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Found 9 papers, 0 papers with code
Structured Evaluation of Synthetic Tabular Data
Many metrics exist for evaluating the quality of synthetic tabular data; however, we lack an objective, coherent interpretation of the many metrics.
Word Importance Explains How Prompts Affect Language Model Outputs
This approach, inspired by permutation importance for tabular data, masks each word in the system prompt and evaluates its effect on the outputs based on the available text scores aggregated over multiple user inputs.
3D Adversarial Augmentations for Robust Out-of-Domain Predictions
We conduct extensive experiments across a variety of scenarios on data from KITTI, Waymo, and CrashD for 3D object detection, and on data from SemanticKITTI, Waymo, and nuScenes for 3D semantic segmentation.
Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning
We propose a novel semi-supervised active learning (SSAL) framework for monocular 3D object detection with LiDAR guidance (MonoLiG), which leverages all modalities of collected data during model development.
Active Learning for Object Detection with Non-Redundant Informative Sampling
Curating an informative and representative dataset is essential for enhancing the performance of 2D object detectors.
Multi-Task Consistency for Active Learning
In single-task vision-based settings, inconsistency-based active learning has proven to be effective in selecting informative samples for annotation.
Segmenting Known Objects and Unseen Unknowns without Prior Knowledge
By doing so, for the first time in panoptic segmentation with unknown objects, our U3HS is trained without unknown categories, reducing assumptions and leaving the settings as unconstrained as in real-life scenarios.
3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection
Despite training only on a standard dataset, such as KITTI, augmenting with our vector fields significantly improves the generalization to differently shaped objects and scenes.
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Instead of sampling and scoring all possible structures individually, we assume the generator of the CTBN to be composed as a mixture of generators stemming from different structures.
