Fusion-Mamba: A Novel Cross-Modality Feature Fusion Method for Robust Object Detection

Основні поняття

The proposed Fusion-Mamba method effectively fuses features from different modalities (RGB and infrared) by associating them in a hidden state space, reducing disparities between cross-modal features and enhancing the representation consistency of fused features.

Анотація

The paper presents a novel Fusion-Mamba method for cross-modality object detection. The key contributions are:

The Fusion-Mamba method explores the potential of Mamba for cross-modal fusion, building a hidden state space for cross-modality interaction to reduce disparities between cross-modal features and enhance the representation consistency of fused features.

The Fusion-Mamba block (FMB) is designed with two modules:

The State Space Channel Swapping (SSCS) module facilitates shallow feature fusion.
The Dual State Space Fusion (DSSF) module enables deep fusion in a hidden state space.

Extensive experiments on three public RGB-IR object detection datasets demonstrate that the proposed Fusion-Mamba method achieves state-of-the-art performance, offering a new baseline in cross-modal object detection.

The authors first extract local features from RGB and infrared images using convolutional blocks. Then, the FMB is applied to fuse the cross-modal features. The SSCS module exchanges channel features between modalities and applies a VSS block for shallow feature fusion. The DSSF module further builds a hidden state space for deep feature fusion using a gating mechanism to reduce modality disparities.
The enhanced features from the two modalities are added to generate the final fused features, which are then fed into the detection network's neck and head. The proposed method outperforms state-of-the-art cross-modality fusion methods on the LLVIP, M3FD, and FLIR-Aligned datasets, demonstrating superior object detection performance.

Статистика

The authors report the following key metrics:

On the LLVIP dataset, the proposed Fusion-Mamba method achieves 97.0% mAP50 and 64.3% mAP, outperforming the previous best method RSDet by 1.2% mAP50 and 3% mAP.
On the M3FD dataset, Fusion-Mamba achieves 88.0% mAP50 and 61.9% mAP, surpassing the previous best method SuperFusion by 4.5% mAP50 and 5.9% mAP.
On the FLIR-Aligned dataset, Fusion-Mamba achieves 84.9% mAP50 and 47.0% mAP, outperforming the previous best method CrossFormer by 5.6% mAP50 and 4.9% mAP.

Цитати

"The proposed Fusion-Mamba method explores the potential of Mamba for cross-modal fusion, which enhances the representation consistency of fused features."
"We design a Fusion-Mamba block with two modules: the State Space Channel Swapping (SSCS) module facilitates shallow feature fusion, and the Dual State Space Fusion (DSSF) module enables deep fusion in a hidden state space."
"Extensive experiments on three public RGB-IR object detection datasets demonstrate that our method achieves state-of-the-art performance, offering a new baseline in the cross-modal object detection method."

Ключові висновки, отримані з

Fusion-Mamba for Cross-modality Object Detection

by Wenhao Dong,... о arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09146.pdf

Глибші Запити

How can the Fusion-Mamba method be extended to other cross-modal tasks beyond object detection, such as image segmentation or activity recognition

The Fusion-Mamba method can be extended to other cross-modal tasks beyond object detection by adapting the fusion strategy and architecture to suit the specific requirements of tasks like image segmentation or activity recognition.
For image segmentation, the Fusion-Mamba method can be modified to incorporate spatial information more effectively by enhancing the interaction between different modalities at the pixel level. This can involve designing specialized modules within the Fusion-Mamba block to handle pixel-wise feature fusion and segmentation. By leveraging the hidden state space concept and gating mechanisms, the Fusion-Mamba method can facilitate the integration of complementary information from multiple modalities to improve segmentation accuracy and robustness.
In the case of activity recognition, the Fusion-Mamba method can be tailored to capture temporal dependencies and context across different modalities. By incorporating temporal modeling components and attention mechanisms, the Fusion-Mamba architecture can be adapted to fuse features from various modalities over time to enhance the recognition of complex activities. This can enable the model to learn rich representations that combine visual, spatial, and temporal cues for more accurate activity recognition.
Overall, by customizing the Fusion-Mamba method to address the specific requirements of image segmentation and activity recognition tasks, it can be extended to a broader range of cross-modal applications beyond object detection.

What are the potential limitations of the Mamba-based approach, and how can they be addressed in future research

One potential limitation of the Mamba-based approach is the scalability and complexity of modeling long-range dependencies in large-scale datasets. While Mamba offers linear-time sequence modeling capabilities, it may face challenges in handling extremely long sequences or high-dimensional data efficiently. To address this limitation, future research could focus on optimizing the Mamba architecture for scalability by exploring techniques such as hierarchical modeling, sparse attention mechanisms, or parallel processing to improve its performance on large datasets.
Another limitation could be the interpretability of the hidden state space in the Fusion-Mamba method. Understanding the representations learned in the hidden space and how they contribute to the fusion process may be challenging. Future research could investigate methods to enhance the interpretability of the hidden state space, such as visualization techniques or feature attribution methods, to provide insights into the fusion process and improve model transparency.
Additionally, the Fusion-Mamba method may face challenges in handling noisy or incomplete data from different modalities. Ensuring robustness to noisy inputs and developing mechanisms to handle missing or unreliable information during fusion could be areas for improvement in future research. Techniques like data augmentation, robust feature selection, or uncertainty estimation could be explored to address these challenges and enhance the reliability of the fusion process.

Can the Fusion-Mamba method be further improved by incorporating additional modalities beyond RGB and infrared, such as depth or radar data, to enhance the robustness and versatility of the cross-modal fusion

The Fusion-Mamba method can be further improved by incorporating additional modalities beyond RGB and infrared, such as depth or radar data, to enhance the robustness and versatility of cross-modal fusion. By integrating multiple modalities, the Fusion-Mamba architecture can capture a more comprehensive range of information, leading to more accurate and reliable fusion results.
To incorporate additional modalities, the Fusion-Mamba method can be extended by introducing new branches in the feature extraction network to handle the different modalities. Each modality-specific branch can feed into the Fusion-Mamba block, where the interactions and fusion processes can take place. By adapting the SSCS and DSSF modules to accommodate the new modalities, the Fusion-Mamba method can effectively handle the fusion of diverse data sources.
Furthermore, the Fusion-Mamba method can leverage the unique characteristics of each modality to enhance the overall fusion process. Depth data, for example, can provide spatial information about the distance of objects, while radar data can offer insights into object movement and velocity. By integrating these modalities intelligently within the Fusion-Mamba framework, the model can leverage the strengths of each modality to improve the overall performance of cross-modal tasks.