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Dr Yang Long's Outputs (56)

Attribute relaxation from class level to instance level for zero-shot learning (2018)
Journal Article
Zhang, H., Long, Y., & Zhao, C. (2018). Attribute relaxation from class level to instance level for zero-shot learning. Electronics Letters, 54(20), 1170-1172. https://doi.org/10.1049/el.2018.5027

Conventional zero-shot learning (ZSL) methods usually use class-level attribute, which corresponds to a batch of images of same category. This setting is not reasonable since the images even though belong to same category still have variances in thei... Read More about Attribute relaxation from class level to instance level for zero-shot learning.

Triple Verification Network for Generalized Zero-Shot Learning (2018)
Journal Article
Zhang, H., Long, Y., Guan, Y., & Shao, L. (2019). Triple Verification Network for Generalized Zero-Shot Learning. IEEE Transactions on Image Processing, 28(1), 506-517. https://doi.org/10.1109/tip.2018.2869696

Conventional zero-shot learning approaches often suffer from severe performance degradation in the generalized zero-shot learning (GZSL) scenario, i.e., to recognize test images that are from both seen and unseen classes. This paper studies the Class... Read More about Triple Verification Network for Generalized Zero-Shot Learning.

Towards affordable semantic searching: Zero-shot retrieval via dominant attributes (2018)
Presentation / Conference Contribution
Long, Y., Liu, L., Shen, Y., & Shao, L. (2018, December). Towards affordable semantic searching: Zero-shot retrieval via dominant attributes. Presented at Thirty-Second AAAI Conference on Artificial Intelligence

Instance-level retrieval has become an essential paradigm to index and retrieves images from large-scale databases. Conventional instance search requires at least an example of the query image to retrieve images that contain the same object instance.... Read More about Towards affordable semantic searching: Zero-shot retrieval via dominant attributes.

Learning to recognise unseen classes by a few similes (2017)
Presentation / Conference Contribution
Long, Y., & Shao, L. (2017, December). Learning to recognise unseen classes by a few similes. Presented at Proceedings of the 25th ACM international conference on Multimedia ACM

Zero-Shot Learning Using Synthesised Unseen Visual Data with Diffusion Regularisation (2017)
Journal Article
Long, Y., Liu, L., Shen, F., Shao, L., & Li, X. (2018). Zero-Shot Learning Using Synthesised Unseen Visual Data with Diffusion Regularisation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(10), 2498-2512. https://doi.org/10.1109/tpami.2017.2762295

Sufficient training examples are the fundamental requirement for most of the learning tasks. However, collecting well-labelled training examples is costly. Inspired by Zero-shot Learning (ZSL) that can make use of visual attributes or natural languag... Read More about Zero-Shot Learning Using Synthesised Unseen Visual Data with Diffusion Regularisation.