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Multimodal Perception Modeling Based on Advanced Computational Technologies
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This Reprint explored the advanced computational technologies for modeling human perceptual responses to multimedia stimuli across multiple modalities. With growing interest in areas such as memorability, attention, aesthetics, and persuasion, the use of multimodal approaches is essential for deepening our understanding of how humans interact with multimedia content. By integrating information from visual, textual, auditory, and other sources, as well as gestures and motion, this Reprint seeks to advance the field of multimodal modeling, particularly in applications that benefit from a holistic perception of stimuli. Cutting-edge techniques like large multimodal language models (MMLLMs), machine and deep learning, and multi-sensor fusion are expected to play a key role in addressing these challenges.
In addition, motion recognition remains an integral part of this research as it contributes valuable contextual information to perceptual response modeling. Wearable sensors, smart devices, and computer vision-based methods offer opportunities to analyze motion and gestures.
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