Localization Distillation for Dense Object Detection
Open accessAbstract
Knowledge distillation (KD) has witnessed its powerful capability in learning compact models in object detection. Previous KD methods for object detection mostly focus on imitating deep features within the imitation regions instead of mimicking classification logit due to its inefficiency in distilling localization information and trivial improvement. In this paper, by reformulating the knowledge distillation process on localization, we present a novel localization distillation (LD) method which can efficiently transfer the localization knowledge from the teacher to the student. Moreover, we also heuristically introduce the concept of valuable localization region that can aid to selectively distill the semantic and localization knowledge for a certain region. Combining these two new components, for the first time, we show that logit mimicking can outperform feature imitation and localization knowledge distillation is more important and efficient than semantic knowledge for distilling object detectors. Our distillation scheme is simple as well as effective and can be easily applied to different dense object detectors. Experiments show that our LD can boost the AP score of GFocal-ResNet-50 with a single-scale 1 x training schedule from 40.1 to 42.1 on the COCO benchmark without any sacrifice on the inference speed. Our source code and pretrained models are publicly available at https://github.com/HikariTJU/LD.
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Benefitting from GFLV1, each edge of a bounding box can be represented as a probability distribution through SoftMax function.
“Benefitting from the general representation of distributions for bounding boxes from GFLV1 detector, each edge e of a bounding box can be represented as a probability distribution through SoftMax function [38].”
From Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation (2022) · cites this paper as [38]
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“Previous works [11, 13, 26, 46, 51] Algorithm 1 Valuable Localization Region”
Improved Knowledge Distillation via Teacher Assistant
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“Knowledge distillation [1, 19, 34, 36, 45, 59] aims to learn compact and efficient student models guided by excellent teacher networks.”
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Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation
Feng, T., Wang, M., & Yuan, H. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00921 · Cited by 132 · see citing works
- Authors
- Zheng, Z., Ye, R., Wang, P., Ren, D., Zuo, W., Hou, Q., & Cheng, M.-M.
- Affiliations
- Nankai University (CN)
- Tianjin University (CN)
- Harbin Institute of Technology (CN)
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 9397-9406
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4313119505
- Cited by
- 220
- References
- 13
- Citations by year
- 2026: 44 · 2025: 60 · 2024: 59 · 2023: 49 · 2022: 8
- FWCI
- 15.96
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Key Research and Development Program of China (2018AAA0100400)
- NSFC (61922046,62172127)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- knowledge distillation, dense object detection, COCO benchmark
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