Multimodal Object Detection via Probabilistic a priori Information Integration
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911886229897216 |
|---|---|
| author | Hafyani, Hafsa El Pasdeloup, Bastien Yver, Camille Romenteau, Pierre |
| author_facet | Hafyani, Hafsa El Pasdeloup, Bastien Yver, Camille Romenteau, Pierre |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15596 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multimodal Object Detection via Probabilistic a priori Information Integration Hafyani, Hafsa El Pasdeloup, Bastien Yver, Camille Romenteau, Pierre Computer Vision and Pattern Recognition 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. |
| title | Multimodal Object Detection via Probabilistic a priori Information Integration |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.15596 |