CFN: A coarse‐to‐fine network for eye fixation prediction
Bibliographic record
Abstract
Abstract Many image‐to‐image computer vision approaches have made great progress by an end‐to‐end framework with the encoder–decoder architecture. However, the same image‐to‐image eye fixation prediction task is not the same as those computer vision tasks in that it focuses more on salient regions rather than precise predictions for every pixel. Thus, it is not appropriate to directly apply the end‐to‐end encoder–decoder to the eye fixation prediction task. In addition, although high‐level feature is important, the contribution of low‐level feature should also be kept and balanced in computational model. Nevertheless, some low‐level features that attract attention are easily neglected while transiting through the deep network. Therefore, the effective way to integrate low‐level and high‐level features for improving eye fixation prediction performance is still a challenging task. In this paper, a coarse‐to‐fine network (CFN) that encompasses two pathways with different training strategies are proposed: coarse perceiving network (CFN‐Coarse) can be a simple encoder network or any of the existing pretrained network to capture the distribution of salient regions and generate high‐quality feature maps; fine integrating network (CFN‐Fine) uses fixed parameters from the CFN‐Coarse and combines features from deep to shallow in the deconvolution process by adding skip connections between down‐sampling and up‐sampling paths to efficiently integrate deep and shallow features. The saliency map obtained by the method is evaluated over 6 standard benchmark datasets, namely SALICON, MIT1003, MIT300, Toronto, OSIE, and SUN500. The results demonstrate that the method can surpass the state‐of‐the‐art accuracy of eye fixation prediction and achieves the competitive performance to date under most evaluation metrics on SALICON Saliency Prediction Challenge (LSUN2017).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".