通过多级注意力机制精准调整服装属性，提升穿搭搜索准确性。
Garment Attribute Manipulation with Multi-level Attention
- 采用双编码器与记忆块的多阶段注意力架构
- 在Shopping100k和DeepFashion数据集上达到当前最佳效果
- 适合需要精细控制服装视觉特征的电商系统
在快速发展的在线时尚购物领域，对更个性化和交互式的图像检索系统的需求日益迫切。现有方法在精确操控特定服装属性时，常无意影响其他属性。为此，我们提出GAMMA（Garment Attribute Manipulation with Multi-level Attention），一种将属性解耦表示与多阶段注意力架构相结合的新框架。GAMMA 能够实现对时尚图像属性的精准操控，使用户可高精度地优化搜索结果。通过使用双编码器Transformer和记忆块，该模型在Shopping100k和DeepFashion等主流数据集上取得了当前最优性能。
原文摘要 · Abstract (English)
In the rapidly evolving field of online fashion shopping, the need for more personalized and interactive image retrieval systems has become paramount. Existing methods often struggle with precisely manipulating specific garment attributes without inadvertently affecting others. To address this challenge, we propose GAMMA (Garment Attribute Manipulation with Multi-level Attention), a novel framework that integrates attribute-disentangled representations with a multi-stage attention-based architecture. GAMMA enables targeted manipulation of fashion image attributes, allowing users to refine their searches with high accuracy. By leveraging a dual-encoder Transformer and memory block, our model achieves state-of-the-art performance on popular datasets like Shopping100k and DeepFashion.
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