KinemaForge jointly infers part geometry, joint topology, and parameters from RGB-D sequences using a kinematic graph and differentiable dynamics, then verifies with an energy residual loss, reporting lower joint errors and reduced simulation drift than PARIS and Ditto baselines.
From physics to foundation models: A review of ai-driven quan- titative remote sensing inversion.arXiv preprint arXiv:2507.09081, 2025
4 Pith papers cite this work. Polarity classification is still indexing.
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FSDrive uses a generated future scene frame as visual spatio-temporal CoT to improve VLA models for safer autonomous driving trajectory prediction.
AtmoFuseNet fuses multi-view sky cameras, millimeter-wave radar, and ceilometer data via hierarchical cross-attention, variational refinement, and motion estimation to produce 4D cloud microphysical fields and wind with reported MAEs of 0.026 g m^{-3} LWC and 1.18 m s^{-1} wind speed.
Gradient boosting with conformal prediction and mutual-information stability selection yields NAFLD risk predictions with 91.3% empirical coverage at 90% nominal level and AUROC 0.91 on multicenter Chinese data.
citing papers explorer
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URDF Synthesis from RGB-D Sequences via Differentiable Joint Inference and Energy-Consistent Verification
KinemaForge jointly infers part geometry, joint topology, and parameters from RGB-D sequences using a kinematic graph and differentiable dynamics, then verifies with an energy residual loss, reporting lower joint errors and reduced simulation drift than PARIS and Ditto baselines.
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FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving
FSDrive uses a generated future scene frame as visual spatio-temporal CoT to improve VLA models for safer autonomous driving trajectory prediction.
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Cross-Modal Hierarchical Fusion for from Multi-Sensor Ground Observation
AtmoFuseNet fuses multi-view sky cameras, millimeter-wave radar, and ceilometer data via hierarchical cross-attention, variational refinement, and motion estimation to produce 4D cloud microphysical fields and wind with reported MAEs of 0.026 g m^{-3} LWC and 1.18 m s^{-1} wind speed.
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Conformal Risk Prediction for Non-Alcoholic Fatty Liver Disease Using Gradient Boosting with Distribution-Free Coverages
Gradient boosting with conformal prediction and mutual-information stability selection yields NAFLD risk predictions with 91.3% empirical coverage at 90% nominal level and AUROC 0.91 on multicenter Chinese data.