FDBPL: Faster Distillation-Based Prompt Learning for Region-Aware Vision-Language Models Adaptation (2505.18053v1)

Abstract: Prompt learning as a parameter-efficient method that has been widely adopted to adapt Vision-LLMs (VLMs) to downstream tasks. While hard-prompt design requires domain expertise and iterative optimization, soft-prompt methods rely heavily on task-specific hard labels, limiting their generalization to unseen categories. Recent popular distillation-based prompt learning methods improve generalization by exploiting larger teacher VLMs and unsupervised knowledge transfer, yet their repetitive teacher model online inference sacrifices the inherent training efficiency advantage of prompt learning. In this paper, we propose {{\large {\textbf{F}}}}aster {{\large {\textbf{D}}}}istillation-{{\large {\textbf{B}}}}ased {{\large {\textbf{P}}}}rompt {{\large {\textbf{L}}}}earning (\textbf{FDBPL}), which addresses these issues by sharing soft supervision contexts across multiple training stages and implementing accelerated I/O. Furthermore, FDBPL introduces a region-aware prompt learning paradigm with dual positive-negative prompt spaces to fully exploit randomly cropped regions that containing multi-level information. We propose a positive-negative space mutual learning mechanism based on similarity-difference learning, enabling student CLIP models to recognize correct semantics while learning to reject weakly related concepts, thereby improving zero-shot performance. Unlike existing distillation-based prompt learning methods that sacrifice parameter efficiency for generalization, FDBPL maintains dual advantages of parameter efficiency and strong downstream generalization. Comprehensive evaluations across 11 datasets demonstrate superior performance in base-to-new generalization, cross-dataset transfer, and robustness tests, achieving $2.2\times$ faster training speed.

Analyzing the FDBPL Framework for Vision-LLM Adaptation

The paper, "FDBPL: Faster Distillation-Based Prompt Learning for Region-Aware Vision-LLMs Adaptation," explores the challenges associated with adapting large-scale Vision-LLMs (VLMs) like CLIP to downstream tasks through prompt learning strategies. Prompt learning, notably parameter-efficient, is primarily categorized into hard and soft prompts. Hard prompts require domain expertise for design, while soft prompts leverage task-specific labels, sometimes leading to limited generalization to unseen classes. The paper proposes Faster Distillation-Based Prompt Learning (FDBPL) for enhancing the efficiency and effectiveness of distillation-based prompt learning methods.

Core Contributions

The FDBPL framework offers two primary advancements:

1. Efficiency in Distillation-Based Prompt Learning: Traditional distillation-based methods often compromise training efficiency due to repeated inference operations of teacher networks during training. The FDBPL method addresses this by precomputing supervision signals, thus eliminating the need for redundant teacher network inference over training epochs. Leveraging fast I/O operations and avoiding the computational overhead of real-time inference restores efficiency advantages crucial in prompt learning methods.
2. Region-Aware Prompt Learning: The framework introduces a region-aware strategy employing dual positive-negative prompt spaces. Positive prompts focus on regions rich in semantic content, whereas negative prompts aid in recognizing and rejecting semantically ambiguous or empty regions, thus enhancing the model's zero-shot performance capabilities.

Strong Numerical Results

FDBPL showcases notable performance improvements across multiple datasets, demonstrating superior generalization and rapid training capabilities:

• Achieves $2.2\times$ faster training speeds compared to conventional distillation-based methods.
• Outperforms competing methods in evaluations conducted on 11 datasets under zero-shot generalization settings.

Implications and Future Developments

The dual-prompt system of FDBPL presents opportunities for broader exploration in efficient model adaptation across varied AI domains. Given its capability to optimize model adaptation while maintaining high efficiency, this approach can be extended to models engaging in tasks requiring nuanced understanding of complex scenes. Further research might focus on exploring its applicability within real-time systems where rapid inference and adaptation are paramount.

As AI models continue to expand in complexity and scope, frameworks like FDBPL highlight the potential of marrying efficient model parameters adjustments with robust task-specific adaptations. Future developments could refine the sharing mechanisms within fast I/O operations and explore integration with larger ensembles of VLM teachers to further enhance shared information precision.

Conclusion

FDBPL signifies an important step in enhancing the flexibility and efficiency of prompt learning in region-aware vision-LLMs adaptation. Its novel approach to distillation and prompt interaction paradigms paves the way for improved model training processes, crucial for evolving AI applications across diverse disciplines.