Search Results for author:
Found 10 papers, 8 papers with code
TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection
We propose a novel transparency-based diffusion process, where the transparency of anomalous regions is progressively increased, restoring their normal appearance accurately and maintaining the appearance of anomaly-free regions without loss of detail.
Ranked #8 on
Anomaly Detection
on VisA
Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation
(ii) We tackle the lack of diverse industrial depth datasets by introducing a simulation process for learning informative depth features in the depth encoder.
Depth Anomaly Detection and Segmentation
RGB+3D Anomaly Detection and Segmentation
DSR -- A dual subspace re-projection network for surface anomaly detection
The state-of-the-art in discriminative unsupervised surface anomaly detection relies on external datasets for synthesizing anomaly-augmented training images.
Ranked #1 on
Supervised Defect Detection
on KolektorSDD2
Supervised Defect Detection
Unsupervised Anomaly Detection
+1
Segmentation of Multiple Myeloma Plasma Cells in Microscopy Images with Noisy Labels
A key component towards an improved and fast cancer diagnosis is the development of computer-assisted tools.
DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection
Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance.
Ranked #15 on
Anomaly Detection
on VisA
Mixed supervision for surface-defect detection: from weakly to fully supervised learning
We also show that mixed supervision with only a handful of fully annotated samples added to weakly labelled training images can result in performance comparable to the fully supervised model's performance but at a significantly lower annotation cost.
Ranked #1 on
Defect Detection
on KolektorSDD2
Reconstruction by Inpainting for Visual Anomaly Detection
Visual anomaly detection addresses the problem of classification or localization of regions in an image that deviate from their normal appearance.
Ranked #6 on
Anomaly Detection
on AeBAD-V
End-to-end training of a two-stage neural network for defect detection
We demonstrate the performance of the end-to-end training scheme and the proposed extensions on three defect detection datasets - DAGM, KolektorSDD and Severstal Steel defect dataset - where we show state-of-the-art results.
Ranked #1 on
Defect Detection
on DAGM2007
(Average Precision metric)
Deep Learning for Large-Scale Traffic-Sign Detection and Recognition
Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory.
Segmentation-Based Deep-Learning Approach for Surface-Defect Detection
This paper presents a segmentation-based deep-learning architecture that is designed for the detection and segmentation of surface anomalies and is demonstrated on a specific domain of surface-crack detection.
Ranked #3 on
Defect Detection
on KolektorSDD
