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Found 4 papers, 1 papers with code
DRUformer: Enhancing the driving scene Important object detection with driving relationship self-understanding
The DRUformer is a transformer-based multi-modal important object detection model that takes into account the relationships between all the participants in the driving scenario.
R-Cut: Enhancing Explainability in Vision Transformers with Relationship Weighted Out and Cut
Our method aims to improve trust in classification results and empower users to gain a deeper understanding of the model for downstream tasks by providing visualizations of class-specific maps.
Learning to Generalize for Cross-domain QA
Our method has been theoretically and empirically shown to be effective in enhancing the generalization ability of both generative and discriminative models.
TsFeX: Contact Tracing Model using Time Series Feature Extraction and Gradient Boosting
With the outbreak of COVID-19 pandemic, a dire need to effectively identify the individuals who may have come in close-contact to others who have been infected with COVID-19 has risen.
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