The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Channel-wise Knowledge Distillation for Dense Prediction
Open accessAbstract
Knowledge distillation (KD) has been proven a simple and effective tool for training compact dense prediction models. Lightweight student networks are trained by extra supervision transferred from large teacher networks. Most previous KD variants for dense prediction tasks align the activation maps from the student and teacher network in the spatial domain, typically by normalizing the activation values on each spatial location and minimizing point-wise and/or pair-wise discrepancy. Different from the previous methods, here we propose to normalize the activation map of each channel to obtain a soft probability map. By simply minimizing the Kullback–Leibler (KL) divergence between the channel-wise probability map of the two networks, the distillation process pays more attention to the most salient regions of each channel, which are valuable for dense prediction tasks.We conduct experiments on a few dense prediction tasks, including semantic segmentation and object detection. Experiments demonstrate that our proposed method outperforms state-of-the-art distillation methods considerably, and can require less computational cost during training. In particular, we improve the RetinaNet detector (ResNet50 backbone) by 3.4% in mAP on the COCO dataset, and PSPNet (ResNet18 backbone) by 5.81% in mIoU on the Cityscapes dataset. Code is available at: https://git.io/Distiller
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Deep Residual Learning for Image Recognition
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
The pascal visual object classes (VOC) challenge
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Distilling the Knowledge in a Neural Network
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The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433
Cited by
YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., et al. (2022) · arXiv · doi:10.48550/arxiv.2209.02976 · Cited by 1,792
“Besides, channelwise distillation [36] (later as CW Distill) is adapted within the YOLOv6 framework, shown in Fig.”
MethodHussain, M. (2023) · Machines · doi:10.3390/machines11070677 · Cited by 1,217
YOLO-based Object Detection Models: A Review and its Applications
Vijayakumar, A., & Vairavasundaram, S. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18872-y · Cited by 470
Masked Generative Distillation
Yang, Z., Li, Z., Shao, M., Shi, D., Yuan, Z., & Yuan, C. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20083-0_4 · Cited by 269
Knowledge Distillation with the Reused Teacher Classifier
Chen, D., Mei, J.-P., Zhang, H., Wang, C., Feng, Y., & Chen, C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01163 · Cited by 254
Metadata
- Authors
- Shu, C., Liu, Y., Gao, J., Yan, Z., & Shen, C.
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 5291-5300
- OpenAlex
- W3188026614
- Cited by
- 452
- References
- 48
- FWCI
- 17.76
- Type
- conference paper
- Language
- en
- Open access
- green
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