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--- image_embedding:reading [2019/04/25 05:10] – yang
+++ image_embedding:reading [2019/10/28 02:08] (current) – yang
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   * Barz, Björn, and Joachim Denzler. "[[https://arxiv.org/pdf/1809.09924.pdf | Hierarchy-based image embeddings for semantic image retrieval.]]" In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 638-647. IEEE, 2019.
   * Verma, Nakul, Dhruv Mahajan, Sundararajan Sellamanickam, and Vinod Nair. "[[http://www.cs.toronto.edu/~vnair/cvpr12.pdf | Learning hierarchical similarity metrics.]]" In 2012 IEEE conference on computer vision and pattern recognition, pp. 2280-2287. IEEE, 2012.
+  * Tianshui Chen et al. [[https://arxiv.org/pdf/1808.04505.pdf | Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding]]
 ===Other image embedding works===
   * Oh Song, Hyun, Yu Xiang, Stefanie Jegelka, and Silvio Savarese. "[[https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Song_Deep_Metric_Learning_CVPR_2016_paper.pdf | Deep metric learning via lifted structured feature embedding.]]" In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4004-4012. 2016.
-  * Nagarajan, Tushar, and Kristen Grauman. "Attributes as operators: factorizing unseen attribute-object compositions." In Proceedings of the European Conference on Computer Vision (ECCV), pp. 169-185. 2018.
+  * Nagarajan, Tushar, and Kristen Grauman. "[[http://www.cs.utexas.edu/~grauman/papers/attributes-operators-eccv2018.pdf | Attributes as operators: factorizing unseen attribute-object compositions.]]" In Proceedings of the European Conference on Computer Vision (ECCV), pp. 169-185. 2018.
 ===Text embedding===