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Found 9 papers, 2 papers with code
Sample, Crop, Track: Self-Supervised Mobile 3D Object Detection for Urban Driving LiDAR
Supervised approaches typically require the annotation of large training sets; there has thus been great interest in leveraging weakly, semi- or self-supervised methods to avoid this, with much success.
DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction
Self-supervised depth learning from monocular images normally relies on the 2D pixel-wise photometric relation between temporally adjacent image frames.
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces
Scene flow is a powerful tool for capturing the motion field of 3D point clouds.
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture
In view of these, we propose DHA, which achieves joint optimization of Data augmentation policy, Hyper-parameter and Architecture.
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning
Our method performs co-optimization of the neural architectures, training hyper-parameters and data augmentation policies in an end-to-end fashion without the need of model retraining.
Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling
Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks.
VMLoc: Variational Fusion For Learning-Based Multimodal Camera Localization
We conjecture that this is because of the naive approaches to feature space fusion through summation or concatenation which do not take into account the different strengths of each modality.
Tighter Bound Estimation of Sensitivity Analysis for Incremental and Decremental Data Modification
Specifically, the proposed algorithm can be used to estimate the upper and lower bounds of the updated classifier's coefficient matrix with a low computational complexity related to the size of the updated dataset.
Smart Train Operation Algorithms based on Expert Knowledge and Reinforcement Learning
Then we develop two algorithms for optimizing the energy efficiency of train operation.
