Adaptive Perspective Distillation for Semantic Segmentation.

Area of Science:

• Computer Vision
• Machine Learning
• Deep Learning

Background:

• Semantic segmentation models often require large backbones, hindering real-time applications.
• Knowledge distillation aims to transfer knowledge from large teacher models to smaller student models.
• Existing methods use a universal classifier perspective, potentially overlooking sample-specific details.

Purpose of the Study:

• To address the limitations of universal perspectives in knowledge distillation.
• To improve the effectiveness of knowledge distillation for semantic segmentation.

Main Methods:

• Propose Adaptive Perspective Distillation (APD), a novel approach for knowledge distillation.
• APD creates an adaptive local perspective for each individual training sample.
• Extracts detailed contextual information specific to each training sample.

Main Results:

• APD effectively mines more details from the teacher model.
• Achieves better knowledge distillation results for the student model.
• Demonstrates effectiveness on Cityscapes, ADE20K, and PASCAL-Context datasets.

Conclusions:

• APD generalizes well to different semantic segmentation models without structural constraints.
• The method yields performance gains in object detection and instance segmentation.
• APD offers an effective solution for improving knowledge distillation in computer vision tasks.