Museum Exhibit Identification Challenge for Domain Adaptation and Beyond
Piotr Koniusz, Yusuf Tas, Hongguang Zhang, Mehrtash Harandi, Fatih, Porikli, Rui Zhang

TL;DR
This paper introduces the Open Museum Identification Challenge dataset to advance research in domain adaptation, egocentric recognition, and few-shot learning for artwork identification across diverse museum environments.
Contribution
It provides a new dataset with real-world challenges and develops baseline methods using class scatter alignment and Riemannian metrics for domain adaptation in artwork recognition.
Findings
Achieved up to 90% accuracy on the dataset
Analyzed challenges like lighting, occlusion, and viewpoint variations
Established baseline performance for future research
Abstract
In this paper, we approach an open problem of artwork identification and propose a new dataset dubbed Open Museum Identification Challenge (Open MIC). It contains photos of exhibits captured in 10 distinct exhibition spaces of several museums which showcase paintings, timepieces, sculptures, glassware, relics, science exhibits, natural history pieces, ceramics, pottery, tools and indigenous crafts. The goal of Open MIC is to stimulate research in domain adaptation, egocentric recognition and few-shot learning by providing a testbed complementary to the famous Office dataset which reaches 90% accuracy. To form our dataset, we captured a number of images per art piece with a mobile phone and wearable cameras to form the source and target data splits, respectively. To achieve robust baselines, we build on a recent approach that aligns per-class scatter matrices of the source and target CNN…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Advanced Image and Video Retrieval Techniques · Human Pose and Action Recognition
