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Found 16 papers, 7 papers with code
Divide and Conquer: High-Resolution Industrial Anomaly Detection via Memory Efficient Tiled Ensemble
To overcome this challenge, we present the tiled ensemble approach, which reduces memory consumption by dividing the input images into a grid of tiles and training a dedicated model for each tile location.
Anomalib: A Deep Learning Library for Anomaly Detection
This paper introduces anomalib, a novel library for unsupervised anomaly detection and localization.
A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items
Screening cluttered and occluded contraband items from baggage X-ray scans is a cumbersome task even for the expert security staff.
Tensor Pooling Driven Instance Segmentation Framework for Baggage Threat Recognition
Furthermore, to the best of our knowledge, this is the first contour instance segmentation framework that leverages multi-scale information to recognize cluttered and concealed contraband data from the colored and grayscale security X-ray imagery.
Unsupervised Anomaly Instance Segmentation for Baggage Threat Recognition
Identifying potential threats concealed within the baggage is of prime concern for the security staff.
Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation
For optimal performance in semantic segmentation, our model generates depth to be used as complementary source information with RGB in the segmentation network.
Multi-Model Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation
Using this architectural formulation with dense skip connections, our model achieves comparable performance to contemporary approaches at a fraction of the overall model complexity.
Trainable Structure Tensors for Autonomous Baggage Threat Detection Under Extreme Occlusion
Detecting baggage threats is one of the most difficult tasks, even for expert officers.
Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from X-ray Scans
In the last two decades, baggage scanning has globally become one of the prime aviation security concerns.
Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging
X-ray security screening is widely used to maintain aviation/transport security, and its significance poses a particular interest in automated screening systems.
Online Deep Reinforcement Learning for Autonomous UAV Navigation and Exploration of Outdoor Environments
With the rapidly growing expansion in the use of UAVs, the ability to autonomously navigate in varying environments and weather conditions remains a highly desirable but as-of-yet unsolved challenge.
Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from Multi-Vendor X-ray Scans
In the last two decades, luggage scanning has globally become one of the prime aviation security concerns.
Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery
X-ray imagery security screening is essential to maintaining transport security against a varying profile of threat or prohibited items.
On the Impact of Object and Sub-component Level Segmentation Strategies for Supervised Anomaly Detection within X-ray Security Imagery
X-ray security screening is in widespread use to maintain transportation security against a wide range of potential threat profiles.
Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments
Increased growth in the global Unmanned Aerial Vehicles (UAV) (drone) industry has expanded possibilities for fully autonomous UAV applications.
Ranked #1 on
Autonomous Flight (Dense Forest)
on mtrl-auto-uav
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal).
Generative Adversarial Network
Semi-supervised Anomaly Detection
+1
