Projects

Advanced implementation of neural style transfer using deep convolutional networks. Enables real-time artistic style application to images and video streams with optimized performance.

Fusion model combining text, image, and audio inputs for comprehensive sentiment analysis. Achieves state-of-the-art results on benchmark datasets through cross-modal attention mechanisms.

Robust object detection system designed for challenging environments including low-light, fog, and adverse weather. Utilizes attention mechanisms and adaptive preprocessing techniques.

Lightweight and efficient library for graph neural networks with support for various architectures including GCN, GAT, and GraphSAGE. Optimized for both research and production deployment.

High-performance human pose estimation system capable of processing 30+ FPS on edge devices. Implements optimized backbone networks and efficient keypoint detection algorithms.

Deep learning-based segmentation tool for medical imaging applications. Features U-Net architecture with attention gates for precise delineation of anatomical structures in CT and MRI scans.

Intelligent data augmentation framework that automatically learns optimal augmentation strategies for specific datasets. Improves model generalization with minimal manual tuning.

Privacy-preserving federated learning implementation supporting multiple aggregation strategies. Enables collaborative model training across distributed data sources without data sharing.

End-to-end system for answering natural language questions about images. Combines vision transformers with language models for sophisticated visual reasoning capabilities.