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Few-shot object detection (FSOD) helps detectors adapt to unseen classes with few training instances, and is useful when manual annotation is time-consuming or data acquisition is limited.
Li, F., Fergus, R., Perona, P.: One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2006)
2006
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Everingham, M., Gool, L.V., Williams, C.K.I., Winn, J.M., Zisserman, A.: The pascal visual object classes (VOC) challenge. International Journal of Computer Vision (2010)
2010
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Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)
2014
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Lin, T., Maire, M., Belongie, S.J., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: European Conference on Computer Vision (ECCV) (2014)
2014
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Everingham, M., Eslami, S.M.A., Gool, L.V., Williams, C.K.I., Winn, J.M., Zisserman, A.: The pascal visual object classes challenge: A retrospective. International Journal of Computer Vision (2015)
2015
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Girshick, R.B.: Fast R-CNN. In: IEEE International Conference on Computer Vision (ICCV) (2015)
2015
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He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2015)
2015
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Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML DeepLearning workshop (2015)
2015
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Misra, I., Shrivastava, A., Hebert, M.: Watch and learn: Semi-supervised learning of object detectors from videos. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
2015
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Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (NIPS) (2015)
2015
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Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M.S., Berg, A.C., Li, F.: Imagenet large scale visual recognition challenge. International Journal of Computer Vision (IJCV) (2015)
2015
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Bertinetto, L., Henriques, J.F., Valmadre, J., Torr, P.H.S., Vedaldi, A.: Learning feed-forward one-shot learners. In: Advances in Neural Information Processing Systems (NIPS) (2016)
2016
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Bilen, H., Vedaldi, A.: Weakly supervised deep detection networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
2016
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
2016
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Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C., Berg, A.C.: SSD: single shot multibox detector. In: European Conference on Computer Vision (ECCV) (2016)
2016
Cited alongside, same era.
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
2016
Cited alongside, same era.
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., Wierstra, D.: Matching networks for one shot learning. In: Advances in Neural Information Processing Systems (NIPS) (2016)
2016
Cited alongside, same era.
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning (ICML) (2017)
2017
Cited alongside, same era.
Qiao, S., Liu, C., Shen, W., Yuille, A.L.: Few-shot image recognition by predicting parameters from activations. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
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Singh, B., Davis, L.S.: An analysis of scale invariance in object detection-SNIP. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
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Singh, B., Najibi, M., Davis, L.S.: SNIPER: efficient multi-scale training. In: Advances in Neural Information Processing Systems (NIPS) (2018)
2018
Later among the works it cites.
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H.S., Hospedales, T.M.: Learning to compare: Relation network for few-shot learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Later among the works it cites.
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2017
Cited alongside, same era.
Lin, T., Dollár, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S.J.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Cited alongside, same era.
Munkhdalai, T., Yu, H.: Meta networks. In: International Conference on Machine Learning (ICML) (2017)
2017
Cited alongside, same era.
Redmon, J., Farhadi, A.: Yolo9000: Better, faster, stronger. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Cited alongside, same era.
Tang, P., Wang, X., Bai, X., Liu, W.: Multiple instance detection network with online instance classifier refinement. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Cited alongside, same era.
Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Cited alongside, same era.
Chen, H., Wang, Y., Wang, G., Qiao, Y.: LSTD: A low-shot transfer detector for object detection. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (2018)
2018
Cited alongside, same era.
Kim, Y., Kang, B., Kim, D.: SAN: learning relationship between convolutional features for multi-scale object detection. In: European Conference on Computer Vision (ECCV) (2018)
2018
Cited alongside, same era.
Tang, P., Wang, X., Wang, A., Yan, Y., Liu, W., Huang, J., Yuille, A.L.: Weakly supervised region proposal network and object detection. In: European Conference on Computer Vision (ECCV) (2018)
2018
Later among the works it cites.
Dong, X., Zheng, L., Ma, F., Yang, Y., Meng, D.: Few-example object detection with model communication. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2019)
2019
Later among the works it cites.
Gao, J., Wang, J., Dai, S., Li, L.J., Nevatia, R.: Note-rcnn: Noise tolerant ensemble rcnn for semi-supervised object detection. In: IEEE International Conference on Computer Vision (ICCV) (2019)
2019
Later among the works it cites.
Kang, B., Liu, Z., Wang, X., Yu, F., Feng, J., Darrell, T.: Few-shot object detection via feature reweighting. In: IEEE International Conference on Computer Vision (ICCV) (2019)
2019
Later among the works it cites.
Karlinsky, L., Shtok, J., Harary, S., Schwartz, E., Aides, A., Feris, R., Giryes, R., Bronstein, A.M.: Repmet: Representative-based metric learning for classification and few-shot object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
2019
Later among the works it cites.
Wan, F., Liu, C., Ke, W., Ji, X., Jiao, J., Ye, Q.: C-MIL: continuation multiple instance learning for weakly supervised object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Fan, Q., Zhuo, W., Tang, C.K., Tai, Y.W.: Few-shot object detection with attention-rpn and multi-relation detector. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
2020
Closest in time.
Wang, X., Huang, T.E., Darrell, T., Gonzalez, J.E., Yu, F.: Frustratingly simple few-shot object detection. In: International Conference on Machine Learning (ICML) (2020)
2020
Closest in time.