Multimodal sensing in physical AI (PAI), sometimes called embodied AI, is the ability for AI to fuse diverse sensory inputs, ...

Precise motion control often requires more than tracking position within a single rotation. Multi-turn encoders provide extended position feedback by counting complete revolutions in addition to ...

Abstract: Human–robot collaboration (HRC) assembly necessitates precise mutual cognition to guarantee safe and efficient execution. In this context, human assembly intention recognition (HAIR) serves ...

A generalized architectural blueprint for building efficient MLLMs. This template achieves efficiency through a combination of component choices and data flow optimization. Key strategies include: (1) ...

Meta's SAM Audio leverages multimodal prompts for audio separation, offering intuitive sound isolation capabilities. The model introduces state-of-the-art features for various audio processing tasks.

With the great success of large language models, self-supervised pre-training technologies have shown the great promise in the field of drug discovery. In particular, multimodal pre-training models ...

Ray's innovative disaggregated hybrid parallelism significantly enhances multimodal AI training efficiency, achieving up to 1.37x throughput improvement and overcoming memory challenges. In a ...

Despite notable progress in deep learning, change detection (CD) in remote sensing images continues to pose significant challenges, especially for multimodal datasets due to intrinsic differences [1].

Note: Pick ONE encoder type per modality that matches your dataset. You don't need to implement all encoder variants. 4. Uncertainty Quantification (src/uncertainty.py) - 20 points ...