[79] Shuo Yang, Zhaopan Xu, Kai Wang, Yang You, Hongxun Yao, Tongliang Liu, and Min Xu. Bicro: Noisy correspondence rectification for multi-modality data via bi-directional cross-modal similarity consistency. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19883-19892, 2023.
[80] Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. Extracting and composing robust features with denoising autoencoders. In International Conference on Machine Learning, pages 1096-1103, 2008.
[81] Zhun Zhong, Liang Zheng, Donglin Cao, and Shaozi Li. Re-ranking person re-identification with k-reciprocal encoding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1318–1327, 2017.
[82] Song Bai and Xiang Bai. Sparse contextual activation for efficient visual re-ranking. IEEE Transactions on Image Processing, 25(3):1056-1069, 2016.
[83] Mang Ye, Chao Liang, Yi Yu, Zheng Wang, Qingming Leng, Chunxia Xiao, Jun Chen, and Ruimin Hu. Person reidentification via ranking aggregation of similarity pulling and dissimilarity pushing. IEEE Transactions on Multimedia, 18(12):2553-2566, 2016.
[84] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations, 2016.
[85] George A Miller. WordNet: An electronic lexical database. MIT press, 1998.
[86] Tingting Wei, Yonghe Lu, Huiyou Chang, Qiang Zhou, and Xianyu Bao. A semantic approach for text clustering using wordnet and lexical chains. Expert Systems with Applications, 42(4): 2264-2275, 2015.
[87] Devansh Arpit, Stanisław Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al. A closer look at memorization in deep networks. In International Conference on Machine Learning, pages 233-242. PMLR, 2017.
[88] Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yantao Zheng. NUS-WIDE: a real-world web image database from national university ofSingapore. In Proceedings of the ACM International Conference on Image and Video Retrieval, pages 1-9, 2009.
[89] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 770-778, 2016.
[90] Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 31, 2017.
[91] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1-9, 2015.
[92] Manan Shah, Krishnamurthy Viswanathan, Chun-Ta Lu, Ariel Fuxman, Zhen Li, Aleksei Timofeev, Chao Jia, and Chen Sun. Inferring context from pixels for multimodal image classification. In Proceedings of the ACM International Conference on Information and Knowledge Management, pages 189–198, 2019.
[93] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2818-2826, 2016.
[94] Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, and Mu Li. Bag of tricks for image classification with convolutional neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 558-567, 2019.