A deep learning approach for automatically generating descriptions of images containing people
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
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| 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