Knowledge-Enhanced Dual-stream Zero-shot Composed Image Retrieval
Authors: Yucheng Suo, Fan Ma, Linchao Zhu, Yi Yang

We study the zero-shot Composed Image Retrieval (ZS-CIR) task which is to retrieve the target image given a reference image and a description without training on the triplet datasets. Previous works generate pseudo-word tokens by projecting the reference image features to the text embedding space. However they focus on the global visual representation ignoring the representation of detailed attributes e.g. color object number and layout. To address this challenge we propose a Knowledge-Enhanced Dual-stream zero-shot composed image retrieval framework (KEDs). KEDs implicitly models the attributes of the reference images by incorporating a database. The database enriches the pseudo-word tokens by providing relevant images and captions emphasizing shared attribute information in various aspects. In this way KEDs recognizes the reference image from diverse perspectives. Moreover KEDs adopts an extra stream that aligns pseudo-word tokens with textual concepts leveraging pseudo-triplets mined from image-text pairs. The pseudo-word tokens generated in this stream are explicitly aligned with fine-grained semantics in the text embedding space. Extensive experiments on widely used benchmarks i.e. ImageNet-R COCO object Fashion-IQ and CIRR show that KEDs outperforms previous zero-shot composed image retrieval methods. Code is available at https://github.com/suoych/KEDs.