Effective image retrieval with text feedback stands to impact a range of
real-world applications, such as e-commerce. Given a source image and text
feedback that describes the desired modifications to that image, the goal is to
retrieve the target images that resemble the source yet satisfy the given
modifications by composing a multi-modal (image-text) query. We propose a novel
solution to this problem, Additive Attention Compositional Learning (AACL),
that uses a multi-modal transformer-based architecture and effectively models
the image-text contexts. Specifically, we propose a novel image-text
composition module based on additive attention that can be seamlessly plugged
into deep neural networks. We also introduce a new challenging benchmark
derived from the Shopping100k dataset. AACL is evaluated on three large-scale
datasets (FashionIQ, Fashion200k, and Shopping100k), each with strong
baselines. Extensive experiments show that AACL achieves new state-of-the-art
results on all three datasets.

该研究提出了一种称为 AACL 的方法，它基于多模式 Transformer 结构，可用于有效地检索类似于给定源图像的目标图像并且满足所提供的修改。在多项实验中，AACL 均实现了最新的最佳结果。