Table-Top Scene Analysis Using Knowledge-Supervised MCMC
In this paper, we propose a probabilistic method to generate abstract scene graphs for table-top scenes from 6D object pose estimates. We explicitly make use of task-specfic context knowledge by encoding this knowledge as descriptive rules in Markov logic networks. Our approach to generate scene graphs is probabilistic: Uncertainty in the object poses is addressed by a probabilistic sensor model that is embedded in a data driven MCMC process. We apply Markov logic inference to reason about hidden objects and to detect false estimates of object poses. The effectiveness of our approach is demonstrated and evaluated in real world experiments.
Code (0)
등록된 구현이 없습니다.
Tasks
DescriptiveObjectSimilar Papers 제목 키워드 기반
Knowledge Removal in Sampling-based Bayesian Inference
The right to be forgotten has been legislated in many countries, but its enforcement in the AI industry would cause unbearable costs. When single data deletion requests come, companies may need to delete the whole models…
Bayesian InferenceMachine UnlearningOn the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models
This study investigates the effects of Markov chain Monte Carlo (MCMC) sampling in unsupervised Maximum Likelihood (ML) learning. Our attention is restricted to the family of unnormalized probability densities for which …
AnatomyLearning Energy-Based Model with Variational Auto-Encoder as Amortized Sampler
Due to the intractable partition function, training energy-based models (EBMs) by maximum likelihood requires Markov chain Monte Carlo (MCMC) sampling to approximate the gradient of the Kullback-Leibler divergence betwee…
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Replica exchange Monte Carlo (reMC), also known as parallel tempering, is an important technique for accelerating the convergence of the conventional Markov Chain Monte Carlo (MCMC) algorithms. However, such a method req…
Image ClassificationGenerative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis
3D data that contains rich geometry information of objects and scenes is valuable for understanding 3D physical world. With the recent emergence of large-scale 3D datasets, it becomes increasingly crucial to have a power…
3D Object ClassificationSuper-Resolution