Bibliographic record
Abstract
Fine-grained image recognition is a problem in Computer Vision which focuses on discriminating between objects that appear similar. Two images of an object in this problem classification can appear vastly different while images from different classes can appear nearly identical. To solve this problem, one must determine regions of significance or salient regions of this image and determine the classification from these regions. Current approaches take the approach of a hard extraction of these regions or some small deviation off hard extraction. Furthermore, in most cases, these regions are used without the spatial context of the region positioning in the image, using an approach similar to the bag-of-words model found in natural language processing. The approach described in this paper will abandon salient region proposals, electing instead to decompose the image into a series of subsets. Each of these subsets will undergo the same feature extraction process, carried out by a series of convolution and pooling layers. The output of this process will be used as the input to a recurrent neural network, ultimately classify the initial image. In processing the image in this fashion, each of these subsets’ salience in the context of the larger classification will be determined. A standardized implementation of this architecture has not yet been completed. As such, results indicative of performance can not currently be determined.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".