The field of multimodal perception and industrial signal representation is moving towards more efficient and robust methods for processing and analyzing complex data. Researchers are exploring new approaches to multi-modal fusion, such as adaptive low-rank compensation and context-aware frameworks, to improve the accuracy and scalability of perception systems in resource-constrained environments. Additionally, there is a growing interest in developing foundation models for comprehensive representation of industrial signals, which can effectively utilize the synergies between modalities and powerful scaling laws. These advancements have the potential to significantly impact various applications, including smart homes, intelligent transport, and healthcare. Noteworthy papers in this area include:

• GRAM-MAMBA, which proposes a holistic feature alignment framework for wireless perception with adaptive low-rank compensation, achieving state-of-the-art performance on several benchmarks.
• Polymorph, a context-aware framework for energy-efficient multi-label classification of video streams on embedded devices, which improves scalability while reducing latency and energy overhead.
• FISHER, a foundation model for multi-modal industrial signal comprehensive representation, which showcases versatile and outstanding capabilities with a general performance gain up to 5.03% on multiple health management tasks.