Towards a framework for learning structured shape models from text-annotated images
Bibliographic record
Abstract
We present on-going work on the topic of learning translation models between image data and text (English) captions. Most approaches to this problem assume a one-to-one or a flat, one-to-many mapping between a segmented image region and a word. However, this assumption is very restrictive from the computer vision standpoint, and fails to account for two important properties of image segmentation: 1) objects often consist of multiple parts, each captured by an individual region; and 2) individual regions are often over-segmented into multiple subregions. Moreover, this assumption also fails to capture the structural relations among words, e.g., part/whole relations. We outline a general framework that accommodates a many-to-many mapping between image regions and words, allowing for structured descriptions on both sides. In this paper, we describe our extensions to the probabilistic translation model of Brown et al. (1993) (as in Duygulu et al. (2002)) that enable the creation of structured models of image objects. We demonstrate our work in progress, in which a set of annotated images is used to derive a set of labeled, structured descriptions in the presence of oversegmentation.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".