2 citations · 4 across the 10 of their papers we have counts for
10 papers
Analysis of Federated Aggregation under Model Poisoning and Backdoor Attacks: A Reconstructed Cross-Dataset and Cross-Architecture Benchmark
Soumya Mazumdar, Vineet Kumar Rakesh, Tapas Samanta
Robust comparisons of federated aggregation methods require joint consideration of predictive performance, threat definitions, metric semantics, and execution provenance. A 500-cel…
DG-FedReuse: Proxy-Gradient-Gated Cached-Update Reuse with Matched Sparse Uplink Accounting
Rahil Aftab, Vineet Kumar Rakesh, Soumya Mazumdar +1
Federated learning repeatedly incurs local optimization and model-update transmission. We study DG-FedReuse, a simulator-level mechanism that allows selected clients to contribute…
RES-DARE: Failure-Aware Expert Adaptation and Rollback-Safe Self-Repair for Intrusion Detection
Rahil Aftab, Anyash Prasad, Soumya Mazumdar +2
Intrusion detection systems are often trained under static benchmark conditions, although deployed network environments are affected by traffic drift, sensor noise, changing worklo…
PrivFedTalk: Privacy-Aware Federated Diffusion with Identity-Stable Adapters for Personalized Talking-Head Generation
Soumya Mazumdar, Vineet Kumar Rakesh, Tapas Samanta
Talking-head generation has advanced rapidly with diffusion-based generative models, but training usually depends on centralized face-video and speech datasets, raising major priva…
TempoSyncDiff: Distilled Temporally-Consistent Diffusion for Low-Latency Audio-Driven Talking Head Generation
Soumya Mazumdar, Vineet Kumar Rakesh
Diffusion models have recently advanced photorealistic human synthesis, although practical talking-head generation (THG) remains constrained by high inference latency, temporal ins…
BayesFusion-SDF: Probabilistic Signed Distance Fusion with View Planning on CPU
Soumya Mazumdar, Vineet Kumar Rakesh, Tapas Samanta
Key part of robotics, augmented reality, and digital inspection is dense 3D reconstruction from depth observations. Traditional volumetric fusion techniques, including truncated si…
VineetVC: Adaptive Video Conferencing Under Severe Bandwidth Constraints Using Audio-Driven Talking-Head Reconstruction
Vineet Kumar Rakesh, Soumya Mazumdar, Tapas Samanta +3
Intense bandwidth depletion within consumer and constrained networks has the potential to undermine the stability of real-time video conferencing: encoder rate management becomes s…
VedicTHG: Symbolic Vedic Computation for Low-Resource Talking-Head Generation in Educational Avatars
Vineet Kumar Rakesh, Ahana Bhattacharjee, Soumya Mazumdar +4
Talking-head avatars are increasingly adopted in educational technology to deliver content with social presence and improved engagement. However, many recent talking-head generatio…
Analysis of Hyperparameter Optimization Effects on Lightweight Deep Models for Real-Time Image Classification
Vineet Kumar Rakesh, Soumya Mazumdar, Tapas Samanta +2
Lightweight convolutional and transformer-based networks are increasingly preferred for real-time image classification, especially on resource-constrained devices. This study evalu…
Advancing Talking Head Generation: A Comprehensive Survey of Multi-Modal Methodologies, Datasets, Evaluation Metrics, and Loss Functions
Vineet Kumar Rakesh, Soumya Mazumdar, Research Pratim Maity +3
Talking Head Generation (THG) has emerged as a transformative technology in computer vision, enabling the synthesis of realistic human faces synchronized with image, audio, text, o…