10 citations · 16 across the 18 of their papers we have counts for
20 papers
Rethinking Knowledge Retrieval for Generation: A Survey on RAG Architectures and Applications
Meghana Sunil, Shravya V, Shravan Venkatraman +1
Large Language Models (LLMs) have demonstrated remarkable fluency and versatility across natural language tasks but remain fundamentally limited by their static knowledge and susce…
Temporal Self-Distillation: Learning Visual State Tracking in Videos Without Supervision
Shravan Venkatraman, Wenshuai Zhao, Mohammad Hassan Vali +1
We introduce ST (Self-Supervised Self-Distillation over Time), which, to the best of our knowledge, is the first fully self-contained framework for continuous video state track…
Perception Before Supervision: Self-Contained Visual Distillation from Counterfactual Blind Spots
Shravan Venkatraman, Omkar Thawakar, Ritesh Thawkar +2
Self-improvement for multimodal large language models (MLLMs) is typically driven by reward-based methods that provide only coarse scalar feedback. Distillation offers a richer alt…
Ask, Solve, Generate: Self-Evolving Unified Multimodal Understanding and Generation via Self-Consistency Rewards
Ritesh Thawkar, Shravan Venkatraman, Omkar Thawakar +5
Most unified large multimodal models (LMMs) that support both visual understanding and image generation still rely on curated post-training supervision, such as human annotations,…
Paying More Attention to Visual Tokens in Self-Evolving Large Multimodal Models
Shravan Venkatraman, Ritesh Thawkar, Omkar Thawakar +4
Recently, self-evolving large multimodal models (LMMs) have received attention for improving visual reasoning in a purely unsupervised setting. However, multi-role self-play and se…
PCM-NeRF: Probabilistic Camera Modeling for Neural Radiance Fields under Pose Uncertainty
Shravan Venkatraman, Rakesh Raj Madavan, Pavan Kumar Sathya Venkatesh
Neural surface reconstruction methods typically treat camera poses as fixed values, assuming perfect accuracy from Structure-from-Motion (SfM) systems. This assumption breaks down…
TIDE: Two-Stage Inverse Degradation Estimation with Guided Prior Disentanglement for Underwater Image Restoration
Shravan Venkatraman, Rakesh Raj Madavan, Pavan Kumar S +1
Underwater image restoration is essential for marine applications ranging from ecological monitoring to archaeological surveys, but effectively addressing the complex and spatially…
Can We Go Beyond Visual Features? Neural Tissue Relation Modeling for Relational Graph Analysis in Non-Melanoma Skin Histology
Shravan Venkatraman, Muthu Subash Kavitha, Joe Dhanith P R +2
Histopathology image segmentation is essential for delineating tissue structures in skin cancer diagnostics, but modeling spatial context and inter-tissue relationships remains a c…
EvoLMM: Self-Evolving Large Multimodal Models with Continuous Rewards
Omkar Thawakar, Shravan Venkatraman, Ritesh Thawkar +5
Recent advances in large multimodal models (LMMs) have enabled impressive reasoning and perception abilities, yet most existing training pipelines still depend on human-curated dat…
UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography
Shravan Venkatraman, Pavan Kumar S, Rakesh Raj Madavan +1
Accurate classification of computed tomography (CT) images is essential for diagnosis and treatment planning, but existing methods often struggle with the subtle and spatially dive…
FUSION: Frequency-guided Underwater Spatial Image recOnstructioN
Jaskaran Singh Walia, Shravan Venkatraman, Pavithra LK
Underwater images suffer from severe degradations, including color distortions, reduced visibility, and loss of structural details due to wavelength-dependent attenuation and scatt…
A Novel Adaptive Hybrid Focal-Entropy Loss for Enhancing Diabetic Retinopathy Detection Using Convolutional Neural Networks
Santhosh Malarvannan, Pandiyaraju V, Shravan Venkatraman +3
Diabetic retinopathy is a leading cause of blindness around the world and demands precise AI-based diagnostic tools. Traditional loss functions in multi-class classification, such…
SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers
Shravan Venkatraman, Jaskaran Singh Walia, Joe Dhanith P R
Vision Transformers (ViTs) have redefined image classification by leveraging self-attention to capture complex patterns and long-range dependencies between image patches. However,…
A Novel Self-Attention-Enabled Weighted Ensemble-Based Convolutional Neural Network Framework for Distributed Denial of Service Attack Classification
Kanthimathi S, Shravan Venkatraman, Jayasankar K S +2
Distributed Denial of Service (DDoS) attacks are a major concern in network security, as they overwhelm systems with excessive traffic, compromise sensitive data, and disrupt netwo…
Targeted Neural Architectures in Multi-Objective Frameworks for Complete Glioma Characterization from Multimodal MRI
Shravan Venkatraman, Pandiyaraju V, Abeshek A +4
Brain tumors result from abnormal cell growth in brain tissue. If undiagnosed, they cause neurological deficits, including cognitive impairment, motor dysfunction, and sensory loss…
Leveraging SeNet and ResNet Synergy within an Encoder-Decoder Architecture for Glioma Detection
Pandiyaraju V, Shravan Venkatraman, Abeshek A +2
Brain tumors are abnormalities that can severely impact a patient's health, leading to life-threatening conditions such as cancer. These can result in various debilitating effects,…
A Channel Attention-Driven Hybrid CNN Framework for Paddy Leaf Disease Detection
Pandiyaraju V, Shravan Venkatraman, Abeshek A +4
Farmers face various challenges when it comes to identifying diseases in rice leaves during their early stages of growth, which is a major reason for poor produce. Therefore, early…
Multimodal Emotion Recognition using Audio-Video Transformer Fusion with Cross Attention
Joe Dhanith P R, Shravan Venkatraman, Vigya Sharma +1
Multimodal emotion recognition (MER) aims to infer human affect by jointly modeling audio and visual cues; however, existing approaches often struggle with temporal misalignment, w…
Leveraging Bi-Focal Perspectives and Granular Feature Integration for Accurate Reliable Early Alzheimer's Detection
Shravan Venkatraman, Pandiyaraju V, Abeshek A +2
Being the most commonly known neurodegeneration, Alzheimer's Disease (AD) is annually diagnosed in millions of patients. The present medical scenario still finds the exact diagnosi…
Exploiting Precision Mapping and Component-Specific Feature Enhancement for Breast Cancer Segmentation and Identification
Pandiyaraju V, Shravan Venkatraman, Pavan Kumar S +2
Breast cancer is one of the leading causes of death globally, and thus there is an urgent need for early and accurate diagnostic techniques. Although ultrasound imaging is a widely…