Retrieving Black-box Optimal Images from External Databases
This work addresses a retrieval challenge for users with limited database access, enabling personalized image search, but it appears incremental as it builds on standard retrieval problems with new assumptions.
The paper tackled the problem of retrieving optimal images from an external database using a user's black-box preference function, such as a deep neural network, under tight API query limits, and proposed an efficient algorithm called Tiara that outperformed baselines in experiments.
Suppose we have a black-box function (e.g., deep neural network) that takes an image as input and outputs a value that indicates preference. How can we retrieve optimal images with respect to this function from an external database on the Internet? Standard retrieval problems in the literature (e.g., item recommendations) assume that an algorithm has full access to the set of items. In other words, such algorithms are designed for service providers. In this paper, we consider the retrieval problem under different assumptions. Specifically, we consider how users with limited access to an image database can retrieve images using their own black-box functions. This formulation enables a flexible and finer-grained image search defined by each user. We assume the user can access the database through a search query with tight API limits. Therefore, a user needs to efficiently retrieve optimal images in terms of the number of queries. We propose an efficient retrieval algorithm Tiara for this problem. In the experiments, we confirm that our proposed method performs better than several baselines under various settings.