☆ 4.7 Article

Research on knowledge distillation algorithm based on Yolov5 attention mechanism

EXPERT SYSTEMS WITH APPLICATIONS (2024)

Focal and Global Knowledge Distillation for Detectors

Zhendong Yang et al.

Summary: This paper proposes a Focal and Global Distillation (FGD) method for knowledge distillation in object detection. By separating foreground and background and rebuilding the relationship between pixels, the proposed method improves the effectiveness of knowledge distillation. Experimental results show that using FGD can significantly improve mAP on different detectors.

2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022) (2022)

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Proceedings Paper Computer Science, Artificial Intelligence

Deep Structured Instance Graph for Distilling Object Detectors

Yixin Chen et al.

Summary: This study introduces a knowledge distillation method by constructing a simple knowledge structure and graph model to assist knowledge transfer between teachers and students, achieving state-of-the-art results in object detection tasks.

2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021) (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

Manifold Regularized Dynamic Network Pruning

Yehui Tang et al.

Summary: The proposed approach in this paper dynamically removes redundant filters by embedding the manifold information of all instances into the space of pruned networks. The effectiveness of the method is verified on several benchmarks, showing better performance in terms of both accuracy and computational cost compared to state-of-the-art methods. This new paradigm maximally excavates redundancy in the network architecture.

2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 (2021)

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Article Computer Science, Artificial Intelligence