# On the Effectiveness of Heterogeneous Ensemble Methods for Re-identification

arXiv: https://arxiv.org/abs/2403.12606 · HTML: https://mlsanity.com/paper/2403.12606/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 52 references that are in the map.
- First posted on arXiv 19 Mar 2024.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [FLAML: A Fast and Lightweight AutoML Library](https://mlsanity.com/paper/1911.04706/) (2019)
- [A survey of advances in vision-based vehicle re-identification](https://mlsanity.com/paper/1905.13258/) (2019)
- [When does Diversity Help Generalization in Classification Ensembles?](https://mlsanity.com/paper/1910.13631/) (2019)
- [Cross-view Asymmetric Metric Learning for Unsupervised Person Re-identification](https://mlsanity.com/paper/1708.08062/) (2017)
- [Deep learning-based person re-identification methods: A survey and outlook of recent works](https://mlsanity.com/paper/2110.04764/) (2021)
- [Learning to rank in person re-identification with metric ensembles](https://mlsanity.com/paper/1503.01543/) (2015)
- [In Defense of the Classification Loss for Person Re-Identification](https://mlsanity.com/paper/1809.05864/) (2018)
- [Deep Learning for Video-based Person Re-Identification: A Survey](https://mlsanity.com/paper/2303.11332/) (2023)
- [End-to-End Training of CNN Ensembles for Person Re-Identification](https://mlsanity.com/paper/2010.01342/) (2020)
- [On the Applicability of Synthetic Data for Re-Identification](https://mlsanity.com/paper/2212.10105/) (2022)
- [Ensemble Feature for Person Re-Identification](https://mlsanity.com/paper/1901.05798/) (2019)

## Abstract (arXiv)

In this contribution, we introduce a novel ensemble method for the re-identification of industrial entities, using images of chipwood pallets and galvanized metal plates as dataset examples. Our algorithms replace commonly used, complex siamese neural networks with an ensemble of simplified, rudimentary models, providing wider applicability, especially in hardware-restricted scenarios. Each ensemble sub-model uses different types of extracted features of the given data as its input, allowing for the creation of effective ensembles in a fraction of the training duration needed for more complex state-of-the-art models. We reach state-of-the-art performance at our task, with a Rank-1 accuracy of over 77% and a Rank-10 accuracy of over 99%, and introduce five distinct feature extraction approaches, and study their combination using different ensemble methods.