5 citations · 6 across the 4 of their papers we have counts for
9 papers
Episode-based Prototype Generating Network for Zero-Shot Learning
Yunlong Yu, Zhong Ji, Zhongfei Zhang +1
We introduce a simple yet effective episode-based training framework for zero-shot learning (ZSL), where the learning system requires to recognize unseen classes given only the cor…
A Semantics-Guided Class Imbalance Learning Model for Zero-Shot Classification
Zhong Ji, Xuejie Yu, Yunlong Yu +2
Zero-Shot Classification (ZSC) equips the learned model with the ability to recognize the visual instances from the novel classes via constructing the interactions between the visu…
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…
Attribute-Guided Network for Cross-Modal Zero-Shot Hashing
Zhong Ji, Yuxin Sun, Yunlong Yu +2
Zero-Shot Hashing aims at learning a hashing model that is trained only by instances from seen categories but can generate well to those of unseen categories. Typically, it is achi…
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.…