Interactive Masked Image Modeling for Multimodal Object Detection in Remote Sensing
This work addresses object detection for remote sensing applications, offering an incremental improvement over existing methods by enhancing multimodal learning with better token interactions.
The paper tackles the challenge of object detection in remote sensing imagery, where small objects are hard to detect, by proposing an interactive Masked Image Modeling method that improves detection accuracy through self-supervised pre-training on unlabeled data.
Object detection in remote sensing imagery plays a vital role in various Earth observation applications. However, unlike object detection in natural scene images, this task is particularly challenging due to the abundance of small, often barely visible objects across diverse terrains. To address these challenges, multimodal learning can be used to integrate features from different data modalities, thereby improving detection accuracy. Nonetheless, the performance of multimodal learning is often constrained by the limited size of labeled datasets. In this paper, we propose to use Masked Image Modeling (MIM) as a pre-training technique, leveraging self-supervised learning on unlabeled data to enhance detection performance. However, conventional MIM such as MAE which uses masked tokens without any contextual information, struggles to capture the fine-grained details due to a lack of interactions with other parts of image. To address this, we propose a new interactive MIM method that can establish interactions between different tokens, which is particularly beneficial for object detection in remote sensing. The extensive ablation studies and evluation demonstrate the effectiveness of our approach.