5 accepted papers
Multimodal machine learning, mimicking the human brain’s ability to integrate various modalities has seen rapid growth. Most previous multimodal models are trained on perfectly paired multimodal input to reach optimal performance. In real‑world deployments, however, the presence of modality is highl…
Dynamic spatio-temporal understanding is essential for video-based multimodal tasks, yet existing methods often struggle to capture fine-grained temporal and spatial relationships in long videos. Current approaches primarily rely on pre-trained CLIP encoders, which excel in semantic understanding bu
Multimodal representation learning is a challenging task in which previous work mostly focus on either uni-modality pre-training or cross-modality fusion. In fact, we regard modeling multimodal representation as building a skyscraper, where laying stable foundation and designing the main structure a…
Periodic binary sequences with good autocorrelation properties are useful for phase-modulated continuous wave (PMCW) automotive radar systems. The optimization problem encountered in the periodic binary sequence designs is NP-hard, and most of the existing algorithms are not suitable for designing l…
We consider herein the case of two vehicles equipped with multi-input multi-output (MIMO) automotive radars driving next to each other. We assume that 5G communications allow us to coordinate the radar probing waveforms for the vehicles. Then the binary sequence sets transmitted by different vehicle…