# Multi-Level Factorisation Net for Person Re-Identification

arXiv: https://arxiv.org/abs/1803.09132 · HTML: https://mlsanity.com/paper/1803.09132/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2018.

## Built on by (most cited first, 30 of 58)

- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [Magic Tokens: Select Diverse Tokens for Multi-modal Object Re-Identification](https://mlsanity.com/paper/2403.10254/) (2024)
- [Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification](https://mlsanity.com/paper/2308.10658/) (2023)
- [TOP-ReID: Multi-spectral Object Re-Identification with Token Permutation](https://mlsanity.com/paper/2312.09612/) (2023)
- [DeMo: Decoupled Feature-Based Mixture of Experts for Multi-Modal Object Re-Identification](https://mlsanity.com/paper/2412.10650/) (2024)
- [Dense Interaction Learning for Video-based Person Re-identification](https://mlsanity.com/paper/2103.09013/) (2021)
- [Towards Discriminative Representation Learning for Unsupervised Person Re-identification](https://mlsanity.com/paper/2108.03439/) (2021)
- [DeepChange: A Large Long-Term Person Re-Identification Benchmark with Clothes Change](https://mlsanity.com/paper/2105.14685/) (2021)
- [Quality-aware Part Models for Occluded Person Re-identification](https://mlsanity.com/paper/2201.00107/) (2022)
- [Wide-Baseline Multi-Camera Calibration using Person Re-Identification](https://mlsanity.com/paper/2104.08568/) (2021)
- [Graph-based Person Signature for Person Re-Identifications](https://mlsanity.com/paper/2104.06770/) (2021)
- [Pose-driven Attention-guided Image Generation for Person Re-Identification](https://mlsanity.com/paper/2104.13773/) (2021)
- [Reconcile Prediction Consistency for Balanced Object Detection](https://mlsanity.com/paper/2108.10809/) (2021)
- [Pseudo-Pair based Self-Similarity Learning for Unsupervised Person Re-identification](https://mlsanity.com/paper/2207.13035/) (2022)
- [Flare-Aware Cross-modal Enhancement Network for Multi-spectral Vehicle Re-identification](https://mlsanity.com/paper/2305.13659/) (2023)
- [Dynamic Enhancement Network for Partial Multi-modality Person Re-identification](https://mlsanity.com/paper/2305.15762/) (2023)
- [Holistic Guidance for Occluded Person Re-Identification](https://mlsanity.com/paper/2104.06524/) (2021)
- [Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification](https://mlsanity.com/paper/2203.15210/) (2022)
- [ICPL-ReID: Identity-Conditional Prompt Learning for Multi-Spectral Object Re-Identification](https://mlsanity.com/paper/2505.17821/) (2025)
- [MDReID: Modality-Decoupled Learning for Any-to-Any Multi-Modal Object Re-Identification](https://mlsanity.com/paper/2510.23301/) (2025)
- [The Group Loss++: A deeper look into group loss for deep metric learning](https://mlsanity.com/paper/2204.01509/) (2022)
- [Sampling Agnostic Feature Representation for Long-Term Person Re-identification](https://mlsanity.com/paper/2209.09574/) (2022)
- [NEXT: Multi-Grained Mixture of Experts via Text-Modulation for Multi-Modal Object Re-Identification](https://mlsanity.com/paper/2505.20001/) (2025)
- [Moving Towards Centers: Re-ranking with Attention and Memory for Re-identification](https://mlsanity.com/paper/2105.01447/) (2021)
- [Meta Generative Attack on Person Reidentification](https://mlsanity.com/paper/2301.06286/) (2023)
- [Improving CNN-based Person Re-identification using score Normalization](https://mlsanity.com/paper/2307.00397/) (2023)
- [Disentangled Representations for Short-Term and Long-Term Person Re-Identification](https://mlsanity.com/paper/2409.05277/) (2024)
- [HashReID: Dynamic Network with Binary Codes for Efficient Person Re-identification](https://mlsanity.com/paper/2308.11900/) (2023)
- [Reliable Multi-Modal Object Re-Identification via Modality-Aware Graph Reasoning](https://mlsanity.com/paper/2504.14847/) (2025)
- [One-Shot Knowledge Transfer for Scalable Person Re-Identification](https://mlsanity.com/paper/2511.06016/) (2025)

## Abstract (arXiv)

Key to effective person re-identification (Re-ID) is modelling discriminative and view-invariant factors of person appearance at both high and low semantic levels. Recently developed deep Re-ID models either learn a holistic single semantic level feature representation and/or require laborious human annotation of these factors as attributes. We propose Multi-Level Factorisation Net (MLFN), a novel network architecture that factorises the visual appearance of a person into latent discriminative factors at multiple semantic levels without manual annotation. MLFN is composed of multiple stacked blocks. Each block contains multiple factor modules to model latent factors at a specific level, and factor selection modules that dynamically select the factor modules to interpret the content of each input image. The outputs of the factor selection modules also provide a compact latent factor descriptor that is complementary to the conventional deeply learned features. MLFN achieves state-of-the-art results on three Re-ID datasets, as well as compelling results on the general object categorisation CIFAR-100 dataset.