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Found 17 papers, 10 papers with code
Normalizing Flow Regression for Bayesian Inference with Offline Likelihood Evaluations
Bayesian inference with computationally expensive likelihood evaluations remains a significant challenge in many scientific domains.
Stacking Variational Bayesian Monte Carlo
Variational Bayesian Monte Carlo (VBMC) is a sample-efficient method for approximate Bayesian inference with computationally expensive likelihoods.
Amortized Bayesian Workflow (Extended Abstract)
Bayesian inference often faces a trade-off between computational speed and sampling accuracy.
InkSight: Offline-to-Online Handwriting Conversion by Learning to Read and Write
Digital note-taking is gaining popularity, offering a durable, editable, and easily indexable way of storing notes in a vectorized form, known as digital ink.
PyVBMC: Efficient Bayesian inference in Python
PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference for black-box computational models (Acerbi, 2018, 2020).
Modular Quantization-Aware Training for 6D Object Pose Estimation
MQAT guides a systematic gradated modular quantization sequence and determines module-specific bit precisions, leading to quantized models that outperform those produced by state-of-the-art uniform and mixed-precision quantization techniques.
Fast post-process Bayesian inference with Variational Sparse Bayesian Quadrature
We propose the framework of post-process Bayesian inference as a means to obtain a quick posterior approximation from existing target density evaluations, with no further model calls.
Model Generalization: A Sharpness Aware Optimization Perspective
Sharpness-Aware Minimization (SAM) and adaptive sharpness-aware minimization (ASAM) aim to improve the model generalization.
Robotic Surgery Remote Mentoring via AR with 3D Scene Streaming and Hand Interaction
In this paper, we propose a novel AR-based robotic surgery remote mentoring system with efficient 3D scene visualization and natural 3D hand interaction.
Understanding Pixel-level 2D Image Semantics with 3D Keypoint Knowledge Engine
Pixel-level 2D object semantic understanding is an important topic in computer vision and could help machine deeply understand objects (e. g. functionality and affordance) in our daily life.
Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes
In the work, we disentangle the direct offset into Local Canonical Coordinates (LCC), box scales and box orientations.
Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and Experimental Study
Finally, a systematic survey to the existing efforts to solve the data hunger problem is conducted on both methodological and dataset's viewpoints, followed by an insightful discussion of remaining problems and open questions To the best of our knowledge, this is the first work to analyze the data hunger problem for 3D semantic segmentation using deep learning techniques that are addressed in the literature review, statistical analysis, and cross-dataset and cross-algorithm experiments.
Semantic Correspondence via 2D-3D-2D Cycle
Visual semantic correspondence is an important topic in computer vision and could help machine understand objects in our daily life.
KeypointNet: A Large-scale 3D Keypoint Dataset Aggregated from Numerous Human Annotations
Detecting 3D objects keypoints is of great interest to the areas of both graphics and computer vision.
SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances
3D semantic segmentation is one of the key tasks for autonomous driving system.
Human Correspondence Consensus for 3D Object Semantic Understanding
Semantic understanding of 3D objects is crucial in many applications such as object manipulation.
Estimating 6D Pose From Localizing Designated Surface Keypoints
In this paper, we present an accurate yet effective solution for 6D pose estimation from an RGB image.
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6D Pose Estimation using RGB
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