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- W4321485430 abstract "Image search engines enable the retrieval of images relevant to a query image. In this work, we consider the setting where a query for similar images is derived from a collection of images. For visual search, the similarity measurements may be made along multiple axes, or views, such as style and color. We assume access to a set of feature extractors, each of which computes representations for a specific view. Our objective is to design a retrieval algorithm that effectively combines similarities computed over representations from multiple views. To this end, we propose a self-supervised learning method for extracting disentangled view-specific representations for images such that the inter-view overlap is minimized. We show how this allows us to compute the intent of a collection as a distribution over views. Finally, we show how effective retrieval can be performed by prioritizing candidate expansion images that match the intent of a query collection." @default.
- W4321485430 created "2023-02-23" @default.
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- W4321485430 date "2023-02-27" @default.
- W4321485430 modified "2023-09-25" @default.
- W4321485430 title "Self-supervised Multi-view Disentanglement for Expansion of Visual Collections" @default.
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- W4321485430 doi "https://doi.org/10.1145/3539597.3570425" @default.
- W4321485430 hasPublicationYear "2023" @default.
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