SIMON improves EEG-to-image retrieval by saliency-aware multi-view sampling, reaching 69.7% intra-subject and 19.6% inter-subject Top-1 accuracy on THINGS-EEG.
Visual attention: The past 25 years.Vision Research, 51(13):1484–1525, 2011
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This survey synthesizes AI techniques for mixed autonomy traffic simulation and introduces a taxonomy spanning agent-level behavior models, environment-level methods, and cognitive/physics-informed approaches.
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SIMON: Saliency-aware Integrative Multi-view Object-centric Neural Decoding
SIMON improves EEG-to-image retrieval by saliency-aware multi-view sampling, reaching 69.7% intra-subject and 19.6% inter-subject Top-1 accuracy on THINGS-EEG.
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Artificial Intelligence for Modeling and Simulation of Mixed Automated and Human Traffic
This survey synthesizes AI techniques for mixed autonomy traffic simulation and introduces a taxonomy spanning agent-level behavior models, environment-level methods, and cognitive/physics-informed approaches.