CSA-CNN: A Contrastive Self-Attention Neural Network for Pupil Segmentation in Eye Gaze Tracking
Bibliographic record
Abstract
This paper presents a novel Contrastive Self-Attention Convolutional Neural Network (CSA-CNN) model with enhanced Difficulty Aware (DA) loss function to improve the segmentation of pupils in eye images. The incorporation of transformer-style self-attention and Difficulty-Aware loss in a UNET-style architecture allows for robust feature representation and promotes shape alignment. The novel model was trained on two public databases (LPW and RIT-Eyes) and evaluated on two other public datasets (ExCuSe and ElSe). When compared with seven state-of-the-art pupil center detection methods, the CSA-CNN showed improvement of over 6% in pupil center detection accuracy (detection within 5 pixels of the labeled center) and more than 9% in Intersection Over Union (IOU) accuracy, compared to the best performer among the other seven methods. Furthermore, when the CSA-CNN model was integrated into a glint-based eye tracking system that uses learning based methods to detect pupil-center, we saw a 25% improvement in gaze accuracy.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".