26 citations · 37 across the 6 of their papers we have counts for
6 papers
Bi-Adversarial Auto-Encoder for Zero-Shot Learning
Yunlong Yu, Zhong Ji, Yanwei Pang +3
Existing generative Zero-Shot Learning (ZSL) methods only consider the unidirectional alignment from the class semantics to the visual features while ignoring the alignment from th…
Stacked Semantic-Guided Attention Model for Fine-Grained Zero-Shot Learning
Yunlong Yu, Zhong Ji, Yanwei Fu +3
Zero-Shot Learning (ZSL) is achieved via aligning the semantic relationships between the global image feature vector and the corresponding class semantic descriptions. However, usi…
Zero-Shot Learning via Latent Space Encoding
Yunlong Yu, Zhong Ji, Jichang Guo +2
Zero-Shot Learning (ZSL) is typically achieved by resorting to a class semantic embedding space to transfer the knowledge from the seen classes to unseen ones. Capturing the common…
Semantic Softmax Loss for Zero-Shot Learning
Zhong Ji, Yunxin Sun, Yulong Yu +2
A typical pipeline for Zero-Shot Learning (ZSL) is to integrate the visual features and the class semantic descriptors into a multimodal framework with a linear or bilinear model.…
Transductive Zero-Shot Learning with Adaptive Structural Embedding
Yunlong Yu, Zhong Ji, Jichang Guo +1
Zero-shot learning (ZSL) endows the computer vision system with the inferential capability to recognize instances of a new category that has never seen before. Two fundamental chal…
Transductive Zero-Shot Learning with a Self-training dictionary approach
Yunlong Yu, Zhong Ji, Xi Li +4
As an important and challenging problem in computer vision, zero-shot learning (ZSL) aims at automatically recognizing the instances from unseen object classes without training dat…