Compact Deep Aggregation for Set Retrieval

• URL: http://arxiv.org/abs/2003.11794v1
• Date: Thu, 26 Mar 2020 08:43:15 GMT
• Title: Compact Deep Aggregation for Set Retrieval
• Authors: Yujie Zhong, Relja Arandjelovi\'c, Andrew Zisserman
• Abstract summary: We focus on retrieving images containing multiple faces from a large scale dataset of images. Here the set consists of the face descriptors in each image, and given a query for multiple identities, the goal is then to retrieve, in order, images which contain all the identities. We show that this compact descriptor has minimal loss of discriminability up to two faces per image, and degrades slowly after that.
• Score: 87.52470995031997
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The objective of this work is to learn a compact embedding of a set of descriptors that is suitable for efficient retrieval and ranking, whilst maintaining discriminability of the individual descriptors. We focus on a specific example of this general problem -- that of retrieving images containing multiple faces from a large scale dataset of images. Here the set consists of the face descriptors in each image, and given a query for multiple identities, the goal is then to retrieve, in order, images which contain all the identities, all but one, \etc To this end, we make the following contributions: first, we propose a CNN architecture -- {\em SetNet} -- to achieve the objective: it learns face descriptors and their aggregation over a set to produce a compact fixed length descriptor designed for set retrieval, and the score of an image is a count of the number of identities that match the query; second, we show that this compact descriptor has minimal loss of discriminability up to two faces per image, and degrades slowly after that -- far exceeding a number of baselines; third, we explore the speed vs.\ retrieval quality trade-off for set retrieval using this compact descriptor; and, finally, we collect and annotate a large dataset of images containing various number of celebrities, which we use for evaluation and is publicly released.

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