# MMLF: Multi-modal Multi-class Late Fusion for Object Detection with Uncertainty Estimation

arXiv: https://arxiv.org/abs/2410.08739 · HTML: https://mlsanity.com/paper/2410.08739/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [PointPainting: Sequential Fusion for 3D Object Detection](https://mlsanity.com/paper/1911.10150/) (2019)
- [Variational Dropout Sparsifies Deep Neural Networks](https://mlsanity.com/paper/1701.05369/) (2017)
- [EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection](https://mlsanity.com/paper/2007.08856/) (2020)
- [Automatic Differentiation Variational Inference](https://mlsanity.com/paper/1603.00788/) (2016)
- [CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection](https://mlsanity.com/paper/2009.00784/) (2020)
- [Bounding Box Regression with Uncertainty for Accurate Object Detection](https://mlsanity.com/paper/1809.08545/) (2018)
- [Trusted Multi-View Classification](https://mlsanity.com/paper/2102.02051/) (2021)
- [Multi-modal Sensor Fusion for Auto Driving Perception: A Survey](https://mlsanity.com/paper/2202.02703/) (2022)
- [Complex-YOLO: Real-time 3D Object Detection on Point Clouds](https://mlsanity.com/paper/1803.06199/) (2018)
- [Uncertainty Estimation in One-Stage Object Detection](https://mlsanity.com/paper/1905.10296/) (2019)
- [Fully Sparse Fusion for 3D Object Detection](https://mlsanity.com/paper/2304.12310/) (2023)

## Built on by (most cited first, 3 of 3)

- [Explainable Deep Neural Network for Multimodal ECG Signals: Intermediate vs Late Fusion](https://mlsanity.com/paper/2508.11666/) (2025)
- [Token-Efficient Multimodal Reasoning via Image Prompt Packaging](https://mlsanity.com/paper/2604.02492/) (2026)
- [Context-aware Bayesian Fusion for Multimodal Object Detection](https://mlsanity.com/paper/2610.18741/) (2026)

## Abstract (arXiv)

Autonomous driving necessitates advanced object detection techniques that integrate information from multiple modalities to overcome the limitations associated with single-modal approaches. The challenges of aligning diverse data in early fusion and the complexities, along with overfitting issues introduced by deep fusion, underscore the efficacy of late fusion at the decision level. Late fusion ensures seamless integration without altering the original detector's network structure. This paper introduces a pioneering Multi-modal Multi-class Late Fusion method, designed for late fusion to enable multi-class detection. Fusion experiments conducted on the KITTI validation and official test datasets illustrate substantial performance improvements, presenting our model as a versatile solution for multi-modal object detection in autonomous driving. Moreover, our approach incorporates uncertainty analysis into the classification fusion process, rendering our model more transparent and trustworthy and providing more reliable insights into category predictions.