Learning structured prediction models for image labeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many fundamental tasks in computational vision can be formulated as predicting unknown properties of a scene from a static image. If the scene property is described by a set of discrete values in each image, then the corresponding vision task is an image labeling problem. A key issue in image labeling concerns how to exploit the context information in images, as local evidence is often insufficient to determine the label value. This thesis takes a statistical learning approach to the labeling problem, focusing on two main issues in incorporating context into the labeling process: 1) what are the efficient representations of contexts for labeling? and 2) how do we learn the context representations for a labeling task from data? We discuss two learning situations based on different degrees of data availability. In the first case, enough fully-labeled data are available for learning. So we develop a discriminative labeling framework based on a Conditional Random Field (CRF), in which multiscale feature functions are proposed to capture the image/label contexts at several spatial scales. Those feature functions affect the labeling from local to global levels: some aspects of the contexts concern co-occurrence of objects in the image, while other aspects concern the geometric relationships between objects. To extend the range of object classes and image database size that
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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