RMP-SAM: Towards Real-Time Multi-Purpose Segment Anything (2401.10228v2)

Abstract: Recent segmentation methods, which adopt large-scale data training and transformer architecture, aim to create one foundation model that can perform multiple tasks. However, most of these methods rely on heavy encoder and decoder frameworks, hindering their performance in real-time scenarios. To explore real-time segmentation, recent advancements primarily focus on semantic segmentation within specific environments, such as autonomous driving. However, they often overlook the generalization ability of these models across diverse scenarios. Therefore, to fill this gap, this work explores a novel real-time segmentation setting called real-time multi-purpose segmentation. It contains three fundamental sub-tasks: interactive segmentation, panoptic segmentation, and video instance segmentation. Unlike previous methods, which use a specific design for each task, we aim to use only a single end-to-end model to accomplish all these tasks in real-time. To meet real-time requirements and balance multi-task learning, we present a novel dynamic convolution-based method, Real-Time Multi-Purpose SAM (RMP-SAM). It contains an efficient encoder and an efficient decoupled adapter to perform prompt-driven decoding. Moreover, we further explore different training strategies and one new adapter design to boost co-training performance further. We benchmark several strong baselines by extending existing works to support our multi-purpose segmentation. Extensive experiments demonstrate that RMP-SAM is effective and generalizes well on proposed benchmarks and other specific semantic tasks. Our implementation of RMP-SAM achieves the optimal balance between accuracy and speed for these tasks.Our code and model are available at https://github.com/xushilin1/RAP-SAM/.

• The paper introduces RAP-SAM, a model that achieves real-time segmentation across diverse tasks through an efficient lightweight encoder-decoder design.
• It unifies panoptic, interactive, and video segmentation using a shared dynamic convolution approach to significantly reduce computational overhead.
• Empirical results demonstrate that RAP-SAM outperforms models like Mask2Former and K-Net on benchmarks such as COCO and YouTube-VIS, offering a superior speed-accuracy trade-off.

Overview of RAP-SAM: Towards Real-Time All-Purpose Segment Anything

The paper presents a novel approach, RAP-SAM, a Real-Time All-Purpose Segment Anything model. This research pioneers the integration of diverse segmentation tasks into a single framework capable of real-time performance. The model is meticulously designed to address the challenges associated with deploying Vision Foundation Models (VFMs) for applications necessitating instant segmentation outputs.

Background and Motivation

Vision Foundation Models, such as the Segment Anything Model (SAM), have shown impressive generalization capabilities across segmentation tasks. However, their real-time deployment is hampered by computational constraints, primarily due to complex and computationally heavy architectures. RAP-SAM seeks to overcome these limitations by proposing a more efficient model that can handle various inputs—images, videos, and interactive prompts—while delivering timely results.

Methodological Innovations

RAP-SAM introduces several key innovations to achieve its objectives:

1. Efficient Architecture Design: The model is structured with a lightweight encoder and a decoupled decoder. This design ensures real-time performance by reducing the computational load without sacrificing segmentation accuracy.
2. Unified Framework for Multiple Tasks: By leveraging a shared dynamic convolution approach, RAP-SAM performs panoptic, interactive, and video segmentation within a single architecture. The model replaces traditional per-pixel cross-attention with pooling mechanisms, enhancing both efficiency and scalability.
3. Adaptive Query Processing: The dual adapter design—comprising an object adapter and a prompt adapter—facilitates task-specific adjustments to shared model components, ensuring a balanced performance across different segmentation tasks.

Empirical Evaluation

The empirical evaluation of RAP-SAM highlights its superior performance across multiple benchmarks, including COCO-Panoptic, COCO-SAM, and YouTube-VIS 2019. Notably, RAP-SAM achieves a commendable trade-off between speed and accuracy, outperforming prominent models like Mask2Former and K-Net in real-time settings. The model's capability to perform well across varied backbones further underscores its adaptability.

Implications and Future Directions

RAP-SAM's contributions hold significant implications for both practical applications and further research in computer vision:

• Practical Deployment: Its efficiency makes it suitable for deployment in applications where real-time feedback is crucial, such as autonomous driving, interactive image editing, and video surveillance systems.
• Future Research: This work opens avenues for exploring more efficient transformer designs and advanced training strategies that could further optimize performance. Additionally, future efforts could focus on extending the model's capabilities to handle even more diverse and complex segmentation tasks or prompt types.

Conclusion

This paper introduces RAP-SAM as a comprehensive solution for all-purpose segmentation in real-time scenarios. By addressing the computational challenges associated with VFMs, it sets a foundational precedent for future research aimed at enhancing the versatility and efficiency of segmentation models. The results and framework provided by RAP-SAM have the potential to influence subsequent innovations in real-time segmentation, further bridging the gap between sophisticated models and practical, real-world applications.