Composed image retrieval attempts to retrieve an image of interest from gallery images through a composed query of a reference image and its corresponding modified text. It has recently attracted attention due to the collaboration of information-rich images and concise language to precisely express the requirements of target images. Most of the existing composed image retrieval methods follow a supervised learning paradigm to perform training on a costly triplet dataset composed of a reference image, modified text, and a corresponding target image. To alleviate the demand for difficult-to-obtain labeled triplet data, recent methods have introduced zero-shot composed image retrieval (ZS-CIR), which aims to retrieve the target image without the supervision of human-labeled triplets but instead relies on image-text pairs or self-generated triplets. However, these methods are less computationally efficient due to the requirement of training and also less understandable, assuming that the interaction between image and text is conducted with implicit query embedding. In this work, we present a new Training-Free zero-shot Composed Image Retrieval (TFCIR) method which translates the query into explicit human-understandable text. This helps improve computation efficiency while maintaining the generalization of foundation models. Further, we introduce a Local Concept Reranking (LCR) mechanism to focus on discriminative local information extracted from the modified instruction. Extensive experiments on three ZS-CIR benchmarks show that the proposed approach can achieve comparable performances with state-of-the-art methods and significantly outperforms other training-free methods on the open domain datasets, CIRR and CIRCO, as well as the fashion domain dataset, FashionIQ.

本文提出了一种新的基于零样本训练的无需训练的组合图像检索方法(TFCIR)，通过将查询翻译成易于理解的文本，提高计算效率并保持模型的泛化性。此外，引入了局部概念再排序机制(LCR)来聚焦于从修改指令中提取的有区别的局部信息。在三个ZS-CIR基准测试上的大量实验证明了该方法在开放领域数据集CIRR、CIRCO以及时尚领域数据集FashionIQ上实现了与最先进方法相当的性能，并明显优于其他无需训练的方法。

无训练的零样本组合图像检索和本地概念重新排序