Self-supervised Multi-view Disentanglement for Expansion of Visual Collections

• URL: http://arxiv.org/abs/2302.02249v1
• Date: Sat, 4 Feb 2023 22:09:17 GMT
• Title: Self-supervised Multi-view Disentanglement for Expansion of Visual Collections
• Authors: Nihal Jain, Praneetha Vaddamanu, Paridhi Maheshwari, Vishwa Vinay, Kuldeep Kulkarni
• Abstract summary: 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. Our objective is to design a retrieval algorithm that effectively combines similarities computed over representations from multiple views.
• Score: 6.944742823561
• License: http://creativecommons.org/licenses/by/4.0/
• 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. We show how effective retrieval can be performed by prioritizing candidate expansion images that match the intent of a query collection. Finally, we present a new querying mechanism for image search enabled by composing multiple collections and perform retrieval under this setting using the techniques presented in this paper.

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