# Label-Aware Ranked Loss for robust People Counting using Automotive in-cabin Radar

arXiv: https://arxiv.org/abs/2110.05876 · HTML: https://mlsanity.com/paper/2110.05876/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting](https://mlsanity.com/paper/1908.03716/) (2019)
- [Object Detection and 3D Estimation via an FMCW Radar Using a Fully Convolutional Network](https://mlsanity.com/paper/1902.05394/) (2019)
- [Attention to Head Locations for Crowd Counting](https://mlsanity.com/paper/1806.10287/) (2018)
- [Constellation Loss: Improving the efficiency of deep metric learning loss functions for optimal embedding](https://mlsanity.com/paper/1905.10675/) (2019)

## Built on by (most cited first, 7 of 7)

- [MCROOD: Multi-Class Radar Out-Of-Distribution Detection](https://mlsanity.com/paper/2303.06232/) (2023)
- [Utilizing Explainable AI for improving the Performance of Neural Networks](https://mlsanity.com/paper/2210.04686/) (2022)
- [HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar](https://mlsanity.com/paper/2308.02396/) (2023)
- [Uncertainty-based Meta-Reinforcement Learning for Robust Radar Tracking](https://mlsanity.com/paper/2210.14532/) (2022)
- [Temporal Patience: Efficient Adaptive Deep Learning for Embedded Radar Data Processing](https://mlsanity.com/paper/2309.05686/) (2023)
- [Cross-modal Learning of Graph Representations using Radar Point Cloud for Long-Range Gesture Recognition](https://mlsanity.com/paper/2203.17066/) (2022)
- [Feature Identification for Hierarchical Contrastive Learning](https://mlsanity.com/paper/2510.00837/) (2025)

## Abstract (arXiv)

In this paper, we introduce the Label-Aware Ranked loss, a novel metric loss function. Compared to the state-of-the-art Deep Metric Learning losses, this function takes advantage of the ranked ordering of the labels in regression problems. To this end, we first show that the loss minimises when datapoints of different labels are ranked and laid at uniform angles between each other in the embedding space. Then, to measure its performance, we apply the proposed loss on a regression task of people counting with a short-range radar in a challenging scenario, namely a vehicle cabin. The introduced approach improves the accuracy as well as the neighboring labels accuracy up to 83.0% and 99.9%: An increase of 6.7%and 2.1% on state-of-the-art methods, respectively.