BBox Adapter

BBox-Adapter, and related adapter methods, represent a family of lightweight modules designed to efficiently adapt large pre-trained models (like LLMs and vision transformers) to new tasks without retraining the entire model. Current research focuses on developing efficient adapter architectures for various modalities (text, image, video, audio) and tasks (e.g., sound event detection, text-to-image generation, speech recognition), often employing techniques like knowledge distillation and ranking-based losses to improve performance and reduce computational costs. This approach is significant because it allows for cost-effective customization of powerful, often black-box, models for specific applications, improving accessibility and reducing the environmental impact of large-scale model training.