Search Results for author:
Found 8 papers, 3 papers with code
Reducing Texture Bias of Deep Neural Networks via Edge Enhancing Diffusion
While most of the previous works in the literature focus on the task of image classification, we go beyond this and study the texture bias of CNNs in semantic segmentation.
Deep Active Learning with Noisy Oracle in Object Detection
Here, we propose a composite active learning framework including a label review module for deep object detection.
LMD: Light-weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds
In order to address these two issues, we propose LidarMetaDetect (LMD), a light-weight post-processing scheme for prediction quality estimation.
Identifying Label Errors in Object Detection Datasets by Loss Inspection
In this work, we for the first time introduce a benchmark for label error detection methods on object detection datasets as well as a label error detection method and a number of baselines.
Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection
Active learning as a paradigm in deep learning is especially important in applications involving intricate perception tasks such as object detection where labels are difficult and expensive to acquire.
MGiaD: Multigrid in all dimensions. Efficiency and robustness by coarsening in resolution and channel dimensions
The complexity of the weight count can be seen as a function of the number of channels, the spatial extent of the input and the number of layers of the network.
MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection
On the other hand, meta regression gives rise to a quality estimate.
Deep Bayesian Active Semi-Supervised Learning
In a setting where a small amount of labeled data as well as a large amount of unlabeled data is available, our method first learns the labeled data set.
