Yet Another Computer Vision Index To Datasets (YACVID) - Details

Stand: 2020-06-02 000000m 23:27:48 - Overview

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Name (Institute + Shorttitle)Open MIC 
Description (include details on usage, files and paper references)Open MIC (Open Museum Identification Challenge) 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 31 dataset which reaches ~90% accuracy.

For the source domain, we captured the photos in a controlled fashion by Android phones e.g., we ensured that each exhibit is centered and non-occluded in photos. We prevented adverse capturing conditions and did not mix multiple objects per photo unless they were all part of one exhibit. We captured 230 photos of each art piece from different viewpoints and distances in their natural settings.
For the target domain, we employed an egocentric setup to ensure in-the-wild capturing process. We equipped several volunteers with cheap wearable cameras and let them stroll and interact with artworks at their discretion. Open MIC contains 10 distinct source-target subsets of images from 10 different kinds of museum exhibition spaces, each exhibiting various photometric and geometric challenges. We annotated each image with labels of art pieces visible in it. The wearable cameras were set to capture an image every 10s and they operated in-the-wild, e.g., volunteers had no control over shutter, focus, centering, etc.
Therefore, the collected target subsets exhibit many realistic challenges, e.g., sensor noises, motion blur, occlusions, background clutter, varying viewpoints, scale changes, rotations, glares, transparency, non-planar surfaces, clipping, multiple exhibits, active light, color inconstancy, very large or small exhibits, to name but a few phenomena.
Every subset (10 distinct exhibition spaces) contains 37166 exhibits to identify. We provide 5 train, 5 validation, and 5 test splits per exhibition. In total, our dataset contains 866 unique exhibit labels, 8560 source and 7596 target images.

Open MIC contains 10 distinct source-target subsets of images from 10 different kinds of museum exhibition spaces. They include:
Paintings from Shenzhen Museum (Shn),
Clocks and Watch Gallery (Clk), and the Indian and Chinese Sculptures (Scl) from the Palace Museum,
Xiangyang Science Museum (Sci),
European Glass Art (Gls) and the Collection of Cultural Relics (Rel) from the Hubei Provincial Museum,
Nature, Animals and Plants in Ancient Times (Nat) from Shanghai Natural History Museum,
Comprehensive Historical and Cultural Exhibits from Shaanxi History Museum (Shx),
Sculptures, Pottery and Bronze Figurines from the Cleveland Museum of Arts (Clv),
Indigenous Arts from Honolulu Museum Of Arts (Hon). 
URL Link 
Files (#)160000 
References (SKIPPED)
Category (SKIPPED) 
Tags (single words, spaced)museum recognition identification benchmark exhibit image paintings, time, sculpture, glassware, ceramics, indigenous 
Last Changed2020-06-02 
Turing (2.12+3.25=?) :-)