2D Cyclist Detection
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Benchmarks
Most implemented
Zephyr: Direct Distillation of LM Alignment
End-to-End Learning for Simultaneously Generating Decision Map and Multi-Focus Image Fusion Result
Py-Feat: Python Facial Expression Analysis Toolbox
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
Leolani: a reference machine with a theory of mind for social communication
Papers
Zephyr: Direct Distillation of LM Alignment
We aim to produce a smaller language model that is aligned to user intent. Previous research has shown that applying distilled supervised fine-tuning (dSFT) on larger models significantly improves task accuracy; however,…
2D Cyclist DetectionFew-Shot LearningLanguage ModelingLanguage Modelling+1Monocular Cyclist Detection with Convolutional Neural Networks
Cycling is an increasingly popular method of transportation for sustainability and health benefits. However, cyclists face growing risks, especially when encountering large vehicles on the road. This study aims to reduce…
2D Cyclist Detectionobject-detectionObject DetectionTransfer LearningPy-Feat: Python Facial Expression Analysis Toolbox
Studying facial expressions is a notoriously difficult endeavor. Recent advances in the field of affective computing have yielded impressive progress in automatically detecting facial expressions from pictures and videos…
2D Cyclist DetectionEnd-to-End Learning for Simultaneously Generating Decision Map and Multi-Focus Image Fusion Result
The general aim of multi-focus image fusion is to gather focused regions of different images to generate a unique all-in-focus fused image. Deep learning based methods become the mainstream of image fusion by virtue of i…
2D Cyclist DetectionDecoderMulti Focus Image FusionOn the safety of vulnerable road users by cyclist orientation detection using Deep Learning
In this work, orientation detection using Deep Learning is acknowledged for a particularly vulnerable class of road users,the cyclists. Knowing the cyclists' orientation is of great relevance since it provides a good not…
2D Cyclist Detectionobject-detectionObject DetectionTransfer LearningOptimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
We show that many machine learning goals, such as improved fairness metrics, can be expressed as constraints on the model's predictions, which we call rate constraints. We study the problem of training non-convex models …
2D Cyclist DetectionFairness