[1] Zeynep Akata et al. “Evaluation of output embeddings for fine-grained image classification”. In: IEEE Computer Society, 2015, pp. 2927–2936. doi:10.1109/CVPR.2015.7298911.
[2] Sara Beery et al. “Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection”. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Computer Vision Foundation / IEEE, 2020, pp. 13072–13082. doi : 10.1109/CVPR42600.2020.01309.
[3] Alexey Dosovitskiy et al. “An image is worth 16x16 words: Transformers for image recognition at scale”.In: 2020.
[4] Nasir Hayat et al. “Synthesizing the Unseen for Zero-Shot Object Detection”. In: ed. by Hiroshi Ishikawa et al. Vol. 12624. Springer, 2020, pp. 155–170. doi:10.1007/978-3-030-69535-4\_10.
[5] Christoph H. Lampert, Hannes Nickisch, and Stefan Harmeling. “Attribute-Based Classification for Zero-Shot Visual Object Categorization”. In:
IEEE Trans. Pattern Anal. Mach. Intell. 36.3 (2014), pp. 453–465. url:https://doi.org/10.1109/TPAMI.2013.140.
[6] Shafin Rahman, Salman H. Khan, and Fatih Porikli. “Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts”. In: Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision. Ed. by C. V. Jawahar et al. Vol. 11361. Lecture Notes in Computer Science. Springer, 2018, pp. 547–563. doi:10.1007/978-3-030-20887-5\_34.
[7] Edgar Schonfeld et al. “Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders”. In: Computer Vision Foundation / IEEE, 2019, pp. 8247–8255. doi:10.1109/CVPR.2019.00844.
[8] Yongqin Xian et al. “Feature Generating Networks for Zero-Shot Learning”. In: Computer Vision Foundation / IEEE Computer Society, 2018, pp. 5542–5551. doi:10.1109/CVPR.2018.00581.
[9] Li Zhang, Tao Xiang, and Shaogang Gong. “Learning a Deep Embedding Model for Zero-Shot Learning”. In: IEEE Computer Society, 2017, pp. 3010–3019. doi:10.1109/CVPR.2017.321.url: https://doi.org/10.1109/CVPR.2017.321.
[10] Shizhen Zhao et al. “GTNet: Generative Transfer Network for Zero-Shot Object Detection”. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020. AAAI Press, 2020, pp. 12967–12974.3