Double Encoding - Slow Decoding Image to Image CNN for Foreground Identification with Application Towards Intelligent Transportation
Bibliographic record
Abstract
The vision-based foreground (FG) identification approaches are in great demand to intelligent transportation applications. Here, the convolutional neural network (CNN)-based image-to-image models have shown impressive performance in saliency detection and semantic segmentation. Inspired by their advancement in computer vision (CV), we introduce a strategy, named double encoding - slow decoding to improve a basic encoder-decoder (EnDec) CNN for precise FG masking. Wherein, at every stage of down-sampling, a feature map is encoded twice and every stage of up-sampling is enhanced by two residual feature concatenations (cat) and interspersed batch normalization (BN). Such continuous forward feature pulling process in the encoding and decoding sub-networks results delineated FG identification. We carry out a thorough analysis to validate the effectiveness of the proposed model in terms of figure of merit (f-measure). We also investigate four different methods of FG binary mask creation from the probability saliency map generated by the model. The experimental study on benchmark datasets proves that the proposed architecture achieves better/competitive results than/against state-of-the-art methods.
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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 | 0.001 |
| 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".