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Found 10 papers, 3 papers with code
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization
By fine-tuning the ADClick-Seg model using the weak labels inferred by ADClick, we establish the state-of-the-art performances in supervised AD tasks (AP $= 86. 4\%$ on MVTec AD and AP $= 78. 4\%$, PRO $= 98. 6\%$ on KSDD2).
Ranked #3 on
Supervised Anomaly Detection
on MVTec AD
(using extra training data)
Seeing Through the Clouds: Cloud Gap Imputation with Prithvi Foundation Model
To address this issue, we compare the performance of a foundational Vision Transformer (ViT) model with a baseline Conditional Generative Adversarial Network (CGAN) model for missing value imputation in time series of multispectral satellite imagery.
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
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers
In this work, we propose a novel framework called "Weakly-supervised RESidual Transformer" (WeakREST), which aims to achieve high AD accuracy while minimizing the need for extensive annotations.
Ranked #1 on
Anomaly Detection
on BTAD
(using extra training data)
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.
