Self-supervised Multi-view Disentanglement for Expansion of Visual Collections
This addresses image search enhancement for users needing multi-view similarity, though it appears incremental as it builds on existing feature extractors and retrieval concepts.
The paper tackles the problem of retrieving similar images from a collection using multiple visual aspects (views) like style and color, proposing a self-supervised method to disentangle view-specific representations and compute collection intent for effective retrieval.
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.