Few-Shot Object Detection with Weight Imprinting

Wang X, Huang TE, Darrell T, Gonzalez JE, Yu F. Frustratingly simple few-shot object detection. In: Proceedings of the 37th International Conference on Machine Learning. 2020 Jul 13. p. 9919–28.

Han G, Huang S, Ma J, He Y, Chang SF. Meta faster r-cnn: towards accurate few-shot object detection with attentive feature alignment. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2022 Jun 28. Vol. 36, No. 1, p. 780–9.

Hsieh TI, Lo YC, Chen HT, Liu TL. One-shot object detection with co-attention and co-excitation. Adv Neural Inf Process Syst. 2019;32.

Chen X, Jiang M, Zhao Q. Leveraging bottom-up and top-down attention for few-shot object detection. arXiv preprint; 2020 Jul 23. arXiv:2007.12104.

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. 2019. p. 9577–86.

Yang S, Liu L, Xu M. Free lunch for few-shot learning: distribution calibration. arXiv preprint; 2021 Jan 16. arXiv:2101.06395.

Qiao L, Zhao Y, Li Z, Qiu X, Wu J, Zhang C. Defrcn: decoupled faster r-cnn for few-shot object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021. p. 8681–90.

Chen WY, Liu YC, Kira Z, Wang YC, Huang JB. A closer look at few-shot classification. In: International Conference on Learning Representations. 2019 May.

Wang Y, Yao Q, Kwok JT, Ni LM. Generalizing from a few examples: a survey on few-shot learning. ACM Comput Surv (csur). 2020 Jun 12;53(3):1–34.

Wang YX, Ramanan D, Hebert M. Meta-learning to detect rare objects. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019. pp. 9925–34.

Fan Z, Ma Y, Li Z, Sun J. Generalized few-shot object detection without forgetting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. pp. 4527–36.

Qi H, Brown M, Lowe DG. Low-shot learning with imprinted weights. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. pp. 5822–30.

Antonelli S, Avola D, Cinque L, Crisostomi D, Foresti GL, Galasso F, Marini MR, Mecca A, Pannone D. Few-shot object detection: a survey. ACM Comput Surv (CSUR). 2022;54(11s):1–37.

Wang YX, Hebert M. Learning to learn: model regression networks for easy small sample learning. In: European Conference on Computer Vision. Cham: Springer; 2016 Oct 8. pp. 616–34.

Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning. PMLR; 2017 Jul 17. p. 1126–35.

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. 2019. p. 8420–9.

Xiao Y, Marlet R. Few-shot object detection and viewpoint estimation for objects in the wild. In: European conference on computer vision. Cham: Springer; 2020 Aug 23. p. 192–210.

Liu L, Ma B, Zhang Y, Yi X, Li H. Afd-net: Adaptive fully-dual network for few-shot object detection. In: Proceedings of the 29th ACM International Conference on Multimedia. 2021 Oct 17. p. 2549–57.

Fan Q, Zhuo W, Tang CK, Tai YW. Few-shot object detection with attention-RPN and multi-relation detector. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020. p. 4013–22.

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. 2021. p. 7363–72.

Zhang G, Luo Z, Cui K, Lu S. Meta-detr: few-shot object detection via unified image-level meta-learning. arXiv preprint. 2021 Mar 22;2(6). arXiv:2103.11731.

Lin W, Deng Y, Gao Y, Wang N, Zhou J, Liu L, Zhang L, Wang P. CAT: cross-attention transformer for one-shot object detection. arXiv preprint; 2021 Apr 30. arXiv:2104.14984.

Chen H, Wang Y, Wang G, Qiao Y. Lstd: a low-shot transfer detector for object detection. In: Proceedings of the AAAI conference on artificial intelligence. 2018 Apr 29. Vol. 32, No. 1.

Article  Google Scholar 

Zhang W, Wang YX, Forsyth DA. Cooperating RPN’s improve few-shot object detection. arXiv preprint; 2020 Nov 19. arXiv:2011.10142.

Wu J, Liu S, Huang D, Wang Y. Multi-scale positive sample refinement for few-shot object detection. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVI 16. Springer International Publishing; 2020. pp. 456–72.

Sun B, Li B, Cai S, Yuan Y, Zhang C. Fsce: Few-shot object detection via contrastive proposal encoding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. pp. 7352–62.

Zhang W, Wang YX. Hallucination improves few-shot object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. pp. 13008–17.

Wu A, Han Y, Zhu L, Yang Y. Universal-prototype enhancing for few-shot object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021. p. 9567–76.

Khandelwal S, Goyal R, Sigal L. Unit: Unified knowledge transfer for any-shot object detection and segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. pp. 5951–61.

Gidaris S, Komodakis N. Dynamic few-shot visual learning without forgetting. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. pp. 4367–75.

Dhillon GS, Chaudhari P, Ravichandran A, Soatto S. A baseline for few-shot image classification. arXiv preprint; 2019 Sep 6. arXiv:1909.02729.

Jung HG, Lee SW. Few-shot learning with geometric constraints. IEEE Trans Neural Netw Learn Syst. 2020 Jan 1;31(11):4660–72.

Ren S, He K, Girshick R, Sun J. Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst. 2015;28.

Yosinski J, Clune J, Bengio Y, Lipson H. How transferable are features in deep neural networks? Adv Neural Inf Process Syst. 2014;27.

Guirguis K, Hendawy A, Eskandar G, Abdelsamad M, Kayser M, Beyerer J. CFA: constraint-based finetuning approach for generalized few-shot object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022. p. 4039–49.

留言 (0)

沒有登入
gif