Dr Yang Long's Outputs (6)

A plug-in attribute correction module for generalized zero-shot learning (2020)
Journal Article
Zhang, H., Bai, H., Long, Y., Liu, L., & Shao, L. (2021). A plug-in attribute correction module for generalized zero-shot learning. Pattern Recognition, 112, Article 107767. https://doi.org/10.1016/j.patcog.2020.107767

While Zero Shot Learning models can recognize new classes without training examples, they often fails to incorporate both seen and unseen classes together at the test time, which is known as the Generalized Zero-shot Learning (GZSL) problem. This pap... Read More about A plug-in attribute correction module for generalized zero-shot learning.

Modality independent adversarial network for generalized zero shot image classification (2020)
Journal Article
Zhang, H., Wang, Y., Long, Y., Yang, L., & Shao, L. (2021). Modality independent adversarial network for generalized zero shot image classification. Neural Networks, 134, 11-22. https://doi.org/10.1016/j.neunet.2020.11.007

Zero Shot Learning (ZSL) aims to classify images of unseen target classes by transferring knowledge from source classes through semantic embeddings. The core of ZSL research is to embed both visual representation of object instance and semantic descr... Read More about Modality independent adversarial network for generalized zero shot image classification.

Pseudo Distribution on Unseen Classes for Generalized Zero Shot Learning (2020)
Journal Article
Zhang, H., Liu, J., Yao, Y., & Long, Y. (2020). Pseudo Distribution on Unseen Classes for Generalized Zero Shot Learning. Pattern Recognition Letters, 135, 451-458. https://doi.org/10.1016/j.patrec.2020.05.021

Although Zero Shot Learning (ZSL) has attracted more and more attention due to its powerful ability of recognizing new objects without retraining, it has a serious drawback that it only focuses on unseen classes during prediction. To solve this issue... Read More about Pseudo Distribution on Unseen Classes for Generalized Zero Shot Learning.

Deep transductive network for generalized zero shot learning (2020)
Journal Article
Zhang, H., Liu, L., Long, Y., Zhang, Z., & Shao, L. (2020). Deep transductive network for generalized zero shot learning. Pattern Recognition, 105, Article 107370. https://doi.org/10.1016/j.patcog.2020.107370

Zero Shot Learning (ZSL) aims to learn projective functions on labeled seen data and transfer the learned functions to unseen classes by discovering their relationship with semantic embeddings. However, the mapping process often suffers from the doma... Read More about Deep transductive network for generalized zero shot learning.

A Joint Label Space for Generalized Zero-Shot Classification (2020)
Journal Article
Li, J., Lan, X., Long, Y., Liu, Y., Chen, X., Shao, L., & Zheng, N. (2020). A Joint Label Space for Generalized Zero-Shot Classification. IEEE Transactions on Image Processing, 29, 5817-5831. https://doi.org/10.1109/tip.2020.2986892

The fundamental problem of Zero-Shot Learning (ZSL) is that the one-hot label space is discrete, which leads to a complete loss of the relationships between seen and unseen classes. Conventional approaches rely on using semantic auxiliary information... Read More about A Joint Label Space for Generalized Zero-Shot Classification.

Learning discriminative domain-invariant prototypes for generalized zero shot learning (2020)
Journal Article
Wang, Y., Zhang, H., Zhang, Z., Long, Y., & Shao, L. (2020). Learning discriminative domain-invariant prototypes for generalized zero shot learning. Knowledge-Based Systems, 196, Article 105796. https://doi.org/10.1016/j.knosys.2020.105796

Zero-shot learning (ZSL) aims to recognize objects of target classes by transferring knowledge from source classes through the semantic embeddings bridging. However, ZSL focuses the recognition only on unseen classes, which is unreasonable in realist... Read More about Learning discriminative domain-invariant prototypes for generalized zero shot learning.