Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models (2304.07221v2)

Abstract: Pre-trained point cloud models have found extensive applications in 3D understanding tasks like object classification and part segmentation. However, the prevailing strategy of full fine-tuning in downstream tasks leads to large per-task storage overhead for model parameters, which limits the efficiency when applying large-scale pre-trained models. Inspired by the recent success of visual prompt tuning (VPT), this paper attempts to explore prompt tuning on pre-trained point cloud models, to pursue an elegant balance between performance and parameter efficiency. We find while instance-agnostic static prompting, e.g. VPT, shows some efficacy in downstream transfer, it is vulnerable to the distribution diversity caused by various types of noises in real-world point cloud data. To conquer this limitation, we propose a novel Instance-aware Dynamic Prompt Tuning (IDPT) strategy for pre-trained point cloud models. The essence of IDPT is to develop a dynamic prompt generation module to perceive semantic prior features of each point cloud instance and generate adaptive prompt tokens to enhance the model's robustness. Notably, extensive experiments demonstrate that IDPT outperforms full fine-tuning in most tasks with a mere 7% of the trainable parameters, providing a promising solution to parameter-efficient learning for pre-trained point cloud models. Code is available at \url{https://github.com/zyh16143998882/ICCV23-IDPT}.

Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models: A Detailed Analysis

The paper, "Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models," presents a novel approach to enhance the efficiency and robustness of pre-trained 3D point cloud models in downstream tasks such as object classification and segmentation. The work addresses the high storage demands associated with full fine-tuning by introducing a parameter-efficient alternative through prompt tuning.

Background and Motivation

The increasing complexity of 3D scanning technologies has led to significant advances in point cloud applications across various domains. Standard approaches to leveraging pre-trained models involve full fine-tuning, which poses several challenges, particularly in terms of storage and deployment across multiple tasks. The research draws inspiration from recent successes in visual prompt tuning (VPT) in the image processing domain. However, the direct application of VPT to point cloud models encounters limitations due to the diverse and noisy nature of real-world data distributions. This paper introduces the Instance-aware Dynamic Prompt Tuning (IDPT) as a solution to overcome these challenges.

Key Contributions

1. Dynamic Prompt Strategy: The paper proposes a dynamic prompt generation module that adapts to the semantic features of each point cloud instance. This approach is distinct from the static prompt methods like VPT, which are vulnerable to the distributional diversity of real-world data.
2. Efficiency and Robustness: IDPT achieves comparable, if not superior, performance to full fine-tuning while requiring only 7% of the trainable parameters. This result is significant as it suggests that dynamic prompting can efficiently adapt pre-trained models with minimal parameter tuning.
3. Empirical Validation: Through extensive experiments on datasets like ModelNet40 and ScanObjectNN, IDPT consistently outperformed traditional tuning methods, especially in scenarios with substantial data noise and variability.

Strong Numerical Results

The experimental results showcase IDPT's superiority in maintaining high classification accuracy across multiple datasets. For instance, IDPT applied to the Point-MAE model outperformed the full fine-tuning strategy by improving accuracy from 93.8% to 94.4% on ModelNet40. On the ScanObjectNN dataset, IDPT consistently showed better performance across various data configurations, demonstrating its robustness to data noise and missing points.

Theoretical and Practical Implications

The introduction of IDPT paves the way for more efficient adaptation of large-scale pre-trained models in resource-constrained environments. The dynamic aspect of the approach aligns well with the inherent variability in real-world point cloud data, ensuring that models remain robust and effective. Theoretically, IDPT underscores the importance of instance-aware adaptations in bridging domain gaps and mitigating distribution mismatches between pre-training and downstream tasks.

Speculation on Future Developments

Looking forward, the principles underlying IDPT could extend beyond 3D point cloud models to other domains where data distribution varies significantly. Furthermore, integrating dynamic prompt strategies with other forms of learned representations or embeddings may lead to even more flexible and adaptive AI models. Continued exploration in prompt tuning and its applications may unlock new paradigms in efficient model adaptation and deployment.

Overall, this paper contributes significantly to the understanding and advancement of parameter-efficient learning strategies for complex 3D data, highlighting both practical implementations and avenues for future research in AI model adaptation.