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A Probabilistic Relaxation of the Two-Stage Object Pose Estimation Paradigm
Existing object pose estimation methods commonly require a one-to-one point matching step that forces them to be separated into two consecutive stages: visual correspondence detection (e. g., by matching feature descriptors as part of a perception front-end) followed by geometric alignment (e. g., by optimizing a robust estimation objective for pointcloud registration or perspective-n-point).
PALMER: Perception-Action Loop with Memory for Long-Horizon Planning
For training these perceptual representations, we combine Q-learning with contrastive representation learning to create a latent space where the distance between the embeddings of two states captures how easily an optimal policy can traverse between them.
Simple Control Baselines for Evaluating Transfer Learning
Transfer learning has witnessed remarkable progress in recent years, for example, with the introduction of augmentation-based contrastive self-supervised learning methods.
Measuring the Effectiveness of Self-Supervised Learning using Calibrated Learning Curves
Self-supervised learning has witnessed remarkable progress in recent years, in particular with the introduction of augmentation-based contrastive methods.
Scan Specific Artifact Reduction in K-space (SPARK) Neural Networks Synergize with Physics-based Reconstruction to Accelerate MRI
Conclusion: SPARK synergizes with physics-based acquisition and reconstruction techniques to improve accelerated MRI by training scan-specific models to estimate and correct reconstruction errors in k-space.
