See the Future, Classify the Present: Stochastic Latent World Modeling for Hyperspectral Images

Key points

• To address this limitation, this work proposes Stochastic Latent World Modeling (LWM), a trajectory-based representation-learning framework that enriches classification through stochastic latent-state prediction.1
• Given a hyperspectral patch, LWM tokenizes and encodes spectral and spatial information separately using dedicated transformer branches, then fuses their outputs into a unified representation.1
• A stochastic latent transition module then predicts multiple future latent states through Gaussian parameterization and reparameterization, with recurrent refinement used to model dependencies across successive states.1
• By extending static spatial-spectral encoding with stochastic multi-step latent-state modeling, LWM enables the classifier to exploit structured dependencies in the representation space and provides richer features for HSIC.1

Sources (1)

• [1]See the Future, Classify the Present: Stochastic Latent World Modeling for Hyperspectral Images
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 04:48 AM

  Hyperspectral image classification (HSIC) relies on static spatial-spectral representations learned from labeled samples, limiting its ability to model structured dependencies in the latent representation space.

  To address this limitation, this work proposes Stochastic Latent World Modeling (LWM), a trajectory-based representation-learning framework that enriches classification through stochastic latent-state prediction.

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