Journal of Shandong University(Engineering Science) ›› 2022, Vol. 52 ›› Issue (6): 115-122.doi: 10.6040/j.issn.1672-3961.0.2022.087
• 机器学习与数据挖掘 • Previous Articles
LIU Dingbo1,2, LIU Xueyan2,3, YU Dongran2,4, YANG Bo2,3, LI Wei5*
CLC Number:
[1] 谢富, 朱定局.深度学习目标检测方法综述[J].计算机系统应用, 2022, 31(2): 1-12. XIE Fu, ZHU Dingju. Survey on deep learning object detection[J]. Computer Systems & Applications, 2022, 31(2):1-12. [2] HU H, BAI S, LI A, et al. Dense relation distillation with context-aware aggregation for few-shot object detection[C] //Proceedings of CVPR-21. Nashville, USA: IEEE, 2021: 10185-10194. [3] TAN M, PANG R, LE Q V. Efficientdet: scalable and efficient object detection[C] //Proceedings of CVPR-20. Seattle, USA: IEEE, 2020: 10781-10790. [4] ZHANG Y, KANG B, HOOI B, et al. Deep long-tailed learning:a survey[EB/OL].(2021-10-09)[2022-02-25]. https://arxiv.org/pdf/2110.04596.pdf. [5] KÖHLER M, EISENBACH M, GROSS H M. Few-shot object detection: a survey[EB/OL].(2021-12-22)[2022-02-25].https://arxiv.org/pdf/2112.11699.pdf. [6] BUDA M, MAKI A, MAZUROWSKI M A. A systematic study of the class imbalance problem in convolutional neural networks[J]. Neural Networks, 2018, 106: 249-259. [7] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 42(2):318-327. [8] KANG B, LIU Z, WANG X, et al. Few-shot object detection via feature reweighting[C] //Proceedings of ICCV-19. Seoul, Korea: IEEE, 2019: 8420-8429. [9] WANG Y X, RAMANAN D, HEBERT M. Meta-learning to detect rare objects[C] //Proceedings of ICCV-19. Seoul, Korea: IEEE, 2019: 9925-9934. [10] YAN X, CHEN Z, XU A, et al. Meta r-cnn: towards general solver for instance-level low-shot learning[C] // Proceedings of ICCV-19. Seoul, Korea: IEEE, 2019: 9577-9586. [11] WANG X, HUANG T E, DARRELL T, et al. Frustratingly simple few-shot object detection[C] // Proceedings of ICML-20. Online: ACM, 2022: 9919-9928. [12] WU J, LIU S, HUANG D, et al. Multi-scale positive sample refinement for few-shot object detection[C] // Proceedings of ECCV-20. Online: Springer, 2020: 456-472. [13] SUN B, LI B, CAI S, et al. FSCE: few-shot object detection via contrastive proposal encoding[C] // Proceedings of CVPR-21. Nashville, USA: IEEE, 2021: 7352-7362. [14] TANG K, HUANG J, ZHANG H. Long-tailed classification by keeping the good and removing the bad momentum causal effect[J]. Advances in Neural Information Processing Systems, 2020, 33: 1513-1524. [15] XU H, JIANG C, LIANG X, et al. Reasoning-rcnn: Unifying adaptive global reasoning into large-scale object detection[C] //Proceedings of CVPR-19. Long Beach, USA: IEEE, 2019: 6419-6428. [16] REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. [17] EVERINGHAM M, ZISSERMAN A, WILLIAMS C K I, et al. The PASCAL visual object classes(VOC)challenge[J]. International Journal of Computer Vision, 2010, 88(2):303-338. [18] LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: common objects in context[C] //Proceedings of ECCV-14. Zurich, Switzerland: Springer, 2014: 740-755. [19] CHEN K, WANG J, PANG J, et al. MMDetection: open mmlab detection toolbox and benchmark[EB/OL].(2019-06-17)[2022-02-25]. https://arxiv.org/pdf/1906.07155.pdf. [20] HEK, ZHANG X, REN S, et al. Deep residual learning for image recognition[C] //Proceedings of CVPR-16. Las Vegas, USA: IEEE, 2016:770-778. [21] LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C] //Proceedings of CVPR-17. Honolulu, USA: IEEE, 2017:2117-2125. |
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