Text-Guided Multi-Scale Frequency Representation Adaptation

Researchers introduce FreqAdapter, a parameter-efficient fine-tuning method that operates in the frequency domain rather than signal space to adapt pre-trained models like CLIP and LLaVA. The approach uses multi-scale adaptation strategies and text-guided prompts to improve model efficiency and performance with minimal training parameters and fast convergence.

FreqAdapter represents an incremental advancement in the parameter-efficient fine-tuning space, addressing specific limitations in how existing methods adapt pre-trained models. Rather than working in traditional signal space where redundancy is high, the approach leverages frequency domain representations to reduce computational overhead while maintaining model adaptability. This methodology aligns with broader trends in machine learning efficiency research, where researchers seek to maximize performance gains from pre-trained models without proportional increases in computational cost or training data requirements.

The technical contribution centers on multi-scale frequency analysis, enabling the adapter to optimize different receptive fields across varying frequency ranges. This granular approach to adaptation represents a conceptual shift from fixed-prompt methods that treat all signal characteristics uniformly. By incorporating textual guidance alongside frequency-based adaptation, FreqAdapter creates a hybrid mechanism that leverages both semantic and structural information during fine-tuning.

For AI practitioners and researchers, this work offers practical value through demonstrated improvements on established multimodal models. The claim of convergence within a single epoch suggests substantially reduced training overhead compared to conventional fine-tuning approaches. However, the impact remains primarily academic and technical rather than commercially transformative at this stage. Practitioners working with CLIP or LLaVA models may find implementation benefits, particularly in resource-constrained environments.

Future developments will likely focus on extending frequency-domain adaptation to larger models and exploring whether this approach generalizes across different model architectures and domains. The released code enables broader community validation of the method's effectiveness.

• →FreqAdapter uses frequency domain operations instead of signal space to reduce information redundancy in model adaptation.
• →Multi-scale adaptation strategy optimizes receptive fields across different frequency ranges for enhanced representational capacity.
• →Method achieves convergence within one epoch on CLIP and LLaVA models with minimal training parameters.
• →Text-guided adaptation integrates semantic information with frequency-domain processing for improved fine-tuning.
• →Open-source code availability enables reproduction and broader adoption within the research community.