ACAM-KD: Adaptive and Cooperative Attention Masking for Knowledge Distillation

• URL: http://arxiv.org/abs/2503.06307v1
• Date: Sat, 08 Mar 2025 18:51:53 GMT
• Title: ACAM-KD: Adaptive and Cooperative Attention Masking for Knowledge Distillation
• Authors: Qizhen Lan, Qing Tian,
• Abstract summary: ACAM-KD adapts to the student's evolving needs throughout the entire distillation process.<n>It improves object detection performance by up to 1.4 mAP over the state-of-the-art.<n>For semantic segmentation on Cityscapes, it boosts mIoU by 3.09 over the baseline.
• Score: 2.7624021966289605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dense visual prediction tasks, such as detection and segmentation, are crucial for time-critical applications (e.g., autonomous driving and video surveillance). While deep models achieve strong performance, their efficiency remains a challenge. Knowledge distillation (KD) is an effective model compression technique, but existing feature-based KD methods rely on static, teacher-driven feature selection, failing to adapt to the student's evolving learning state or leverage dynamic student-teacher interactions. To address these limitations, we propose Adaptive student-teacher Cooperative Attention Masking for Knowledge Distillation (ACAM-KD), which introduces two key components: (1) Student-Teacher Cross-Attention Feature Fusion (STCA-FF), which adaptively integrates features from both models for a more interactive distillation process, and (2) Adaptive Spatial-Channel Masking (ASCM), which dynamically generates importance masks to enhance both spatial and channel-wise feature selection. Unlike conventional KD methods, ACAM-KD adapts to the student's evolving needs throughout the entire distillation process. Extensive experiments on multiple benchmarks validate its effectiveness. For instance, on COCO2017, ACAM-KD improves object detection performance by up to 1.4 mAP over the state-of-the-art when distilling a ResNet-50 student from a ResNet-101 teacher. For semantic segmentation on Cityscapes, it boosts mIoU by 3.09 over the baseline with DeepLabV3-MobileNetV2 as the student model.

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