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Found 8 papers, 2 papers with code
A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online Adaptation
Effectively addressing the challenge of industrial Anomaly Detection (AD) necessitates an ample supply of defective samples, a constraint often hindered by their scarcity in industrial contexts.
DART: Depth-Enhanced Accurate and Real-Time Background Matting
In this paper, we leverage the rich depth information provided by the RGB-Depth (RGB-D) cameras to enhance background matting performance in real-time, dubbed DART.
Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval
In this framework, the anomaly detection problem is solved via a cascade patch retrieval procedure that retrieves the nearest neighbors for each test image patch in a coarse-to-fine fashion.
Ranked #1 on
Supervised Anomaly Detection
on BTAD
Efficient Anomaly Detection with Budget Annotation Using Semi-Supervised Residual Transformer
Thus the sliding transformer can attain even higher accuracy with much less annotation labor.
Ranked #1 on
Anomaly Detection
on MVTec AD
(Segmentation AUROC metric)
Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation
Visual tracking is a fundamental problem in computer vision.
DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking
In this work, we present an efficient and very robust tracking algorithm using a single Convolutional Neural Network (CNN) for learning effective feature representations of the target object, in a purely online manner.
Face Recognition using Optimal Representation Ensemble
It also overwhelms other modular heuristics on the faces with random occlusions, extreme expressions and disguises.
