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Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection

Back to the Feature: Classical 3D Features are (Almost) All ...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Horwitz, Eliahu Hoshen, Yedid The Hebrew University of Jerusalem School of Computer Science and Engineering Israel

ISBN: (纸本)9798350302493

Despite significant advances in image anomaly detection and segmentation, few methods use 3D information. We utilize a recently introduced 3D anomaly detection dataset to evaluate whether or not using 3D information is a lost opportunity. First, we present a surprising finding: standard color-only methods outperform all current methods that are explicitly designed to exploit 3D information. This is counter-intuitive as even a simple inspection of the dataset shows that color-only methods are insufficient for images containing geometric anomalies. This motivates the question: how can anomaly detection methods effectively use 3D information? We investigate a range of shape representations including hand-crafted and deep-learning-based;we demonstrate that rotation invariance plays the leading role in the performance. We uncover a simple 3D-only method that beats all recent approaches while not using deep learning, external pre-training datasets, or color information. As the 3D-only method cannot detect color and texture anomalies, we combine it with color-based features, significantly outperforming previous state-of-the-art. Our method, dubbed BTF (Back to the Feature) achieves pixel-wise ROCAUC: 99.3% and PRO: 96.4% on MVTec 3D-AD. © 2023 ieee.

关键词： Anomaly detection

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Spatial-Angular Multi-Scale Mechanism for Light Field Spatial Super-Resolution

Spatial-Angular Multi-Scale Mechanism for Light Field Spatia...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Gao, Chen Lin, Youfang Chang, Song Zhang, Shuo School of Computer and Information Technology Beijing Jiaotong University Beijing Key Lab of Traffic Data Analysis and Mining China

ISBN: (纸本)9798350302493

Light Field (LF) cameras are promising due to their ability to capture both spatial and angular information of scenes. However, the trade-off between spatial and angular resolution significantly limits the real-world applications. In this paper, we propose a spatial-angular multi-scale decoupling network to reconstruct high-resolution LF images. Considering the epipolar geometry, we propose a spatial-angular multi-scale processing approach to explore the correspondence of sub-pixel information with different disparity ranges between sub-aperture images in LFs. We extract sub-pixel information from various dimensions and fuse it to generate global high-frequency details. Finally, we combine upsampled low-frequency and high-frequency details to generate high resolution results. To further filter the correct interpolation information, we use the shear operation to change the disparity range of the LF images and fine-tune the results. Experimental results on synthetic and real-world datasets demonstrate that our method outperforms other state-of-the-art methods in visual and numerical evaluations, especially on datasets with small disparity ranges. Furthermore, our approach fully considers the epipolar geometry of the LF image, enabling us to recover information that better maintains the imaging consistency of the LF. © 2023 ieee.

关键词： Numerical methods

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Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural Network

Asynchronous Events-based Panoptic Segmentation using Graph ...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Kachole, Sanket Alkendi, Yusra Baghaei Naeini, Fariborz Makris, Dimitrios Zweiri, Yahya Kingston University Department of Computer Science London United Kingdom Abu Dhabi United Arab Emirates Khalifa University of Science and Technology Department of Aerospace Engineering Abu Dhabi United Arab Emirates Ipsotek An Eviden Company London United Kingdom

ISBN: (纸本)9798350302493

In the context of robotic grasping, object segmentation encounters several difficulties when faced with dynamic conditions such as real-time operation, occlusion, low lighting, motion blur, and object size variability. In response to these challenges, we propose the Graph Mixer Neural Network that includes a novel collaborative contextual mixing layer, applied to 3D event graphs formed on asynchronous events. The proposed layer is designed to spread spatiotemporal correlation within an event graph at four nearest neighbor levels parallelly. We evaluate the effectiveness of our proposed method on the Event-based Segmentation (ESD) Dataset, which includes five unique image degradation challenges, including occlusion, blur, brightness, trajectory, scale variance, and segmentation of known and unknown objects. The results show that our proposed approach outperforms state-of-the-art methods in terms of mean intersection over the union and pixel accuracy. Code available at: https://***/sanket0707/*** © 2023 ieee.

关键词： Image segmentation

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Improving Deep Learning-based Automatic Checkout System Using Image Enhancement Techniques

Improving Deep Learning-based Automatic Checkout System Usin...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Pham, Long Hoang Tran, Duong Nguyen-Ngoc Nguyen, Huy-Hung Jeon, Hyung-Joon Tran, Tai Huu-Phuong Jeon, Hyung-Min Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

ISBN: (纸本)9798350302493

The retail sector has experienced significant growth in artificial intelligence and computer vision applications, particularly with the emergence of automatic checkout (ACO) systems in stores and supermarkets. ACO systems encounter challenges such as object occlusion, motion blur, and similarity between scanned items while acquiring accurate training images for realistic checkout scenarios is difficult due to constant product updates. This paper improves existing deep learning-based ACO solutions by incorporating several image enhancement techniques in the data pre-processing step. The proposed ACO system employs a detect-and-track strategy, which involves: (1) detecting objects in areas of interest;(2) tracking objects in consecutive frames;and (3) counting objects using a track management pipeline. Several data generation techniques - including copy-and-paste, random placement, and augmentation - are employed to create diverse training data. Additionally, the proposed solution is designed as an open-ended framework that can be easily expanded to accommodate multiple tasks. The system has been evaluated on the AI City Challenge 2023 Track 4 dataset, showcasing outstanding performance by achieving a top-1 ranking on test-set A with an F1 score of 0.9792. © 2023 ieee.

关键词： Image enhancement

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Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation

Unsupervised domain adaptation and super resolution on drone...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Albert, Paul Saadeldin, Mohamed Narayanan, Badri Mac Namee, Brian Hennessy, Deirdre O'Connor, Noel E. McGuinness, Kevin Dublin City Univ Sch Elect Engn Dublin Ireland Univ Coll Dublin Sch Comp Sci Dublin Ireland Insight Ctr Data Analyt Dublin Ireland TEAGASC Dublin Ireland VistaMilk Dublin Ireland

ISBN: (纸本)9781665487399

Herbage mass yield and composition estimation is an important tool for dairy farmers to ensure an adequate supply of high quality herbage for grazing and subsequently milk production. By accurately estimating herbage mass and composition, targeted nitrogen fertiliser application strategies can be deployed to improve localised regions in a herbage field, effectively reducing the negative impacts of over-fertilization on biodiversity and the environment. In this context, deep learning algorithms offer a tempting alternative to the usual means of sward composition estimation, which involves the destructive process of cutting a sample from the herbage field and sorting by hand all plant species in the herbage. The process is labour intensive and time consuming and so not utilised by farmers. Deep learning has been successfully applied in this context on images collected by high-resolution cameras on the ground. Moving the deep learning solution to drone imaging, however, has the potential to further improve the herbage mass yield and composition estimation task by extending the ground-level estimation to the large surfaces occupied by fields/paddocks. Drone images come at the cost of lower resolution views of the fields taken from a high altitude and requires further herbage ground-truth collection from the large surfaces covered by drone images. This paper proposes to transfer knowledge learned on ground-level images to raw drone images in an unsupervised manner. To do so, we use unpaired image style translation to enhance the resolution of drone images by a factor of eight and modify them to appear closer to their ground-level counterparts. We then use the enhanced drone images to train a semi-supervised algorithm that uses ground-truthed, ground-level images as the labelled data together [GRAPHICS] with a large amount of unlabeled drone images. We validate our results on a small held-out drone image test set to show the validity of our approach, which opens the way f

关键词： Deep learning computer vision Image resolution Costs Estimation Prediction algorithms Cameras

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Variable Few Shot Class Incremental and OpenWorld Learning

Variable Few Shot Class Incremental and OpenWorld Learning

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Ahmad, Touqeer Dhamija, Akshay Raj Jafarzadeh, Mohsen Cruz, Steve Rabinowitz, Ryan Li, Chunchun Boult, Terrance E. Univ Colorado Vis & Secur Technol Lab Colorado Springs CO 80907 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Prior work on few-shot class incremental learning has operated with an unnatural assumption: the number of ways and number of shots are assumed to be known and fixed e.g., 10-ways 5-shots, 5-ways 5-shots, etc. Hence, we refer to this setting as Fixed-Few-Shot Class Incremental Learning (FFSCIL). In practice, the pre-specified fixed number of classes and examples per class may not be available, meaning one cannot update the model. Evaluation of FSCIL approaches in such unnatural settings renders their applicability questionable for practical scenarios where such assumptions do not hold. To mitigate the limitation of FFSCIL, we propose Variable-Few-Shot Class Incremental Learning (VFSCIL) and demonstrate it with Up-to N-Ways, Up-to K-Shots class incremental learning;wherein each incremental session, a learner may have up to N classes and up to K samples per class. Consequently, conventional FFSCIL is a special case of herein introduced VFSCIL. Further, we extend VFSCIL to a more practical problem of Variable-Few-Shot OpenWorld Learning (VFSOWL), where an agent is not only required to perform incremental learning, but must detect unknown samples and enroll only those that it detects correctly. We formulate and study VFSCIL and VFSOWL on two benchmark datasets conventionally employed for FFSCIL i.e., Caltech-UCSD Birds-200-2011 (CUB200) and miniImageNet. First, to serve as a strong baseline, we extend the state-of-the-art FSCIL approach to operate in Up-to N-Ways, Up-to K-Shots class incremental and open-world settings. Then, we propose a novel but simple approach for VFSCIL/VFSOWL where we leverage the current advancements in self-supervised feature learning. Utilizing both benchmark datasets, our proposed approach outperforms the strong baseline on the conventional FFSCIL setting and newly introduced VFSCIL/VFSOWL settings.

关键词： Representation learning computer vision Codes conferences Benchmark testing Feature extraction Power capacitors

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Learning to See in Nighttime Driving Scenes with Inter-frequency Priors

Learning to See in Nighttime Driving Scenes with Inter-frequ...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Fan, Zhentao Wu, Xianhao Chen, Xiang Li, Yufeng Shenyang Aerospace University College of Electronic and Information Engineering China Nanjing University of Science and Technology School of Computer Science and Engineering China

ISBN: (纸本)9798350302493

Currently, image-to-image translation methods have achieved significant performance with the help of deep CNNs and GANs, but most existing models are not suitable for autonomous driving scenarios due to their interpretability. In this paper, we develop a high-quality image translator (i.e. night → day), N2D-LPNet, to facilitate nighttime driving scene dperception. Instead of pursuing a complex network structure, we first attempt to explore the inter-frequency relation knowledge to simplify image translation process. Specifically, the lightweight Laplace pyramid is introduced as the backbone architecture to decompose the feature maps of nighttime image into high- and low-frequency components. Considering the similar morphological properties of different frequency components, we design a flexible inter-frequency guiding strategy which utilizes each lower frequency information to refine the higher frequency feature in a progressive manner. Benefiting from the advantage of constraint from inter-frequency priors, our method can process the nighttime image better under the practical driving system while still persevere competitive results. We also discuss the potential value of N2D-LPNet for other high-level vision tasks. © 2023 ieee.

关键词： Complex networks

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Content-Adaptive Downsampling in Convolutional Neural Networks

Content-Adaptive Downsampling in Convolutional Neural Networ...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Hesse, Robin Schaub-Meyer, Simone Roth, Stefan TU Darmstadt Department of Computer Science Germany Hessian.AI

ISBN: (纸本)9798350302493

Many convolutional neural networks (CNNs) rely on progressive downsampling of their feature maps to increase the network's receptive field and decrease computational cost. However, this comes at the price of losing granularity in the feature maps, limiting the ability to correctly understand images or recover fine detail in dense prediction tasks. To address this, common practice is to replace the last few downsampling operations in a CNN with dilated convolutions, allowing to retain the feature map resolution without reducing the receptive field, albeit increasing the computational cost. This allows to trade off predictive performance against cost, depending on the output feature resolution. By either regularly downsampling or not downsampling the entire feature map, existing work implicitly treats all regions of the input image and subsequent feature maps as equally important, which generally does not hold. We propose an adaptive downsampling scheme that generalizes the above idea by allowing to process informative regions at a higher resolution than less informative ones. In a variety of experiments, we demonstrate the versatility of our adaptive downsampling strategy and empirically show that it improves the cost-accuracy trade-off of various established CNNs. © 2023 ieee.

关键词： Convolution

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Towards Evaluating Explanations of vision Transformers for Medical Imaging

Towards Evaluating Explanations of Vision Transformers for M...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Komorowski, Piotr Baniecki, Hubert Biecek, Przemyslaw University of Warsaw Poland Warsaw University of Technology Poland

ISBN: (纸本)9798350302493

As deep learning models increasingly find applications in critical domains such as medical imaging, the need for transparent and trustworthy decision-making becomes paramount. Many explainability methods provide insights into how these models make predictions by attributing importance to input features. As vision Transformer (ViT) becomes a promising alternative to convolutional neural networks for image classification, its interpretability remains an open research question. This paper investigates the performance of various interpretation methods on a ViT applied to classify chest X-ray images. We introduce the notion of evaluating faithfulness, sensitivity, and complexity of ViT explanations. The obtained results indicate that Layerwise relevance propagation for transformers outperforms Local interpretable model-agnostic explanations and Attention visualization, providing a more accurate and reliable representation of what a ViT has actually learned. Our findings provide insights into the applicability of ViT explanations in medical imaging and highlight the importance of using appropriate evaluation criteria for comparing them. © 2023 ieee.

关键词： Decision making

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Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment

Enhancing Multi-Camera People Tracking with Anchor-Guided Cl...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Huang, Hsiang-Wei Yang, Cheng-Yen Jiang, Zhongyu Kim, Pyong-Kun Lee, Kyoungoh Kim, Kwangju Ramkumar, Samartha Mullapudi, Chaitanya Jang, In-Su Huang, Chung-I Hwang, Jenq-Neng University of Washington Information Processing Lab United States Electronics and Telecommunications Research Institute Korea Republic of National Center for High-Performance Computing Taiwan

ISBN: (纸本)9798350302493

Multi-camera multiple people tracking has become an increasingly important area of research due to the growing demand for accurate and efficient indoor people tracking systems, particularly in settings such as retail, healthcare centers, and transit hubs. We proposed a novel multi-camera multiple people tracking method that uses anchor-guided clustering for cross-camera re-identification and spatio-temporal consistency for geometry-based cross-camera ID reassigning. Our approach aims to improve the accuracy of tracking by identifying key features that are unique to every individual and utilizing the overlap of views between cameras to predict accurate trajectories without needing the actual camera parameters. The method has demonstrated robustness and effectiveness in handling both synthetic and real-world data. The proposed method is evaluated on CVPR AI City Challenge 2023 dataset, achieving IDF1 of 95.36% with the first-place ranking in the challenge. The code is available at: https://***/ipl-uw/AIC23-Track1-UWIPL-ETRI. © 2023 ieee.

关键词： computer vision

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