# MMLF: Multi-modal Multi-class Late Fusion for Object Detection with Uncertainty Estimation Source: [MMLF: Multi-modal Multi-class Late Fusion for Object Detection with Uncertainty Estimation]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:29:07.731Z Authors: Qihang Yang, Yang Zhao, Hong Cheng Published: 2024-10-11T11:58:35.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()