Author

Yihan Lin

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Jul 2026

Brain-inspired visual sensing–perception for open-world environments

Experiments show that bottom-up learning yields consistent generalization across diverse degradations, while top-down modulation substantially improves monocular depth estimation and video instance segmentation under severe interference, establishing a principled brain-inspired computational approach for advancing artificial visual intelligence and understanding human vision.

Yihan Lin, Yuguo Chen, Y. Meng et al. · Nature Sensors · 0 citations

Preprint Aug 2026

JEPA-WAM: Learning Vision-Language-Action Policies with Joint-Embedding World Modeling

JEPA-WAM, a latent WAM built in a pretrained V-JEPA space, which couples latent transition prediction with continuous action generation through a shared predictor, predicts a spatially structured joint current-future target that captures task-shared visual temporal structure between current and future observations, while preserving dense patch-level correspondence.

Yihan Lin, Jiawei He, Shifeng Bao et al. · 2 citations