Structured Knowledge Distillation for Dense Prediction in Computer Vision

Structured Knowledge Distillation for Dense Prediction in Computer Vision

This work explores the transfer of structural information from large networks to compact ones for dense prediction tasks in computer vision. Existing knowledge distillation strategies for dense prediction often directly adapt image classification distillation schemes, performing pixel-wise distillation, leading to suboptimal results.

We propose a novel structured knowledge distillation approach that accounts for the structured nature of dense prediction. Two schemes are investigated:

1. Pairwise distillation: This scheme distills pairwise similarities by constructing a static graph, capturing the relationships between pixels.
2. Holistic distillation: This scheme leverages adversarial training to distill holistic knowledge, capturing the overall structure of the prediction.

The effectiveness of our structured knowledge distillation methods is demonstrated through experiments on three dense prediction tasks: semantic segmentation, depth estimation, and object detection. Our code is publicly available at: https://git.io/StructKD.

Key Contributions:

• Introduces a novel structured knowledge distillation approach for dense prediction tasks.
• Proposes two schemes: pairwise distillation and holistic distillation.
• Demonstrates the effectiveness of our approach on various dense prediction tasks.
• Provides publicly available code for reproducibility.

Advantages:

• Improved performance over existing knowledge distillation methods for dense prediction.
• Enables the transfer of structural knowledge from large models to smaller ones.
• Offers flexibility and adaptability for different dense prediction tasks.

Further Research:

• Explore the application of our approach to other computer vision tasks.
• Investigate the use of different graph structures and adversarial training strategies.
• Analyze the impact of different knowledge distillation losses on the performance.

This work offers a significant advancement in knowledge distillation for dense prediction, paving the way for efficient and accurate model deployment in various computer vision applications.