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Composed Image Retrieval (CIR) is a complex task that retrieves images using a query, which is configured with an image and a caption that describes desired modifications to that image.
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Liu, Z., Rodriguez-Opazo, C., Teney, D., Gould, S.: Image retrieval on real-life images with pre-trained vision-and-language models. In: CVPR (2021)
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Ma, J., Jiang, X., Fan, A., Jiang, J., Yan, J.: Image matching from handcrafted to deep features: A survey. IJCV 129
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: ICML. PMLR (2021)
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Wu, H., Gao, Y., Guo, X., Al-Halah, Z., Rennie, S., Grauman, K., Feris, R.: Fashion iq: A new dataset towards retrieving images by natural language feedback. In: CVPR (2021)
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Baldrati, A., Bertini, M., Uricchio, T., Del Bimbo, A.: Effective conditioned and composed image retrieval combining clip-based features. In: CVPR (2022)
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Cohen, N., Gal, R., Meirom, E.A., Chechik, G., Atzmon, Y.: “this is my unicorn, fluffy”: Personalizing frozen vision-language representations. In: ECCV (2022)
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