The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
What do we perceive in a glance of a real-world scene?
Open accessAbstract
What do we see when we glance at a natural scene and how does it change as the glance becomes longer? We asked naive subjects to report in a free-form format what they saw when looking at briefly presented real-life photographs. Our subjects received no specific information as to the content of each stimulus. Thus, our paradigm differs from previous studies where subjects were cued before a picture was presented and/or were probed with multiple-choice questions. In the first stage, 90 novel grayscale photographs were foveally shown to a group of 22 native-English-speaking subjects. The presentation time was chosen at random from a set of seven possible times (from 27 to 500 ms). A perceptual mask followed each photograph immediately. After each presentation, subjects reported what they had just seen as completely and truthfully as possible. In the second stage, another group of naive individuals was instructed to score each of the descriptions produced by the subjects in the first stage. Individual scores were assigned to more than a hundred different attributes. We show that within a single glance, much object- and scene-level information is perceived by human subjects. The richness of our perception, though, seems asymmetrical. Subjects tend to have a propensity toward perceiving natural scenes as being outdoor rather than indoor. The reporting of sensory- or feature-level information of a scene (such as shading and shape) consistently precedes the reporting of the semantic-level information. But once subjects recognize more semantic-level components of a scene, there is little evidence suggesting any bias toward either scene-level or object-level recognition.
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Cites
The fusiform face area: a module in human extrastriate cortex specialized for face perception
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A cortical area selective for visual processing of the human body
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Changing faces: a detection advantage in the flicker paradigm
Ro, T., Russell, C., & Lavie, N. (2001) · Psychological Science · doi:10.1111/1467-9280.00317 · Cited by 348
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Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902
Self-citationOn the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676
Self-citation80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
Torralba, A., Fergus, R., & Freeman, W. T. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.128 · Cited by 1,921
Deep Visual-Semantic Alignments for Generating Image Descriptions
Karpathy, A., & Fei-Fei, L. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2598339 · Cited by 1,048
Self-citationGREENE, M., & OLIVA, A. (2009) · Cognitive Psychology · doi:10.1016/j.cogpsych.2008.06.001 · Cited by 467
Metadata
- Authors
- Fei-Fei, L., Iyer, A., Koch, C., & Perona, P.
- Venue
- Journal of Vision
- Published
- 2007-01-31
- Volume
- 7
- Issue
- 1
- Pages
- 10
- ISSN
- 1534-7362 (ISSN-L)
- OpenAlex
- W2056621158
- Cited by
- 463
- References
- 5
- FWCI
- 8.20
- Type
- journal article
- Language
- en
- Open access
- gold
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