Attention-Based Accurate and Robust Facial Landmark Detector
Bibliographic record
Abstract
Recently, most of the existing face landmark de-tection algorithms focus on improving the performance and the efficiency of the model. However, due to the scaling and weighting effects as well as the existence of maximum pooling, the traditional convolutional neural network-based face alignment algorithms usually discard position and direction information implicit in the face image, thus decreasing its ability to capture the spatial position relationship between features, and resulting in significant performance degradation when partial occlusion occurs. To overcome this problem, in this paper, we propose an attention-based accurate, and robust face landmark detector called SCPNet. First, a SEBottleneck is used as the proposed detector's foundation module to explicitly describe channel in-terdependencies by adaptively recalibrating feature responses in terms of channels. The attention maps are then inferred sequentially along two independent dimensions (channel and space) by embedding a convolutional block attention module (CBAM) at the backbone network's tail, and the attention maps are multiplied by the input feature maps for adaptive feature refinement. Experimental results on WFLW, COFW, and 300W datasets show that our SCPNet outperforms current mainstream face alignment algorithms.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".