Learning More from Less: Resource-Constrained Generative AI for Classification, Generation, and Personalization

Abstract

The rapid advancement of generative models has created new opportunities for addressing core challenges in computer vision, including data scarcity, image quality, and efficient personalization. My research develops principled, resource- aware methods that enable models to generalize effectively from limited supervision, adapt efficiently to new concepts, and generate high-fidelity visual content. I first address few-shot learning through augmentation-driven uncertainty- guided mixup, improving robustness in data-constrained regimes. Building on this, I propose caption-guided multi-modal augmentation techniques that enrich visual diversity while mitigating real-to-synthetic domain gaps. To enhance the quality and realism of generated images, I introduce diffusion models grounded in natural image statistics, yielding perceptually aligned outputs suitable for downstream tasks. To advance personalization, I develop parameter-efficient mechanisms for combining low-rank adapters, enabling fine-grained control over content and style without retraining. I further extend personalization to a zero-shot setting through a training-free textual-inversion-based method that customizes arbitrary objects directly within the diffusion process. Finally, I present a frequency-guided multi-LoRA fusion framework that leverages wavelet-domain cues and timestep-aware weighting for accurate, training-free concept composition. Collectively, these contributions move toward a unified vision of generative models that are efficient, adaptive, and capable of high-quality, customizable image synthesis.