Outputs (56)

Towards light-weight annotations: Fuzzy interpolative reasoning for zero-shot image classification (2018)
Presentation / Conference Contribution
Long, Y., Tan, Y., Organisciak, D., Yang, L., & Shao, L. (2018, December). Towards light-weight annotations: Fuzzy interpolative reasoning for zero-shot image classification. Presented at BMVC

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.

Unsupervised Deep Hashing With Pseudo Labels for Scalable Image Retrieval (2018)
Journal Article
Zhang, H., Liu, L., Long, Y., & Shao, L. (2018). Unsupervised Deep Hashing With Pseudo Labels for Scalable Image Retrieval. IEEE Transactions on Image Processing, 27(4), 1626-1638. https://doi.org/10.1109/tip.2017.2781422

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.

Face recognition with a small occluded training set using spatial and statistical pooling (2018)
Journal Article
Long, Y., Zhu, F., Shao, L., & Han, J. (2018). Face recognition with a small occluded training set using spatial and statistical pooling. Information Sciences, 430, 634-644. https://doi.org/10.1016/j.ins.2017.10.042

Adaptive RGB Image Recognition by Visual-Depth Embedding (2018)
Journal Article
Cai, Z., Long, Y., & Shao, L. (2018). Adaptive RGB Image Recognition by Visual-Depth Embedding. IEEE Transactions on Image Processing, 27(5), 2471-2483. https://doi.org/10.1109/tip.2018.2806839

From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis (2017)
Presentation / Conference Contribution
Long, Y., Liu, L., Shao, L., Shen, F., Ding, G., & Han, J. (2017, December). From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis. Presented at Computer Vision and Pattern Recognition IEEE

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

Towards fine-grained open zero-shot learning: Inferring unseen visual features from attributes (2017)
Presentation / Conference Contribution
Long, Y., Liu, L., & Shao, L. (2017, December). Towards fine-grained open zero-shot learning: Inferring unseen visual features from attributes. Presented at 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) IEEE