Search Results for author: Wenjin Hou

Found 6 papers, 2 papers with code

Visual-Augmented Dynamic Semantic Prototype for Generative Zero-Shot Learning

no code implementations23 Apr 2024 Wenjin Hou, Shiming Chen, Shuhuang Chen, Ziming Hong, Yan Wang, Xuetao Feng, Salman Khan, Fahad Shahbaz Khan, Xinge You

Generative Zero-shot learning (ZSL) learns a generator to synthesize visual samples for unseen classes, which is an effective way to advance ZSL.

Zero-Shot Learning

Progressive Semantic-Guided Vision Transformer for Zero-Shot Learning

no code implementations11 Apr 2024 Shiming Chen, Wenjin Hou, Salman Khan, Fahad Shahbaz Khan

ZSLViT mainly considers two properties in the whole network: i) discover the semantic-related visual representations explicitly, and ii) discard the semantic-unrelated visual information.

Zero-Shot Learning

EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning

no code implementations19 Aug 2023 Shiming Chen, Shihuang Chen, Wenjin Hou, Weiping Ding, Xinge You

However, existing GAN-based generative ZSL methods are based on hand-crafted models, which cannot adapt to various datasets/scenarios and fails to model instability.

Generative Adversarial Network Neural Architecture Search +1

Evolving Semantic Prototype Improves Generative Zero-Shot Learning

no code implementations12 Jun 2023 Shiming Chen, Wenjin Hou, Ziming Hong, Xiaohan Ding, Yibing Song, Xinge You, Tongliang Liu, Kun Zhang

After alignment, synthesized sample features from unseen classes are closer to the real sample features and benefit DSP to improve existing generative ZSL methods by 8. 5\%, 8. 0\%, and 9. 7\% on the standard CUB, SUN AWA2 datasets, the significant performance improvement indicates that evolving semantic prototype explores a virgin field in ZSL.

Zero-Shot Learning

TransZero++: Cross Attribute-Guided Transformer for Zero-Shot Learning

1 code implementation16 Dec 2021 Shiming Chen, Ziming Hong, Wenjin Hou, Guo-Sen Xie, Yibing Song, Jian Zhao, Xinge You, Shuicheng Yan, Ling Shao

Analogously, VAT uses the similar feature augmentation encoder to refine the visual features, which are further applied in visual$\rightarrow$attribute decoder to learn visual-based attribute features.

Attribute Decoder +1

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