domain-adaptation

HERS presents a domain-adaptive diffusion framework for controllable, realistic, and trustworthy vehicle damage synthesis. The method decomposes complex damage generation into a set of risk-specific expert modules, each specializing in a particular damage type such as dents, scratches, broken lights, or cracked paint, and trained using self-supervised image–text pairs without manual annotation. These experts are later integrated into a unified diffusion model that balances specialization with generalization, enabling precise control over damage attributes while maintaining visual coherence. Extensive experiments across multiple diffusion backbones demonstrate consistent improvements in text–image alignment and human preference over standard fine-tuning baselines. Beyond visual fidelity, HERS highlights broader implications for auditability, fraud prevention, and the responsible deployment of generative models in high-stakes domains, underscoring the need for trustworthy and risk-aware diffusion systems in applications such as automated insurance assessment.

My PhD thesis focuses on improving semantic segmentation of aerial and satellite images, a crucial task for applications like agriculture planning, map updates, route optimization, and navigation. Current models like the Deep Convolutional Encoder-Decoder (DCED) have limitations in accuracy due to their inability to recover low-level features and the scarcity of training data. To address these issues, I propose a new architecture with five key enhancements, a Global Convolutional Network (GCN) for improved feature extraction, channel attention for selecting discriminative features, domain-specific transfer learning to address data scarcity, Feature Fusion (FF) for capturing low-level details, and Depthwise Atrous Convolution (DA) for refining features. Experiments on Landsat-8 datasets and the ISPRS Vaihingen benchmark showed that my proposed architecture significantly outperforms the baseline models in remote sensing imagery.