4 citations · 4 across the 4 of their papers we have counts for
4 papers
What Happens Without Background? Constructing Foreground-Only Data for Fine-Grained Tasks
Yuetian Wang, Wenjin Hou, Qinmu Peng +1
Fine-grained recognition, a pivotal task in visual signal processing, aims to distinguish between similar subclasses based on discriminative information present in samples. However…
Visual-Augmented Dynamic Semantic Prototype for Generative Zero-Shot Learning
Wenjin Hou, Shiming Chen, Shuhuang Chen +6
Generative Zero-shot learning (ZSL) learns a generator to synthesize visual samples for unseen classes, which is an effective way to advance ZSL. However, existing generative metho…
EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning
Shiming Chen, Shihuang Chen, Wenjin Hou +2
Zero-shot learning (ZSL) aims to recognize the novel classes which cannot be collected for training a prediction model. Accordingly, generative models (e.g., generative adversarial…
Evolving Semantic Prototype Improves Generative Zero-Shot Learning
Shiming Chen, Wenjin Hou, Ziming Hong +5
In zero-shot learning (ZSL), generative methods synthesize class-related sample features based on predefined semantic prototypes. They advance the ZSL performance by synthesizing u…