# Learning to Detect Important People in Unlabelled Images for Semi-supervised Important People Detection

arXiv: https://arxiv.org/abs/2004.07568 · HTML: https://mlsanity.com/paper/2004.07568/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [Refining Pseudo Labels with Clustering Consensus over Generations for Unsupervised Object Re-identification](https://mlsanity.com/paper/2106.06133/) (2021)
- [Important Object Identification with Semi-Supervised Learning for Autonomous Driving](https://mlsanity.com/paper/2203.02634/) (2022)
- [Semi-supervised 3D Object Detection via Temporal Graph Neural Networks](https://mlsanity.com/paper/2202.00182/) (2022)
- [Most Important Person-guided Dual-branch Cross-Patch Attention for Group Affect Recognition](https://mlsanity.com/paper/2212.07055/) (2022)
- [MIP-GAF: A MLLM-annotated Benchmark for Most Important Person Localization and Group Context Understanding](https://mlsanity.com/paper/2409.06224/) (2024)
- [Automatic Main Character Recognition for Photographic Studies](https://mlsanity.com/paper/2106.09064/) (2021)
- [On-Road Object Importance Estimation: A New Dataset and A Model with Multi-Fold Top-Down Guidance](https://mlsanity.com/paper/2411.17152/) (2024)
- [Mining Multi-Modality Spatio-Temporal Cues for Video Important Person Identification](https://mlsanity.com/paper/2605.28604/) (2026)

## Abstract (arXiv)

Important people detection is to automatically detect the individuals who play the most important roles in a social event image, which requires the designed model to understand a high-level pattern. However, existing methods rely heavily on supervised learning using large quantities of annotated image samples, which are more costly to collect for important people detection than for individual entity recognition (eg, object recognition). To overcome this problem, we propose learning important people detection on partially annotated images. Our approach iteratively learns to assign pseudo-labels to individuals in un-annotated images and learns to update the important people detection model based on data with both labels and pseudo-labels. To alleviate the pseudo-labelling imbalance problem, we introduce a ranking strategy for pseudo-label estimation, and also introduce two weighting strategies: one for weighting the confidence that individuals are important people to strengthen the learning on important people and the other for neglecting noisy unlabelled images (ie, images without any important people). We have collected two large-scale datasets for evaluation. The extensive experimental results clearly confirm the efficacy of our method attained by leveraging unlabelled images for improving the performance of important people detection.