THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images
By Martin N. Hebart, Adam H. Dickter, A. Kidder, Wan Y. Kwok, A. Corriveau, Caitlin Van Wicklin, Chris L. Baker. bioRxiv, 2019
323 citations recorded by the source corpus.
In recent years, the use of a large number of object concepts and naturalistic object images has been growing strongly in cognitive neuroscience research. Classical databases of object concepts are based mostly on a manually curated set of concepts. Further, databases of naturalistic object images typically consist of single images of objects cropped from their background, or a large number of naturalistic images of varying quality, requiring elaborate manual image curation. Here we provide a set of 1,854 diverse object concepts sampled systematically from concrete picturable and nameable nouns in the American English language. Using these object concepts, we conducted a large-scale web image search to compile a database of 26,107 high-quality naturalistic images of those objects, with 12 or more object images per concept and all images cropped to square size. Using crowdsourcing, we provide higher-level category membership for the 27 most common categories and validate them by relating them to representations in a semantic embedding derived from large text corpora. Finally, by feeding images through a deep convolutional neural network, we demonstrate that they exhibit high selectivity for different object concepts, while at the same time preserving variability of different object images within each concept. Together, the THINGS database provides a rich resource of object concepts and object images and offers a tool for both systematic and large-scale naturalistic research in the fields of psychology, neuroscience, and computer science.
Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/cf36635483035524773bed9e185a7bbadd05e30f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
In Computer Vision and Pattern Recognition
- Xiaodan Liang
- Luc Van Gool
- Zhenguo Li
- Dacheng Tao
- Ming-Hsuan Yang
- Andrea Vedaldi
- Richard Zhang
- Alan L. Yuille
Citations in the corpus
- DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data (2023, background): “[59] provide insight into high-level human similarity by training on a subset of the THINGS [35] dataset, focusing on concept similarity and omitting visual cues for images within a category.”
- DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection (2022, methodology): “…from multiple sources: (1) noun phrases extracted from the large-scale image-text pair dataset, i.e., YFCC100m; (2) category names of existing public detection datasets (e.g., Objects365 [45], OpenImages [26]); (3) object names from the manually-collected concept database, i.e., Things [18]).”