An Attempt at Defining And Quantifying Image Describability Through Semantic Connection Between Visual and Language
Why this work is in the frame
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Bibliographic record
Abstract
<p>One of the most challenging tasks of modern artificial intelligence systems is image captioning, the task requiring a machine to adequately comprehend the semantic content of visual data and correctly map it to a description within the language domain. Generally, to achieve acceptable performance, a learning system is presented with human-generated ground truth captions as a target to aim for. While significant progress has been achieved in creating highly functional image captioning systems, not much research has been focused on exploring the nature of the ground truth itself. In this thesis, such ground truth captions are analyzed in an attempt to find the semantic connection between visual data and associated language data describing it, revealing potential insights on human judgement and getting closer to defining and quantifying an abstract notion of image “describability”; the extent to which an image can be adequately described using language.</p>
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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