- 🔭 I’m currently working on data-efficient deep learning-based algorithms for industrial machine vision applications.
- 🌱 I’m currently studying weakly-supervised and unsupervised algorithms for defect segmentation and anomaly detection.
- 👯 I’m looking to collaborate on unsupervised segmentation methods using multi-modal datasets of image, text and audio for general purpose method.
- 👯 I'm also interested in medical image analysis, anomaly detection and machinery fault diagnosis.
- 📫 How to reach me: djene.mengistu@gmail.com
Postdoctoral researcher
Machine vision and deep learning
-
Xidian University
- Xi'an, Shaanxi, China
- in/dejene-mengistu-37a44724
- https://scholar.google.co.uk/citations?user=YNUTFG4AAAAJ&hl=en
- https://www.researchgate.net/profile/Dejene-Mengistu
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dseg_models
dseg_models PublicThis repo contains implementation of deep learning-based steel surface defect segmentation models.
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Machine-Vision-and-Anomaly-Detection-Papers
Machine-Vision-and-Anomaly-Detection-Papers PublicThis repo contains state-of-the-art deep learning models for industrial anomaly detection, defect segmentation, detection, and classification, with other industrial machine vision applications.
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Awesome-Segment-Anything
Awesome-Segment-Anything PublicForked from liliu-avril/Awesome-Segment-Anything
This repository is for the first comprehensive survey on Meta AI's Segment Anything Model (SAM).
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AnomalyGPT
AnomalyGPT PublicForked from CASIA-IVA-Lab/AnomalyGPT
The first LVLM based IAD method!
Python 1
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