Seg2Change adapts open-vocabulary segmentation models to open-vocabulary change detection via a category-agnostic change head and new dataset CA-CDD, delivering +9.52 IoU on WHU-CD and +5.50 mIoU on SECOND.
Exploring efficient open-vocabulary segmentation in the remote sensing
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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.
Introduces MTRS task, MTRefSeg-21K benchmark of 21K image-text-mask triplets, and MTRefSeg-R1 LVLM baseline that outperforms standard models via two-stage change-aware training.
IDCL adds density-based curriculum learning and density-core guidance to deep image clustering, claiming superior robustness, faster convergence, and flexibility on benchmark datasets.
GR-CoT improves remote sensing open-vocabulary segmentation by building category interpretation standards offline and using macro-scenario anchoring plus knowledge-driven synthesis online to create image-adaptive vocabularies.
PALM improves long-horizon robotic manipulation success by distilling affordance representations for object interaction and predicting within-subtask progress in a VLA model.
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.
SAM 3 can be applied training-free to remote sensing open-vocabulary segmentation and change detection by fusing its semantic and instance heads and filtering with presence scores.
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.
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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.