Search Results for author:
Found 5 papers, 2 papers with code
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
We train a student network to predict the extracted features of normal, i. e., anomaly-free training images.
Ranked #2 on
Anomaly Detection
on MVTec AD
(using extra training data)
Computational Efficiency
Semi-supervised Anomaly Detection
+1
A Hybrid Approach for 6DoF Pose Estimation
We propose a method for 6DoF pose estimation of rigid objects that uses a state-of-the-art deep learning based instance detector to segment object instances in an RGB image, followed by a point-pair based voting method to recover the object's pose.
Oriented Boxes for Accurate Instance Segmentation
State-of-the-art instance-aware semantic segmentation algorithms use axis-aligned bounding boxes as an intermediate processing step to infer the final instance mask output.
Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentation
Semantic amodal segmentation is a recently proposed extension to instance-aware segmentation that includes the prediction of the invisible region of each object instance.
MVTec D2S: Densely Segmented Supermarket Dataset
The dataset covers several challenges highly relevant in the field, such as a limited amount of training data and a high diversity in the test and validation sets.
