Dr. Dhruv Sharma | Computer Vision | Best Researcher Award

Dr. Dhruv Sharma has made extensive contributions to the domains of artificial intelligence, deep learning, and multimodal systems through a wide range of impactful publications. His research encompasses visual data captioning, adaptive attention mechanisms, and transformer-based models that enhance image understanding and description generation. Notable works include Evolution of Visual Data Captioning Methods, Datasets, and Evaluation Metrics: A Comprehensive Survey, Automated Image Caption Generation Framework using Adaptive Attention and Bi-LSTM, and XGL-T Transformer Model for Intelligent Image Captioning, which collectively advance the field of vision-language integration. His studies such as Lightweight Transformer with GRU Integrated Decoder for Image Captioning and Control With Style: Style Embedding-based Variational Autoencoder for Controlled Stylized Caption Generation Framework propose innovative architectures for stylistic and efficient captioning. In addition, he has developed frameworks like FDT–Dr2T: A Unified Dense Radiology Report Generation Transformer Framework for X-ray Images and Unma-Capsumt: Unified and Multi-Head Attention-Driven Caption Summarization Transformer, highlighting his interest in medical AI and caption summarization. His earlier works, including Memory-Based FIR Digital Filter using Modified OMS-LUT Design and Modified Efficient OMS LUT-Design for Memory-Based Multiplication, show his foundational expertise in signal processing and hardware-efficient algorithms. Moreover, his contributions such as Obscenity Detection Transformer and DVRGNet reflect his commitment to developing socially responsible AI for content moderation. Overall, Dr. Sharma’s scholarly output demonstrates a consistent trajectory from traditional signal processing to cutting-edge multimodal AI, bridging research innovation with practical applications in intelligent computing and human-centered artificial intelligence.