Page 168 - 《软件学报》2021年第11期

3494 Journal of Software 软件学报 Vol.32, No.11, November 2021

References:
[1] Zhu C, Tan X, Zhou F, Liu X, Yue KY, Ding ER, Ma Y. Fine-grained video categorization with redundancy reduction attention. In:
Proc. of the European Conf. on Computer Vision (ECCV). Berlin: Springer-Verlag, 2018. 139−155.
[2] Torralba A, Efros AA. Unbiased look at dataset bias. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition
(CVPR). Piscataway: IEEE, 2011. 1521−1528.
[3] Zhang NN, Donahue J, Girshick R, Darrell T. Part-based r-CNNs for fine-grained category detection. In: Proc. of the Int’l Conf. on
Machine Learning (ICML). New York: ACM, 2014. 834−849.
[4] Krause J, Jin HL, Yang JC, Li FF. Fine-grained recognition without part annotations. In: Proc. of the IEEE Conf. on Computer
Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2015. 5546−5555.
[5] Xiao TJ, Xu YC, Yang KY, Zhang JX, Peng YX, Zhang Z. The application of two-level attention models in deep convolutional
neural network for fine-grained image classification. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition
(CVPR). Piscataway: IEEE, 2015. 842−850.
[6] Uijlings JRR, van de Sande KEA, Gevers T, Smeulders AWM. Selective search for object recognition. Int’l Journal of Computer
Vision (IJCV), 2013,104(2):154−171.
[7] Fu JL, Zheng HL, Mei T. Look closer to see better: Recurrent attention convolutional neural network for fine-grained image
recognition. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2017.
4438−4446.
[8] He XT, Peng YX, Zhao JJ. Stackdrl: Stacked deep reinforcement learning for fine-grained visual categorization. In: Proc. of the
Int’l Joint Conf. on Artificial Intelligence (IJCAI). San Francisco: Morgan Kaufmann Publishers, 2018. 741−747.
[9] Lin TY, Chowdhury AR, Maji S. Bilinear CNN models for fine-grained visual recognition. In: Proc. of the Int’l Conf. of Computer
Vision (ICCV). Piscataway: IEEE, 2015. 1449−1457.
[10] Gao Y, Beijbom O, Zhang N, Darrell T. Compact bilinear pooling. In: Proc. of the IEEE Conf. on Computer Vision and Pattern
Recognition (CVPR). Piscataway: IEEE, 2016. 317−326.
[11] Cui Y, Zhou F, Wang J, Liu X, Lin YQ, Belongie S. Kernel pooling for convolutional neural networks. In: Proc. of the IEEE
Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2017. 3049−3058.
[12] Wang YM, Morariu VI, Davis LS. Learning a discriminative filter bank within a CNN for fine-grained recognition. In: Proc. of the
IEEE Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2018. 4148−4157.
[13] He XT, Peng YX. Fine-grained image classification via combining vision and language. In: Proc. of the IEEE Conf. on Computer
Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2017. 5994−6002.
[14] Chen TS, Lin L, Chen RQ, Wu Y, Luo XN. Knowledge-embedded representation learning for fine-grained image recognition. In:
Proc. of the Int’l Joint Conf. on Artificial Intelligence (IJCAI). San Francisco: Morgan Kaufmann Publishers, 2018. 627−634.
[15] Saito T, Kanezaki A, Harada T. Ibc127: Video dataset for fine-grained bird classification. In: Proc. of the IEEE Int’l Conf. on
Multimedia and Expo (ICME). Piscataway: IEEE, 2016. 1−6.
[16] Kalogeiton V, Ferrari V, Schmid C. Analysing domain shift factors between videos and images for object detection. IEEE Trans. on
Pattern Analysis and Machine Intelligence (TPAMI), 2016,38(11):2327−2334.
[17] Ben-David S, Blitzer J, Crammer K, Pereira F. Analysis of representations for domain adaptation. In: Proc. of the Neural
Information Processing Systems (NeurIPS). Cambridge: MIT Press, 2007. 137−144.
[18] Gebru T, Hoffman J, Li FF. Fine-grained recognition in the wild: A multi-task domain adaptation approach. In: Proc. of the IEEE
Int’l Conf. onComputer Vision (ICCV). Piscataway: IEEE, 2017. 1358−1367.
[19] Cui Y, Song Y, Sun C, Howard A, Belongie S. Large scale fine-grained categorization and domain-specific transfer learning. In:
Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2018. 4109−4118.
[20] He KM, Zhang XY, Ren SQ, Sun J. Deep residual learning for image recognition. In: Proc. of the IEEE Conf. on Computer Vision
and Pattern Recognition (CVPR). Piscataway: IEEE, 2016. 770−778.
[21] Yosinski J, Clune J, Bengio Y, Lipson H. How transferable are features in deep neural networks? In: Proc. of the Neural
Information Processing Systems (NeurIPS). Cambridge: MIT Press, 2014. 3320−3328.