Stereopsis Improves Rapid Scene Categorization
Bibliographic record
Abstract
Theories of rapid scene categorization have emphasised the importance of computationally inexpensive image cues such as texture (Renninger & Malik, 2004) and spectral structure (Oliva & Torralba, 2001). Although binocular disparities provide reliable depth information, stereopsis is typically thought to be sluggish (Tyler, 1991; Kane, Guan & Banks, 2014; Valsecchi, Caziot, Backus, & Gegenfurtner, 2013), and therefore unhelpful during early scene processing. Recent work, however, suggests that stereopsis improves object recognition following brief (33 msec) presentations (Caziot & Backus, 2015). We investigated whether disparity information facilitates the categorization of briefly presented real-world scenes. Subjects viewed greyscale or colour images from the Southampton-York Natural Scenes dataset (Adams et al., 2016), presented monoscopically (same image to both eyes), stereoscopically, or in reversed stereo (inverted disparity-defined depth). Images were presented for 13.3, 26.6, 53.2, or 106.5 msecs, with backward masking. Subjects identified the semantic (e.g., beach / road / farm) or 3D spatial structure (e.g., open / closed off / navigable route) category, in addition to the viewing condition (mono / stereo / stereo-reversed). Disparity information facilitated semantic categorization, but only for grayscale images, reflecting redundancy across disparity and colour segmentation cues. Strikingly, the stereoscopic advantage emerged for 13.3 msec presentations, suggesting that disparity cues are encoded rapidly in complex scenes. Colour also facilitated semantic categorization, but only for presentations of 26.6 msecs or longer. Disparity and colour both improved 3D spatial structure categorization. Interestingly, however, observers were only able to distinguish the viewing condition of images presented for 53.2 msecs or longer – much later than the onset of the disparity advantage for categorisation. Our findings contradict previous claims that stereoscopic depth cues are extracted from real-world scenes after monocular cues (e.g., Valsecchi et al., 2013), and suggest that stereopsis improves the recognition of briefly presented scenes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".