Multimodal Cognitive Attention for Robust Recognition in Adverse Environments

Many current systems—like the drowsiness detection by Safarov et al. (2023) or sign recognition by Ingle et al. (2025)—rely heavily on visual cues, struggling in ambiguous or cluttered scenes. Drawing inspiration from bottom-up visual attention models (Prahara et al., 2020) and the multimodal synthesis trend (Corona et al., 2024; Cohen et al., 2025), this idea proposes a system that integrates vision (RGB/depth), audio, and environmental sensors (temperature, motion, etc.), uses a neural attention mechanism inspired by human cognition and saliency (as discussed by Prahara et al.), and dynamically weights input modalities based on context (e.g., if vision is noisy, rely more on audio or sensor cues). This “cognitive fusion” approach is especially promising for real-world HCI (Sebe et al., 2005), collaborative robotics (Cohen et al., 2025), or assistive devices for the visually impaired (Raskar et al., 2025). The novelty is in using a neuroscience-inspired attention mechanism to adaptively prioritize modalities in real time, informed by bottom-up and top-down cues—a step beyond simple sensor fusion.

References:

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