# Multispectral Pedestrian Detection with Sparsely Annotated Label

arXiv: https://arxiv.org/abs/2501.02640 · HTML: https://mlsanity.com/paper/2501.02640/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 5 Jan 2025.

## Builds on (most cited first, 14 of 14)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [LLVIP: A Visible-infrared Paired Dataset for Low-light Vision](https://mlsanity.com/paper/2108.10831/) (2021)
- [Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems](https://mlsanity.com/paper/2008.03043/) (2020)
- [Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection](https://mlsanity.com/paper/1803.05347/) (2018)
- [Multimodal Object Detection via Probabilistic Ensembling](https://mlsanity.com/paper/2104.02904/) (2021)
- [Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection](https://mlsanity.com/paper/1802.09972/) (2018)
- [Retrieval-Augmented Transformer for Image Captioning](https://mlsanity.com/paper/2207.13162/) (2022)
- [Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection](https://mlsanity.com/paper/2012.01950/) (2020)
- [Solving Missing-Annotation Object Detection with Background Recalibration Loss](https://mlsanity.com/paper/2002.05274/) (2020)
- [Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects](https://mlsanity.com/paper/1811.10862/) (2018)
- [SparseDet: Improving Sparsely Annotated Object Detection with Pseudo-positive Mining](https://mlsanity.com/paper/2201.04620/) (2022)
- [Calibrated Teacher for Sparsely Annotated Object Detection](https://mlsanity.com/paper/2303.07582/) (2023)

## Built on by (most cited first, 5 of 5)

- [MonoSAOD: Monocular 3D Object Detection with Sparsely Annotated Label](https://mlsanity.com/paper/2604.01646/) (2026)
- [LTV-YOLO: A Lightweight Thermal Object Detector for Young Pedestrians in Adverse Conditions](https://mlsanity.com/paper/2601.11662/) (2026)
- [Towards Sparsely Annotated Open-World Object Detection](https://mlsanity.com/paper/2608.12714/) (2026)
- [PersonaDrive: Controllable Trajectory Prediction with Multi-Dimensional Driving Personas](https://mlsanity.com/paper/2608.15230/) (2026)
- [Aligned Consensus Teaching for Label-Efficient Oriented Object Detection in Weakly-Aligned Visible-Infrared Imagery](https://mlsanity.com/paper/2609.18124/) (2026)

## Abstract (arXiv)

Although existing Sparsely Annotated Object Detection (SAOD) approches have made progress in handling sparsely annotated environments in multispectral domain, where only some pedestrians are annotated, they still have the following limitations: (i) they lack considerations for improving the quality of pseudo-labels for missing annotations, and (ii) they rely on fixed ground truth annotations, which leads to learning only a limited range of pedestrian visual appearances in the multispectral domain. To address these issues, we propose a novel framework called Sparsely Annotated Multispectral Pedestrian Detection (SAMPD). For limitation (i), we introduce Multispectral Pedestrian-aware Adaptive Weight (MPAW) and Positive Pseudo-label Enhancement (PPE) module. Utilizing multispectral knowledge, these modules ensure the generation of high-quality pseudo-labels and enable effective learning by increasing weights for high-quality pseudo-labels based on modality characteristics. To address limitation (ii), we propose an Adaptive Pedestrian Retrieval Augmentation (APRA) module, which adaptively incorporates pedestrian patches from ground-truth and dynamically integrates high-quality pseudo-labels with the ground-truth, facilitating a more diverse learning pool of pedestrians. Extensive experimental results demonstrate that our SAMPD significantly enhances performance in sparsely annotated environments within the multispectral domain.