arXiv:2605.21308v1 Announce Type: cross Abstract: State Space Models (SSMs) have emerged as a powerful and efficient alternative to Transformers, demonstrating linear-time complexity and exceptional sequence modeling capabilities. However, their application to vision tasks remains challenging. First, existing vision SSMs largely depend on manually designed fixed scanning methods to flatten image patches into sequences, which imposes predefined geometric structures and increases the complexity. Second, the broader adoption of vision SSMs is hindered in domains that require query-based interacti
The continuous evolution of AI models necessitates more efficient architectures for complex tasks, pushing the boundaries of existing Transformer-based systems and driving innovation in alternative approaches like State Space Models.
Improving vision State Space Models makes advanced AI vision capabilities more computationally efficient and adaptable, potentially broadening their application in critical sectors from robotics to autonomous systems.
Vision SSMs may become a more viable and efficient alternative to Transformers for image understanding, reducing computational costs and overcoming some limitations in handling visual data.
- · AI researchers and developers
- · Robotics companies
- · Companies developing autonomous systems
- · Hardware manufacturers for efficient AI inference
- · Developers solely invested in Transformer-based vision models
- · High-energy-consumption AI compute providers
More efficient and accurate AI visual perception systems emerge, leading to better performance in computer vision tasks.
Reduced computational demands for advanced AI vision accelerate deployment of sophisticated AI in edge devices and cost-sensitive applications.
The democratization of advanced visual AI capabilities fosters innovation across various industries, enabling new products and services that were previously too expensive or resource-intensive.
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Read at arXiv cs.AI