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 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 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