Abstract
Deep learning methods have advanced the accuracy and speed of object detection. However, acquiring labeled data is especially challenging in real-world scenarios. As a result, the general-purpose object detection algorithms based on deep learning experience significant performance degradation with limited samples. To tackle the issue of declining detection accuracy in the presence of few-shot data, this paper introduces a few-shot object detection algorithm based on adaptive relation distillation, which improves upon existing algorithms by enhancing the fusion of query and support features. In the proposed method, the adaptive relational distillation module discards the hand-designed and inefficient query information utilization strategy employed in previous algorithms and adaptively fuses the features of support and query images using convolutional networks. To augment the learning capability of the adaptive relational distillation module, we utilize a hybrid attention module in the support branch to emphasize the regions crucial for detecting specific classes of objects. The experimental results demonstrate our proposed algorithm achieves an average accuracy of 47.6% on three data divisions and five sample size settings for the PASCAL VOC dataset, which marks an improvement of 8.3% over DCNet.
This work is supported by National Key R&D Program of China under Grant No. 2022YFF0902401, the National Natural Science Foundation of China under Grant 62271455 and the Fundamental Research Funds for the Central Universities under Grant Nos. CUC210C013 and CUC18LG024.
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References
Diwan, T., Anirudh, G., Tembhurne, J.V.: Object detection using yolo: challenges, architectural successors, datasets and applications. Multimed. Tools Appl. 82(6), 9243ā9275 (2023)
Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: Yolov7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464ā7475 (2023)
Zeng, N., Wu, P., Wang, Z., Li, H., Liu, W., Liu, X.: A small-sized object detection oriented multi-scale feature fusion approach with application to defect detection. IEEE Trans. Instrum. Meas. 71, 1ā14 (2022)
Vs, V., Poster, D., You, S., Hu, S., Patel, V.M.: Meta-uda: Unsupervised domain adaptive thermal object detection using meta-learning. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1412ā1423 (2022)
Su, H., Xiang, L., Hu, A., Xu, Y., Yang, X.: A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions. Mech. Syst. Signal Process. 169, 108765 (2022)
Huang, S.F., Lin, C.J., Liu, D.R., Chen, Y.C., Lee, H.y.: Meta-tts: meta-learning for few-shot speaker adaptive text-to-speech. IEEE/ACM Trans. Audio Speech Lang. Process. 30, 1558ā1571 (2022)
Kang, B., Liu, Z., Wang, X., Yu, F., Feng, J., Darrell, T.: Few-shot object detection via feature reweighting. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 8420ā8429 (2019)
Yan, X., Chen, Z., Xu, A., Wang, X., Liang, X., Lin, L.: Meta r-cnn: towards general solver for instance-level low-shot learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9577ā9586 (2019)
Fan, Q., Zhuo, W., Tang, C.K., Tai, Y.W.: 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. 4013ā4022 (2020)
Hu, H., Bai, S., Li, A., Cui, J., Wang, L.: Dense relation distillation with context-aware aggregation for few-shot object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10185ā10194 (2021)
Schmidhuber, J.: Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-... hook. Ph.D. thesis, Technische UniversitƤt MĆ¼nchen (1987)
Hinton, G.E., Plaut, D.C.: Using fast weights to deblur old memories. In: Proceedings of the 9th Annual Conference of the Cognitive Science Society, pp. 177ā186 (1987)
Fu, K., Zhang, T., Zhang, Y., Yan, M., Chang, Z., Zhang, Z., Sun, X.: Meta-ssd: towards fast adaptation for few-shot object detection with meta-learning. IEEE Access 7, 77597ā77606 (2019)
Wang, Y.X., Ramanan, D., Hebert, M.: Meta-learning to detect rare objects. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9925ā9934 (2019)
Karlinsky, L., et al.: Repmet: representative-based metric learning for classification and few-shot object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5197ā5206 (2019)
Yang, Y., Wei, F., Shi, M., Li, G.: Restoring negative information in few-shot object detection. Adv. Neural. Inf. Process. Syst. 33, 3521ā3532 (2020)
Li, B., Yang, B., Liu, C., Liu, F., Ji, R., Ye, Q.: Beyond max-margin: Class margin equilibrium for few-shot object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7363ā7372 (2021)
Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3ā19 (2018)
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vision 88(2), 303ā338 (2010)
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Duan, D., Zhong, W., Peng, L., Ran, S., Hu, F. (2024). Few-Shot Object Detection Algorithm Based onĀ Adaptive Relation Distillation. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14436. Springer, Singapore. https://doi.org/10.1007/978-981-99-8555-5_26
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