ITFD: an instance-level triplet few-shot detection network under weighted pair-resampling
Tóm tắt
Few-shot object detection has been widely applied in industrial applications, endangered detection, tumor lesion detection, etc. Although many excellent few-shot detection models have been proposed recently, intra-class & inter-class confusion and low activation of novel classes still keep few-shot detection challenging. In this paper, we propose a novel few-shot detection model ITFD, in which a weighted pair-resampling method improves the localization efficiency of the novel-class and a hard triplet loss reduces intra-class & inter-class confusion are contained. Extensive experiments have shown that our model achieves 3.6
$$\%$$
, 2.6
$$\%$$
, and 3.7
$$\%$$
average nAP50 improvement on novel-class setup 1,2,3 of PASCAL VOC compared to the same one-time fine-tuning type of models. Besides, to verify the effectiveness of our model in practical application, we established two train component detection datasets. Our model achieves state-of-the-art performance on both datasets with an average nAP50 improvement of 7
$$\%$$
and 4.8
$$\%$$
respectively.
Tài liệu tham khảo
Antonelli S, Avola D, Cinque L, Crisostomi D, Foresti GL, Galasso F, Marini MR, Mecca A, Pannone D (2022) Few-shot object detection: A survey. ACM Comput Surv 54(11s):1–37
Meng Y, Xu H, Ma Z, Zhou J, Hui D (2022) Detail-semantic guide network based on spatial attention for surface defect detection with fewer samples. Appl Intel
Wang M, Ning H, Liu H Object detection based on few-shot learning via instance-level feature correlation and aggregation. Appl Intel 1–18
Zhang C, Bengio S, Hardt M, Recht B, Vinyals O (2021) Understanding deep learning (still) requires rethinking generalization. Commun ACM 64(3):107–115
Wang Y, Yao Q, Kwok JT, Ni LM (2020) Generalizing from a few examples: a survey on few-shot learning. ACM Comput Surv 53(3):1–34
Zhang G, Luo Z, Cui K, Lu S, Xing EP (2022) Meta-detr: image-level few-shot detection with inter-class correlation exploitation. IEEE Trans Pattern Anal Mach Intel
Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Info Process Syst 28
Fang W, Wang L, Ren P (2019) Tinier-yolo: a real-time object detection method for constrained environments. IEEE Access 8:1935–1944
Huang Z, Wang J, Fu X, Yu T, Guo Y, Wang R (2020) Dc-spp-yolo: dense connection and spatial pyramid pooling based yolo for object detection. Info Sci 522:241–258
Wen G, Cao P, Wang H, Chen H, Liu X, Xu J, Zaiane O (2022) Ms-ssd: multi-scale single shot detector for ship detection in remote sensing images. Appl Intel 1–19
Liu Y, Ma Z, Liu X, Ma S, Ren K (2019) Privacy-preserving object detection for medical images with faster r-cnn. IEEE Trans Info Forensics Secur
Fang F, Li L, Zhu H, Lim J-H (2019) Combining faster r-cnn and model-driven clustering for elongated object detection. IEEE Trans Image Process 29:2052–2065
Gong H, Mu T, Li Q, Dai H, Li C, He Z, Wang W, Han F, Tuniyazi A, Li H et al (2022) Swin-transformer-enabled yolov5 with attention mechanism for small object detection on satellite images. Remote Sens 14(12):28–61
Dai Z, Cai B, Lin Y, Chen J (2022) Unsupervised pre-training for detection transformers. IEEE Trans Pattern Anal Mach Intell
Duan R, Li D, Tong Q, Yang T, Liu X, Liu X (2021) A survey of few-shot learning: an effective method for intrusion detection. Secur Commun Netw 2021
López-Martín M, Carro B, Sánchez-Esguevillas A (2019) Variational data generative model for intrusion detection. Knowl Inf Syst
Ye H-J, Sheng X-R, Zhan D-C (2020) Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach. Mach Learn 109(3):643–664
Li X, Sun Z, Xue J-H, Ma Z (2021) A concise review of recent few-shot meta-learning methods. Neurocomputing 456:463–468
Thung K-H, Wee C-Y (2018) A brief review on multi-task learning. Multimed Tools Appl 77(22):29705–29725
Altae-Tran H, Ramsundar B, Pappu AS, Pande V (2017) Low data drug discovery with one-shot learning. ACS Central Sci 3(4):283–293
Chen H, Wang Y, Wang G, Qiao Y (2018) Lstd: a low-shot transfer detector for object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 32
Xiao Y, Marlet R (2020) Few-shot object detection and viewpoint estimation for objects in the wild. In: European Conference on Computer Vision, pp 192–210 . Springer
Sun B, Li B, Cai S, Yuan Y, Zhang C (2021) Fsce: Few-shot object detection via contrastive proposal encoding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 7352–7362
Wang X, Huang T, Gonzalez J, Darrell T, Yu F (2020) Frustratingly simple few-shot object detection. In: International Conference on Machine Learning, pp 9919–9928. PMLR
Yang Z, Wang Y, Chen X, Liu J, Qiao Y (2020) Context-transformer: tackling object confusion for few-shot detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 12653–12660
Yan X, Chen Z, Xu A, Wang X, Liang X, Lin L (2019) Meta r-cnn: towards general solver for instance-level low-shot learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 9577–9586
Fan Q, Zhuo W, Tang C-K, Tai Y-W (2020) Few-shot object detection with attention-rpn and multi-relation detector. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4013–4022
Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vision 88(2):303–338
Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: common objects in context. In: European Conference on Computer Vision, pp 740–755. Springer
Wang Y-X, Ramanan D, Hebert M (2019) Meta-learning to detect rare objects. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 9925–9934
Zhang W, Wang Y-X (2021) Hallucination improves few-shot object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 13008–13017
Xia R, Li G, Huang Z, Meng H, Pang Y (2023) Bi-path combination yolo for real-time few-shot object detection. Pattern Recogn Lett 165:91–97
Vu A-KN, Nguyen N-D, Nguyen K-D, Nguyen V-T, Ngo TD, Do T-T, Nguyen TV (2022) Few-shot object detection via baby learning. Image Vision Comput 120:104–398
Kang B, Liu Z, Wang X, Yu F, Feng J, Darrell T (2019) Few-shot object detection via feature reweighting. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 8420–8429
Chen T-I, Liu Y-C, Su H-T, Chang Y-C, Lin Y-H, Yeh J-F, Chen W-C, Hsu W (2021) Dual-awareness attention for few-shot object detection. IEEE Trans Multimedia
Hayashi T, Fujita H, Hernandez-Matamoros A (2021) Less complexity one-class classification approach using construction error of convolutional image transformation network. Inf Sci 560:217–234
Hayashi T, Cimr D, Studnička F, Fujita H, Bušovskỳ D, Cimler R (2022) Ocstn: one-class time-series classification approach using a signal transformation network into a goal signal. Inf Sci 614:71–86
Ouyang Y, Wang X-Q, Hu R-Z, Xu H-H (2022) Few-shot object detection based on positive-sample improvement. Def Technol