LCMF: Lightweight Cross-Modality Mambaformerfor Embodied Robotics VQA

Key Points

• This approach enhances visual question answering capabilities in embodied robotics, improving interaction fluidity.
• It demonstrates reduced computational resource needs while maintaining effectiveness, indicating a balance of efficiency.
• The analysis employs a cross-modality framework to process visual and textual inputs, maximizing contextual understanding.
• Results suggest broader applications in robotics and AI, highlighting the need for lightweight systems in resource-limited environments.

Abstract