Segment Anything with Multiple Modalities (2408.09085v1)

Abstract: Robust and accurate segmentation of scenes has become one core functionality in various visual recognition and navigation tasks. This has inspired the recent development of Segment Anything Model (SAM), a foundation model for general mask segmentation. However, SAM is largely tailored for single-modal RGB images, limiting its applicability to multi-modal data captured with widely-adopted sensor suites, such as LiDAR plus RGB, depth plus RGB, thermal plus RGB, etc. We develop MM-SAM, an extension and expansion of SAM that supports cross-modal and multi-modal processing for robust and enhanced segmentation with different sensor suites. MM-SAM features two key designs, namely, unsupervised cross-modal transfer and weakly-supervised multi-modal fusion, enabling label-efficient and parameter-efficient adaptation toward various sensor modalities. It addresses three main challenges: 1) adaptation toward diverse non-RGB sensors for single-modal processing, 2) synergistic processing of multi-modal data via sensor fusion, and 3) mask-free training for different downstream tasks. Extensive experiments show that MM-SAM consistently outperforms SAM by large margins, demonstrating its effectiveness and robustness across various sensors and data modalities.

• The paper introduces MM-SAM, extending the SAM framework to multi-modal sensor data through unsupervised cross-modal transfer and weakly-supervised fusion.
• The methodology integrates LiDAR, thermal, depth, and other sensors via a selective fusion gate, significantly enhancing segmentation accuracy on benchmark datasets.
• The results demonstrate robust performance in complex environments, underlining MM-SAM’s potential for applications in autonomous driving and remote sensing.

Segment Anything with Multiple Modalities: An Analytical Overview

The paper "Segment Anything with Multiple Modalities" by Xiao et al., introduces MM-SAM, an innovative extension of the Segment Anything Model (SAM) specifically designed to address the limitations of handling single-modal RGB images in segmentation tasks. The proposed framework expands SAM's capabilities to process multi-modal data from various sensor suites, such as LiDAR, depth, and thermal sensors, enabling robust segmentation in diverse and dynamic environments.

Introduction and Motivation

Visual scene segmentation is a critical component in numerous applications such as autonomous driving, robotics, and remote sensing. Traditional segmentation models, including the recent SAM, have demonstrated state-of-the-art performance using RGB images. However, today's sensing technology often combines multiple sensors, capturing complementary data from different modalities, which RGB-only models cannot fully exploit. Hence, there is a need for segmentation models that can effectively integrate and process multi-modal sensor data to improve segmentation accuracy and robustness in real-world scenarios.

Methodology

The proposed MM-SAM enables SAM to process cross-modal and multi-modal sensor data through two primary mechanisms: Unsupervised Cross-Modal Transfer (UCMT) and Weakly-Supervised Multi-Modal Fusion (WMMF).

1. Unsupervised Cross-Modal Transfer (UCMT):

UCMT facilitates adaptation to diverse non-RGB sensors by leveraging a lightweight module in SAM's image encoder for modality-specific patch embedding and parameter-efficient tuning structures like LoRA. Through an embedding unification loss, UCMT aligns the embeddings of different modalities, ensuring compatibility with SAM's original pipeline. This approach allows MM-SAM to efficiently handle individual sensor modalities without extensive re-training or additional supervision.

2. Weakly-Supervised Multi-Modal Fusion (WMMF):

WMMF enhances synergistic processing of multi-modal data by introducing a Selective Fusion Gate (SFG) that adaptively fuses embeddings from multiple sensors to generate a comprehensive representation. This fusion is achieved with pseudo-labeling that uses geometric prompts, eliminating the need for extensive mask annotations. The SFG dynamically adjusts the weights assigned to different sensor modalities based on the input data, optimizing the final segmentation outcome.

Experimental Results

The authors evaluated MM-SAM on seven distinct datasets comprising both time-synchronized and time-asynchronous sensor suites, covering a wide range of modalities such as thermal, depth, LiDAR, HSI, MS-LiDAR, SAR, and DSM.

1. Time-Synchronized Sensor Suites:

• MFNet (RGB + Thermal): MM-SAM achieved a notable improvement with an mIoU of 75.9 compared to SAM’s 68.2 on RGB images.
• SUN RGB-D (RGB + Depth): MM-SAM attained an mIoU of 81.2, outperforming SAM’s 78.7 on RGB images.
• SemanticKITTI (RGB + LiDAR): MM-SAM yielded an mIoU of 69.9, surpassing SAM’s 67.8 on RGB images.

2. Time-Asynchronous Sensor Suites:

• DFC2023 (RGB + SAR): MM-SAM exhibited an IoU of 77.4, compared to SAM’s 75.3 on RGB images.
• DFC2018 (RGB + HSI + MS-LiDAR): By integrating all three modalities, MM-SAM achieved the highest IoU of 89.3.

The results clearly demonstrate the effectiveness of MM-SAM in both cross-modal and multi-modal segmentation tasks, significantly outperforming the original SAM, particularly in diverse and complex environments.

Implications and Future Work

Practical Implications:

MM-SAM's ability to enhance segmentation performance across various sensor suites has practical applications in multiple domains. For autonomous driving, it can improve object detection in challenging lighting conditions by integrating thermal and LiDAR data. In remote sensing, combining HSI, SAR, and LiDAR data can lead to more accurate environmental monitoring and urban planning.

Theoretical Implications:

The paper provides valuable insights into the sharability of embedding spaces across different modalities. This approach demonstrates that multi-modal data can be effectively processed within a unified framework, simplifying the architecture and reducing the need for extensive re-training.

Future Directions:

Future work could focus on further optimizing MM-SAM for real-time applications by reducing computational complexity. Additionally, extending MM-SAM to handle semantic and panoptic segmentation tasks could broaden its applicability. Research into more sophisticated fusion techniques and the integration of additional sensor modalities would further enhance the model's versatility and robustness.

Conclusion

In conclusion, MM-SAM represents a significant advancement in the field of visual segmentation, addressing the limitations of single-modal models by enabling efficient and effective processing of multi-modal sensor data. This work lays a strong foundation for further exploration and development of visual foundation models capable of leveraging the synergy of multiple sensors, thereby enhancing the accuracy and robustness of segmentation in complex real-world scenarios.