Learning Categorical Shape from Captioned Images
Why this work is in the frame
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Bibliographic record
Abstract
Given a set of captioned images of cluttered scenes containing various objects in different positions and scales, we learn named contour models of object categories without relying on bounding box annotation. We extend a recent language-vision integration framework that finds spatial configurations of image features that co-occur with words in image captions. By substituting appearance features with local contour features, object categories are recognized by a contour model that grows along the object's boundary. Experiments on ETHZ are presented to show that 1) the extended framework is better able to learn named visual categories whose within-class variation is better captured by a shape model than an appearance model, and 2) typical object recognition methods fail when manually annotated bounding boxes are unavailable.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it