Deep Learning Data Annotation for CNN Architectures
This project involved the high-granularity annotation of visual and linguistic datasets to support the development of domain-specific Convolutional Neural Network (CNN) architectures and cross-modal classification models. The scope encompassed the labeling of thousands of images for object detection and the curation of sentiment-based text datasets for conversion into image-matrix representations, as detailed in my EMNLP and CVPR publications. My specific tasks included defining bounding box parameters for real-time IOT object recognition and establishing a systematic mapping between natural language sentiment and visual features. To ensure rigorous quality, I adhered to iterative cross-validation measures and utilized multi-stage verification to maintain high inter-annotator agreement and training data integrity. This foundational work contributed to the successful filing of two U.S. patents and the development of ultra-power-efficient accelerators achieving 9.3 TOPS/Watt.