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TAB: Text-Align Anomaly Backbone Model for Industrial Inspection Tasks

TAB: Text-Align Anomaly Backbone Model for Industrial Inspec...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ho-Weng Lee Shang-Hong Lai Department of Computer Science National Tsing Hua University Taiwan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, the focus on anomaly detection and localization in industrial inspection tasks has intensified. While existing studies have demonstrated impressive outcomes, they often rely heavily on extensive training datasets or robust features extracted from pre-trained models trained on diverse datasets like ImageNet. In this work, we propose a novel framework leveraging the visual-linguistic CLIP model to adeptly train a backbone model tailored to the manufacturing domain. Our approach concurrently considers visual and text-aligned embedding spaces for normal and abnormal conditions. The resulting pre-trained backbone markedly enhances performance in industrial downstream tasks, particularly in anomaly detection and localization. This improvement is substantiated through experiments conducted on multiple datasets such as MVTecAD, BTAD, and KSDD2. Furthermore, using our pre-trained backbone weights allows previous works to achieve superior performance in few-shot scenarios with less training data. The proposed anomaly backbone provides a foundation model for more precise anomaly detection and localization. The source code and pre-trained weights are available at https://***/Howeng98/TAB.

关键词： Location awareness Training Visualization Source coding Training data Inspection Feature extraction

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Homogeneous Linear Inequality Constraints for Neural Network Activations

Homogeneous Linear Inequality Constraints for Neural Network...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Thomas Frerix Matthias Nießner Daniel Cremers Technical University of Munich

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We propose a method to impose homogeneous linear inequality constraints of the form Ax ≤ 0 on neural network activations. The proposed method allows a data-driven training approach to be combined with modeling prior knowledge about the task. One way to achieve this task is by means of a projection step at test time after unconstrained training. However, this is an expensive operation. By directly incorporating the constraints into the architecture, we can significantly speed-up inference at test time; for instance, our experiments show a speed-up of up to two orders of magnitude over a projection method. Our algorithm computes a suitable parameterization of the feasible set at initialization and uses standard variants of stochastic gradient descent to find solutions to the constrained network. Thus, the modeling constraints are always satisfied during training. Crucially, our approach avoids to solve an optimization problem at each training step or to manually trade-off data and constraint fidelity with additional hyperparameters. We consider constrained generative modeling as an important application domain and experimentally demonstrate the proposed method by constraining a variational autoencoder.

关键词： Training Neural networks Computational modeling Task analysis Optimization Machine learning computer architecture

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Lens-to-Lens Bokeh Effect Transformation. NTIRE 2023 Challenge Report

Lens-to-Lens Bokeh Effect Transformation. NTIRE 2023 Challen...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Marcos V. Conde Manuel Kolmet Tim Seizinger Tom E. Bishop Radu Timofte Xiangyu Kong Dafeng Zhang Jinlong Wu Fan Wang Juewen Peng Zhiyu Pan Chengxin Liu Xianrui Luo Huiqiang Sun Liao Shen Zhiguo Cao Ke Xian Chaowei Liu Zigeng Chen Xingyi Yang Songhua Liu Yongcheng Jing Michael Bi Mi Xinchao Wang Zhihao Yang Wenyi Lian Siyuan Lai Haichuan Zhang Trung Hoang Amirsaeed Yazdani Vishal Monga Ziwei Luo Fredrik K. Gustafsson Zheng Zhao Jens Sjölund Thomas B. Schön Yuxuan Zhao Baoliang Chen Yiqing Xu JiXiangNiu Computer Vision Lab University of Würzburg Germany Glass Imaging Inc CA

We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge. Recent advancements of mobile photography aim to reach the visual quality of full-frame cameras. Now, a goal in computational photography is to optimize the Bokeh effect itself, which is the aesthetic quality of the blur in out-of-focus areas of an image. Photographers create this aesthetic effect by benefiting from the lens optical *** aim of this work is to design a neural network capable of converting the the Bokeh effect of one lens to the effect of another lens without harming the sharp foreground regions in the image. For a given input image, knowing the target lens type, we render or transform the Bokeh effect accordingly to the lens properties. We build the BETD using two full-frame Sony cameras, and diverse lens *** the best of our knowledge, we are the first attempt to solve this novel task, and we provide the first BETD dataset and benchmark for it. The challenge had 99 registered participants. The submitted methods gauge the state-of-the-art in Bokeh effect rendering and transformation.

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VADANA: A dense dataset for facial image analysis

VADANA: A dense dataset for facial image analysis

引用

International conference on computer vision workshops (ICCV workshops)

作者： Gowri Somanath MV Rohith Chandra Kambhamettu Video/Image Modeling and Synthesis Lab University of Delaware USA Video/Image Modeling and Synthesis Lab Department of Computer and Information Sciences University of Delaware USA

Analysis of face images has been the topic of in-depth research with wide spread applications. Face recognition, verification, age progression studies are some of the topics under study. In order to facilitate comparison and benchmarking of different approaches, various datasets have been released. For the specific topics of face verification with age progression, aging pattern extraction and age estimation, only two public datasets are currently available. The FGNET and MORPH datasets contain a large number of subjects, but only a few images are available for each subject. We present a new dataset, VADANA, which complements them by providing a large number of high quality digital images for each subject within and across ages (depth vs. breadth). It provides the largest number of intrapersonal pairs, essential for better training and testing. The images also offer a natural range of pose, expression and illumination variation. A parallel version with aligned faces is also created. Additionally, we provide relationships between subjects. We demonstrate the difference and difficulty of VADANA by testing with state-of-the-art algorithms. Our findings from experiments show how VADANA can aid further research on different types of verification algorithms. The variety of characteristics our data offers facilitate testing and benchmarking of other facial analysis algorithms.

关键词： Face Training Lighting Databases Algorithm design and analysis Testing Aging

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Predicting Fall Probability Based on a Validated Balance Scale

Predicting Fall Probability Based on a Validated Balance Sca...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Alaa Masalha Nadav Eichler Shmuel Raz Adi Toledano-Shubi Daphna Niv Ilan Shimshoni Hagit Hel-Or Dept of Computer Science University of Haifa Dept of Information Systems University of Haifa Physiotherapy Institute Galilee Medical Center

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Accidental falls are the most frequent injury of old age and have dramatic implications on the individual, family, and the society as a whole. To date, fall prediction estimation is clinical, relying on the expertise of the physiotherapist for performing the diagnosis based on standard scales, such as the highly common and validated Berg Balance Scale (BBS). Unfortunately, the BBS is a time consuming subjective score, prone to variability and inconsistency between examiners. In this study, we developed an objective, computational tool, which automates the BBS fall assessment process and allows easy, efficient and accessible assessment of fall risk. The tool is based on a novel multi depth-camera human motion tracking system integrated with Machine Learning algorithms. The system enables large scale screening of the general public at very little cost while significantly reducing physiotherapist resources. The system was pilot tested in the physiotherapy unit at a major hospital and showed high rates of fall risk predictions as well as correlation with physiotherapists BBS scores on individual BBS motion tasks.

关键词： Task analysis Cameras Sensors Three-dimensional displays Calibration Medical diagnostic imaging Tools

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Reconstructing Occluded Surfaces Using Synthetic Apertures: Stereo, Focus and Robust Measures

Reconstructing Occluded Surfaces Using Synthetic Apertures: ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： V. Vaish M. Levoy R. Szeliski C.L. Zitnick Sing Bing Kang Computer Science Department University of Stanford CA USA Microsoft Research Limited Redmond WA USA

Most algorithms for 3D reconstruction from images use cost functions based on SSD, which assume that the surfaces being reconstructed are visible to all cameras. This makes it difficult to reconstruct objects which are partially occluded. Recently, researchers working with large camera arrays have shown it is possible to "see through" occlusions using a technique called synthetic aperture focusing. This suggests that we can design alternative cost functions that are robust to occlusions using synthetic apertures. Our paper explores this design space. We compare classical shape from stereo with shape from synthetic aperture focus. We also describe two variants of multi-view stereo based on color medians and entropy that increase robustness to occlusions. We present an experimental comparison of these cost functions on complex light fields, measuring their accuracy against the amount of occlusion.

关键词： Surface reconstruction Robustness Image reconstruction Focusing Cost function Cameras Space exploration Shape Stereo image processing Entropy

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Learning a manifold-constrained map between image sets: applications to matching and pose estimation

Learning a manifold-constrained map between image sets: appl...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jihun Ham Ikkjin Ahn D. Lee GRASP Laboratory University of Pennsylvania USA

This paper proposes a method for matching two sets of images given a small number of training examples by exploiting the underlying structure of the image manifolds. A nonlinear map from one manifold to another is constructed by combining linear maps locally defined on the tangent spaces of the manifolds. This construction imposes strong constraints on the choice of the maps, and makes possible good generalization of correspondences between all of the image sets. This map is flexible enough to approximate an arbitrary diffeomorphism between manifolds and can serve many purposes for applications. The underlying algorithm is a non-iterative efficient procedure whose complexity mainly depends on the number of matched training examples and the dimensionality of the manifold, and not on the number of samples nor on the dimensionality of the images. Several experiments were performed to demonstrate the potential of our method in image analysis and pose estimation. The first example demonstrates how images from a rotating camera can be mapped to the underlying pose manifold. Second, computer generated images from articulating toy figures are matched using the underlying 4 dimensional manifold to generate image-driven animations. Finally, two sets of actual lip images during speech are matched by their appearance manifold. In all these cases, our algorithm is able to obtain reasonable matches between thousands of large-dimensional images, with a minimum of computation.

关键词： Cameras Image analysis Image generation computer vision Robot vision systems Laboratories Animation Speech Lighting Application software

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A New Deformable Model for Boundary Tracking in Cardiac MRI and Its Application to the Detection of Intra-Ventricular Dyssynchrony

A New Deformable Model for Boundary Tracking in Cardiac MRI ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： P.F.U. Gotardo K.L. Boyer J. Saltz S.V. Raman Department of Electrical and Computer Engineering Ohio State Uinversity Columbus OH USA Department of Biomedical Informatics Ohio State Uinversity Columbus OH USA

We present a new deformable model technique following a snake-like approach and using a complex Fourier shape descriptors parameterization to efficiently formulate the forces that constrain contour deformation. The method was successfully applied to track the left ventricle’s (LV) endocardial and epicardial boundaries in sequences of shortaxis magnetic resonance images depicting complete cardiac cycles. The extracted shapes show the method’s robustness to weak contrast, noisy edge maps and to papillary muscle anatomy. Our second contribution is a statistical pattern recognition approach for the detection of asynchronous activation of the LV walls. We applied our deformable model method to provide spatio-temporal characterizations of complete cardiac cycles and then designed a linear classifier using the popular combination of Principal Component Analysis and Linear Discriminant Analysis. From a database comprising 33 patients, our approach provided a correct classification performance of 90.9% showing its potential in providing improved dyssynchrony characterization as an adjunct to current criteria for selecting patients for therapy, which provided a classification accuracy of just 62.5% on the same database.

关键词： Deformable models Magnetic resonance imaging Shape Databases Magnetic resonance Robustness Magnetic noise Noise shaping Image edge detection Muscles

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Situation Monitor: Diversity-Driven Zero-Shot Out-of-Distribution Detection using Budding Ensemble Architecture for Object Detection

Situation Monitor: Diversity-Driven Zero-Shot Out-of-Distrib...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Syed Sha Qutub Michael Paulitsch Kay-Ulrich Scholl Neslihan Kose Cihangir Korbinian Hagn Fabian Oboril Gereon Hinz Alois Knoll Intel Labs Munich Germany Technical University of Munich Munich Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce Situation Monitor, a novel zero-shot Out-of-Distribution (OOD) detection approach for transformer-based object detection models to enhance reliability in safety-critical machine learning applications such as autonomous driving. The Situation Monitor utilizes the Diversity-based Budding Ensemble Architecture (DBEA) and increases the OOD performance by integrating a diversity loss into the training process on top of the budding ensemble architecture, detecting Far-OOD samples and minimizing false positives on Near-OOD samples. Moreover, utilizing the resulting DBEA increases the model’s OOD performance and improves the calibration of confidence scores, particularly concerning the intersection over union of the detected objects. The DBEA model achieves these advancements with a 14% reduction in trainable parameters compared to the vanilla model. This signifies a substantial improvement in efficiency without compromising the model’s ability to detect OOD instances and calibrate the confidence scores accurately.

关键词： Training Adaptation models Zero-shot learning Computational modeling Scalability computer architecture Object detection

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Low Latency Point Cloud Rendering with Learned Splatting

Low Latency Point Cloud Rendering with Learned Splatting

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yueyu Hu Ran Gong Qi Sun Yao Wang Tandon School of Engineering New York University Tsinghua University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Point cloud is a critical 3D representation with many emerging applications. Because of the point sparsity and irregularity, high-quality rendering of point clouds is challenging and often requires complex computations to recover the continuous surface representation. On the other hand, to avoid visual discomfort, the motion-to-photon latency has to be very short, under 10 ms. Existing rendering solutions lack in either quality or speed. To tackle these challenges, we present a framework that unlocks interactive, free-viewing and high-fidelity point cloud rendering. We train a generic neural network to estimate 3D elliptical Gaussians from arbitrary point clouds and use differentiable surface splatting to render smooth texture and surface normal for arbitrary views. Our approach does not require per-scene optimization, and enable real-time rendering of dynamic point cloud. Experimental results demonstrate the proposed solution enjoys superior visual quality and speed, as well as generalizability to different scene content and robustness to compression artifacts.

关键词： Point cloud compression Visualization Three-dimensional displays Visual communication Neural networks Rendering (computer graphics) Robustness

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