# Multimodal Object Detection via Probabilistic a priori Information Integration

Source: [Multimodal Object Detection via Probabilistic a priori Information Integration](<https://www.sota2.com/research/paper/68fa73511e209131cea5bd00>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:47:16.604Z

Authors: Hafsa El Hafyani, Bastien Pasdeloup, Camille Yver, Pierre Romenteau

Published: 2024-05-24T14:28:06.000Z

## Abstract

Multimodal object detection has shown promise in remote sensing. However, multimodal data frequently encounter the problem of low-quality, wherein the modalities lack strict cell-to-cell alignment, leading to mismatch between different modalities. In this paper, we investigate multimodal object detection where only one modality contains the target object and the others provide crucial contextual information. We propose to resolve the alignment problem by converting the contextual binary information into probability maps. We then propose an early fusion architecture that we validate with extensive experiments on the DOTA dataset.

## 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](<https://arxiv.org/abs/2405.15596>)

[PDF](<https://arxiv.org/pdf/2405.15596.pdf>)