A deep learning approach for automatically generating descriptions of images containing people
Bibliographic record
Abstract
Generating image descriptions is a challenging Artificial Intelligence problem with many interesting applications such as robots’ communication or helping visually impaired people. However, it is a complex task for computers: it requires Computer Vision algorithms, to understand what the image depicts, and Natural Language Processing algorithms, to generate a well-formed sentence. Nowadays, deep neural networks are the state-of-the-art in these two Artificial Intelligence fields. \nFurthermore, we believe that images that contain people are described in a slightly different manner and that restricting an image description generator model to these images may produce better descriptions. Therefore, the main objective of this project is to develop a Deep Learning model that automatically produces descriptions of images containing people and to conclude if it is a good practice the restriction to this kind of images. For this purpose, we have reviewed and studied the literature in the field and we have built, trained and compared four different models using Deep Learning techniques and a GPU to speed-up the computation, as well as a big and complete dataset.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".