Multimodal Object Detection via Probabilistic a priori Information Integration

• URL: http://arxiv.org/abs/2405.15596v1
• Date: Fri, 24 May 2024 14:28:06 GMT
• Title: Multimodal Object Detection via Probabilistic a priori Information Integration
• Authors: Hafsa El Hafyani, Bastien Pasdeloup, Camille Yver, Pierre Romenteau,
• Abstract summary: Multimodal object detection has shown promise in remote sensing. In this paper, we investigate multimodal object detection where only one modality contains the target object. We propose to resolve the alignment problem by converting the contextual binary information into probability maps.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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