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Did: Dimension-Invariant Disentangling Model for Light Field Spatial Super-Resolution

Did: Dimension-Invariant Disentangling Model for Light Field...

引用

2023 ieee International conference on Image Processing Challenges and workshops, ICIPCW 2023

作者： Bao, Yuxin Qi, Na Zhu, Qing Xu, Wei Beijing University of Technology Faculty of Information Technology Beijing100124 China Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing100124 China

ISBN: (纸本)9798350302585

4D light field (LF) images can be encoded from 3D scenes by LF cameras, have highly coupled spatial and angular information and cannot be directly utilized. To solve this problem, a disentangling mechanism is proposed in the DistgSSR method [1]. Despite delivering encouraging results of DistgSSR, the parameters of different feature extractors in the Distg-Block of DistgSSR are manually tweaked. Moreover, the concatenated features generated by different extractors should be fused by two convolutions, which will consume unnecessary resources. To address this issue, we propose a dimension-invariant disentangling (DiD) model, which is a plug-and-play model and can adaptively allocate the dimension of the features to fit the residual-in-residual structure. The computational complexity, GPU_Memory and FLOPs of our DiD model are further reduced without losing performance compared with the DistgSSR. Furthermore, our DiD model can be adapted to features of different sizes and may be applied to some networks for other tasks. To further improve the reconstruction result, we integrate a multi-stage spectral dimensional Transformer (MST++) into the DiD model to propose the DiD-MST++ for LF super-resolution. Extensive experimental results on five public LF datasets demonstrate that the DiD and DiD-MST++ can outperform state-of-the-art methods in terms of objective and subjective quality. © 2023 ieee.

关键词： computer vision

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PI-Net: A Deep Learning Approach to Extract Topological Persistence Images

PI-Net: A Deep Learning Approach to Extract Topological Pers...

引用

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

作者： Som, Anirudh Choi, Hongjun Ramamurthy, Karthikeyan Natesan Buman, Matthew P. Turaga, Pavan Arizona State Univ Sch Arts Media & Engn Tempe AZ 85287 USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA IBM Res Albany NY USA Arizona State Univ Coll Hlth Solut Tempe AZ 85287 USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Topological features such as persistence diagrams and their functional approximations like persistence images (PIs) have been showing substantial promise for machine learning and computer vision applications. This is greatly attributed to the robustness topological representations provide against different types of physical nuisance variables seen in real-world data, such as view-point, illumination, and more. However, key bottlenecks to their large scale adoption are computational expenditure and difficulty incorporating them in a differentiable architecture. We take an important step in this paper to mitigate these bottlenecks by proposing a novel one-step approach to generate PIs directly from the input data. We design two separate convolutional neural network architectures, one designed to take in multi-variate time series signals as input and another that accepts multi-channel images as input. We call these networks Signal PI-Net and Image PI-Net respectively. To the best of our knowledge, we are the first to propose the use of deep learning for computing topological features directly from data. We explore the use of the proposed PI-Net architectures on two applications: human activity recognition using tri-axial accelerometer sensor data and image classification. We demonstrate the ease of fusion of PIs in supervised deep learning architectures and speed up of several orders of magnitude for extracting PIs from data. Our code is available at https://***/anirudhsom/PI-Net.

关键词： Machine learning computer architecture computer vision Task analysis Topology Feature extraction Data models

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Few-Shot Supervised Prototype Alignment for Pedestrian Detection on Fisheye Images

Few-Shot Supervised Prototype Alignment for Pedestrian Detec...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Thaddä us Wiedemer Stefan Wolf Arne Schumann Kaisheng Ma Jü rgen Beyerer Tsinghua University Karlsruhe Institute of Technology Fraunhofer IOSB Fraunhofer Center for Machine Learning

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Complete and pre-trained models are readily available for download for object detection and can perform well on datasets containing everyday images. Domain adaptation is used to transfer models to more specific datasets with characteristics not present in pre-training. We propose the novel adaptation setting of pedestrian detection in fisheye images, where target samples are scarce but annotated. Our setting provides interesting new challenges for adaptation due to global perspective changes and geometric distortions not found in existing adaptation tasks. To this end, we introduce loss coupling for unsupervised adversarial adaptation and boost prototype-based adaptation with ground-truth information. We additionally propose a novel supervised adaptation head for features in the bounding box regressor. Our method leads to more stable adversarial training and outperforms supervised and unsupervised baselines. Our method requires half the amount of training samples for small datasets to achieve the same performance as supervised fine-tuning.

关键词： Training Couplings Adaptation models Schedules Surveillance Prototypes computer architecture

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Augmentation of Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models

Augmentation of Atmospheric Turbulence Effects on Thermal Ad...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Engin Uzun Ahmet Anı l Dursun Erdem Akagü ndü z Dept. of Image Proc. &amp Computer Vis. Technologies Aselsan Inc. Turkey Graduate School of Informatics Middle East Technical University Turkey

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Atmospheric turbulence has a degrading effect on the image quality of long-range observation systems. As a result of various elements such as temperature, wind velocity, humidity, etc., turbulence is characterized by random fluctuations in the refractive index of the atmosphere. It is a phenomenon that may occur in various imaging spectra such as the visible or the infrared bands. In this paper, we analyze the effects of atmospheric turbulence on object detection performance in thermal imagery. We use a geometric turbulence model to simulate turbulence effects on a medium-scale thermal image set, namely "FLIR ADAS v2". We apply thermal domain adaptation to state-of-the-art object detectors and propose a data augmentation strategy to increase the performance of object detectors which utilizes turbulent images in different severity levels as training data. Our results show that the proposed data augmentation strategy yields an increase in performance for both turbulent and non-turbulent thermal test images.

关键词： Adaptation models Atmospheric modeling Wind speed Training data Refractive index Imaging Object detection

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Modulating Bottom-Up and Top-Down Visual Processing via Language-Conditional Filters

Modulating Bottom-Up and Top-Down Visual Processing via Lang...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ilker Kesen Ozan Arkan Can Erkut Erdem Aykut Erdem Deniz Yü ret KUIS AI Center Ko&#x00E7 University Computer Engineering Department Ko&#x00E7 Amazon Search Computer Engineering Department Hacettepe University

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

How to best integrate linguistic and perceptual processing in multi-modal tasks that involve language and vision is an important open problem. In this work, we argue that the common practice of using language in a top-down manner, to direct visual attention over high-level visual features, may not be optimal. We hypothesize that the use of language to also condition the bottom-up processing from pixels to high-level features can provide benefits to the overall performance. To support our claim, we propose a U-Net-based model and perform experiments on two language-vision dense-prediction tasks: referring expression segmentation and language-guided image colorization. We compare results where either one or both of the top-down and bottom-up visual branches are conditioned on language. Our experiments reveal that using language to control the filters for bottom-up visual processing in addition to top-down attention leads to better results on both tasks and achieves competitive performance. Our linguistic analysis suggests that bottom-up conditioning improves segmentation of objects especially when input text refers to low-level visual concepts. Code is available at https://***/ilkerkesen/bvpr.

关键词： Visualization Image segmentation Image color analysis Grounding Computational modeling Process control Predictive models

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The Art of Managing Flaky Tests at Scale 18

The Art of Managing Flaky Tests at Scale

引用

18th ieee International conference on Software Testing, Verification and Validation workshops, ICSTW 2025

作者： Andreyev, Oleg Okta Inc. United States

ISBN: (纸本)9798331534677

Test flakiness, characterized by non-deterministic test outcomes despite unchanged code, poses a significant challenge to continuous integration system efficiency and developer productivity. We comprehensively study a year-long initiative to mitigate test flakiness within a large-scale engineering organization. We adjust test failure sensitivity thresholds to account for alert fatigue and resolution quality, refocus data visualization for prioritization and pattern analysis, and introduce incentives to improve the rate of solutions contributing to test stabilization. Key findings reveal the importance of balancing technical efficiency gains with organizational dynamics. © 2025 ieee.

关键词： computer software

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Complete and temporally consistent video outpainting

Complete and temporally consistent video outpainting

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Loï c Dehan Wiebe Van Ranst Patrick Vandewalle Toon Goedemé EAVISE-PSI-ESAT KU Leuven Sint-Katelijne-Waver Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

We describe a novel method for video outpainting. The goal of outpainting is to fill in missing regions at the edges of video frames. Our focus lies on converting portrait (9:16) to landscape (16:9) video. In contrast, most video completion research is focused on inpainting: filling a masked section within the frame based on the remaining, known *** proposed method consists of three main aspects: (1) We form a background estimation using video object segmentation and video inpainting methods, (2) we use optical flow to form temporal consistency, and (3) we propose image shifting to improve individual frame completions. Our method is able to successfully broaden the aspect ratio of a video. On most videos, we achieve realistic results. Only on videos with complex camera motion and foreground objects leaving the frame, the quality is *** contrast to other state-of-the-art methods, our method is able to reconstruct the full frame, including unseen image parts. Moreover, it is temporally consistent. We evaluate our method on the DAVIS and YouTube-VOS datasets. The code is publicly available 1 .

关键词： Measurement computer vision Image edge detection conferences Estimation Object segmentation Filling

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Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning

Adaptive Differential Filters for Fast and Communication-Eff...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Daniel Becking Heiner Kirchhoffer Gerhard Tech Paul Haase Karsten Mü ller Heiko Schwarz Wojciech Samek Fraunhofer Heinrich Hertz Institute (HHI) Berlin

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Federated learning (FL) scenarios inherently generate a large communication overhead by frequently transmitting neural network updates between clients and server. To minimize the communication cost, introducing sparsity in conjunction with differential updates is a commonly used technique. However, sparse model updates can slow down convergence speed or unintentionally skip certain update aspects, e.g., learned features, if error accumulation is not properly addressed. In this work, we propose a new scaling method operating at the granularity of convolutional filters which 1) compensates for highly sparse updates in FL processes, 2) adapts the local models to new data domains by enhancing some features in the filter space while diminishing others and 3) motivates extra sparsity in updates and thus achieves higher compression ratios, i.e., savings in the overall data transfer. Compared to unscaled updates and previous work, experimental results on different computer vision tasks (Pascal VOC, CIFAR10, Chest X-Ray) and neural networks (ResNets, MobileNets, VGGs) in uni-, bidirectional and partial update FL settings show that the proposed method improves the performance of the central server model while converging faster and reducing the total amount of transmitted data by up to 377×.

关键词： computer vision Adaptation models Computational modeling Pipelines Neural networks Collaborative work Data models

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Slimmable Video Codec

Slimmable Video Codec

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zhaocheng Liu Luis Herranz Fei Yang Saiping Zhang Shuai Wan Marta Mrak Marc Gó rriz Blanch School of Electronics and Information Northwestern Polytechnical University Xi&#x2019 an China Computer Vision Center Universitat Autonoma de Barcelona Barcelona Spain State Key Laboratory of Integrated Services Networks Xidian University Xi&#x2019 The Lighthouse White City Place 201 Wood Lane BBC Research &#x0026 Development London UK

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Neural video compression has emerged as a novel paradigm combining trainable multilayer neural net-works and machine learning, achieving competitive rate-distortion (RD) performances, but still remaining impractical due to heavy neural architectures, with large memory and computational demands. In addition, models are usually optimized for a single RD tradeoff. Recent slimmable image codecs can dynamically adjust their model capacity to gracefully reduce the memory and computation requirements, without harming RD performance. In this paper we propose a slimmable video codec (SlimVC), by integrating a slimmable temporal entropy model in a slimmable autoencoder. Despite a significantly more complex architecture, we show that slimming remains a powerful mechanism to control rate, memory footprint, computational cost and latency, all being important requirements for practical video compression.

关键词： Computational modeling Memory management Memory architecture Rate-distortion Machine learning Video compression Nonhomogeneous media

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Trust Your IMU: Consequences of Ignoring the IMU Drift

Trust Your IMU: Consequences of Ignoring the IMU Drift

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Marcus Valtonen Ö rnhag Patrik Persson Må rten Wadenbä ck Kalle Å strö m Anders Heyden Centre for Mathematical Sciences Lund University Department of Electrical Engineering Link&#x00F6 ping University

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals. This allows us to consider a simplified camera model, which in turn admits further intrinsic calibration. We develop the first-ever solver to jointly solve the relative pose problem with unknown and equal focal length and radial distortion profile while utilizing the IMU data. Furthermore, we show significant speed-up compared to state-of-the-art algorithms, with small or negligible loss in accuracy for partially calibrated *** proposed algorithms are tested on both synthetic and real data, where the latter is focused on navigation using unmanned aerial vehicles (UAVs). We evaluate the proposed solvers on different commercially available low-cost UAVs, and demonstrate that the novel assumption on IMU drift is feasible in real-life applications. The extended intrinsic auto-calibration enables us to use distorted input images, making tedious calibration processes obsolete, compared to current state-of-the-art methods. Code available at: https://***/marcusvaltonen/DronePoseLib. 1

关键词： computer vision Measurement units Codes conferences Inertial navigation Distortion Real-time systems

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