Models (CDMs) offer impressive capabilities for personalization in generative modeling, yet they remain vulnerable to catastrophic forgetting when learning new con…
Aligning Characteristic Descriptors with Images for Human-Expert-like Explainability
Bharat Chandra Yalavarthi, Nalini Ratha
In mission-critical domains such as law enforcement and medical diagnosis, the ability to explain and interpret the outputs of deep learning models is crucial for ensuring user tru…
RidgeBase: A Cross-Sensor Multi-Finger Contactless Fingerprint Dataset
Bhavin Jawade, Deen Dayal Mohan, Srirangaraj Setlur +2
Contactless fingerprint matching using smartphone cameras can alleviate major challenges of traditional fingerprint systems including hygienic acquisition, portability and presenta…
HEFT: Homomorphically Encrypted Fusion of Biometric Templates
Luke Sperling, Nalini Ratha, Arun Ross +1
This paper proposes a non-interactive end-to-end solution for secure fusion and matching of biometric templates using fully homomorphic encryption (FHE). Given a pair of encrypted…
Deep Learning for Face Recognition: Pride or Prejudiced?
Shruti Nagpal, Maneet Singh, Richa Singh +1
Do very high accuracies of deep networks suggest pride of effective AI or are deep networks prejudiced? Do they suffer from in-group biases (own-race-bias and own-age-bias), and mi…
Diversity in Faces
Michele Merler, Nalini Ratha, Rogerio S. Feris +1
Face recognition is a long standing challenge in the field of Artificial Intelligence (AI). The goal is to create systems that accurately detect, recognize, verify, and understand…
Understanding Unequal Gender Classification Accuracy from Face Images
Vidya Muthukumar, Tejaswini Pedapati, Nalini Ratha +7
Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender. Acc…
Recognizing Disguised Faces in the Wild
Maneet Singh, Richa Singh, Mayank Vatsa +2
Research in face recognition has seen tremendous growth over the past couple of decades. Beginning from algorithms capable of performing recognition in constrained environments, th…
Heterogeneity Aware Deep Embedding for Mobile Periocular Recognition
Rishabh Garg, Yashasvi Baweja, Soumyadeep Ghosh +3
Mobile biometric approaches provide the convenience of secure authentication with an omnipresent technology. However, this brings an additional challenge of recognizing biometric p…
Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks
Gaurav Goswami, Nalini Ratha, Akshay Agarwal +2
Deep neural network (DNN) architecture based models have high expressive power and learning capacity. However, they are essentially a black box method since it is not easy to mathe…