AdaVIT: an Adaptive Visual-Tabular Fusion Multi-modal for road surface snow detection
Bibliographic record
Abstract
Snow detection on road surfaces, especially in bare pavement lost and regain periods, is crucial for traffic safety. Traditional methods like manual observation and computer vision, encounter difficulties under complex weather and lighting conditions, with factors like snowfall intensity, temperature fluctuations, and road surfaces complicating detection. To address these limitations, we propose AdaVIT (Adaptive Visual-Tabular Fusion Multi-modal), an innovative framework for road surface snow detection that integrates real-time monitoring images with environmental data. It employs ConvNeXt for image recognition and an enhanced random forest for analysis environmental data. A feature-level multi-modal fusion strategy is designed to integrate environmental data into the image recognition process, while an adaptive feature fusion mechanism ensures an intelligent balance between different data types. Experimental results demonstrate that AdaVIT delivers optimal performance, with significant improvements in both robustness and accuracy, particularly as the dataset size increases. An ablation study further confirms the effectiveness of the proposed adaptive fusion mechanism.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".