Paper Info
Title
Museum Exhibit Identification Challenge For The Supervised Domain Adaptation And Beyond
Abstract
We study 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 indigenosus 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 similar to 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 streams. Moreover, we exploit the positive definite nature of such representations by using end-to-end Bregman divergences and the Riemannian metric. We present baselines such as training/evaluation per exhibition and training/evaluation on the combined set covering 866 exhibit identities. As each exhibition poses distinct challenges e.g., quality of lighting, motion blur, occlusions, clutter, viewpoint and scale variations, rotations, glares, transparency, non-planarity, clipping, we break down results w.r.t. these factors.
Year
DOI
Venue
2018
10.1007/978-3-030-01270-0_48
COMPUTER VISION - ECCV 2018, PT XVI
Field
DocType
Volume
Computer science,Domain adaptation,Human–computer interaction,Artificial intelligence,Machine learning
Conference
11220
ISSN
Citations 
PageRank 
0302-9743
4
0.39
References 
Authors
23
6
Name
Order
Citations
PageRank
Piotr Koniusz117316.64
Yusuf Tas240.73
Hongguang Zhang310616.70
Mehrtash Tafazzoli Harandi461839.19
Fatih Porikli53409169.13
Rui Zhang638186.83