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Blind Non-Uniform Motion Deblurring using Atrous Spatial Pyramid Deformable Convolution and Deblurring-Reblurring Consistency

Blind Non-Uniform Motion Deblurring using Atrous Spatial Pyr...

引用

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

作者： Huo, Dong Masoumzadeh, Abbas Yang, Yee-Hong Univ Alberta Dept Comp Sci Edmonton AB Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Many deep learning based methods are designed to remove non-uniform (spatially variant) motion blur caused by object motion and camera shake without knowing the blur kernel. Some methods directly output the latent sharp image in one stage, while others utilize a multi-stage strategy (e.g. multi-scale, multi-patch, or multi-temporal) to gradually restore the sharp image. However, these methods have the following two main issues: 1) The computational cost of multi-stage is high;2) The same convolution kernel is applied in different regions, which is not an ideal choice for non-uniform blur. Hence, non-uniform motion deblurring is still a challenging and open problem. In this paper, we propose a new architecture which consists of multiple Atrous Spatial Pyramid Deformable Convolution (ASPDC) modules to deblur an image end-to-end with more flexibility. Multiple ASPDC modules implicitly learn the pixel-specific motion with different dilation rates in the same layer to handle movements of different magnitude. To improve the training, we also propose a reblurring network to map the deblurred output back to the blurred input, which constrains the solution space. Our experimental results show that the proposed method outperforms state-of-the-art methods on the benchmark datasets. The code is available at https://***/Dong-Huo/ASPDC.

关键词： Training Degradation Deep learning computer vision Convolution Image color analysis conferences

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Human vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scenes

Human Vision Based 3D Point Cloud Semantic Segmentation of L...

引用

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

作者： Yoo, Sunghwan Jeong, Yeonjeong Jameela, Maryam Sohn, Gunho York University Department of Earth and Space Science and Engineering TorontoONM3J 1P3 Canada

ISBN: (纸本)9798350302493

This paper proposes EyeNet, a novel semantic segmentation network for point clouds that addresses the critical yet often overlooked parameter of coverage area size. Inspired by human peripheral vision, EyeNet overcomes the limitations of conventional networks by introducing a simple but efficient multi-scale input and a parallel processing network with connection blocks between parallel streams. The proposed approach effectively addresses the challenges of dense point clouds, as demonstrated by our ablation studies and state-of-the-art performance on Large-Scale Outdoor datasets. © 2023 ieee.

关键词： Semantic Segmentation

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Learning to Ask Informative Sub-Questions for Visual Question Answering

Learning to Ask Informative Sub-Questions for Visual Questio...

引用

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

作者： Uehara, Kohei Duan, Nan Harada, Tatsuya Univ Tokyo Tokyo Japan Microsoft Res Asia Beijing Peoples R China Univ Tokyo RIKEN Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

VQA (Visual Question Answering) model tends to make incorrect inferences for questions that require reasoning over world knowledge. Recent study has shown that training VQA models with questions that provide lower-level perceptual information along with reasoning questions improves performance. Inspired by this, we propose a novel VQA model that generates questions to actively obtain auxiliary perceptual information useful for correct reasoning. Our model consists of a VQA model for answering questions, a Visual Question Generation (VQG) model for generating questions, and an Info-score model for estimating the amount of information the generated questions contain, which is useful in answering the original question. We train the VQG model to maximize the "informativeness" provided by the Info-score model to generate questions that contain as much information as possible, about the answer to the original question. Our experiments show that by in-putting the generated questions and their answers as additional information to the VQA model, it can indeed predict the answer more correctly than the baseline model.

关键词： Training Visualization computer vision Computational modeling conferences Reinforcement learning Predictive models

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Does Federated Dropout actually work?

Does Federated Dropout actually work?

引用

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

作者： Cheng, Gary Charles, Zachary Garrett, Zachary Rush, Keith Stanford Univ Stanford CA 94305 USA Google Res Mountain View CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Model sizes are limited in Federated Learning due to network bandwidth and on-device memory constraints. The success of increasing model sizes in other machine learning domains motivates the development of methods for training large-scale models in Federated Learning. To this end, [3] draws inspiration from dropout and proposes Federated Dropout: an algorithm where clients train randomly selected subsets of a larger server model. Despite the promising empirical results and the many other works that build on it [1, 8, 13], we argue in this paper that the metrics used to measure the performance of Federated Dropout and its variants are misleading. We propose and perform new experiments which suggest that Federated Dropout is actually detrimental to scaling efforts. We show how a simple ensembling technique outperforms Federated Dropout and other baselines. We perform ablations that suggest that the best performing variations of Federated Dropout approximate ensembling. The simplicity of ensembling allows for easy, practical implementations. Furthermore, ensembling naturally leverages the parallelizable nature of Federated Learning-recall that it is easy to train several models independently because there are a lot of clients and server-compute is not the bottleneck. Ensembling's strong performance against our baselines suggests that Federated Learning models may be more easily scaled than previously thought with more sophisticated ensembling strategies e.g., via boosting.

关键词： Training Measurement computer vision Machine learning algorithms conferences Computational modeling Memory management

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SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity recognition

SPARTAN: Self-supervised Spatiotemporal Transformers Approac...

引用

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

作者： Chappa, Naga V. S. Raviteja Nguyen, Pha Nelson, Alexander H. Seo, Han-Seok Li, Xin Dobbs, Page Daniel Luu, Khoa University of Arkansas United States West Virginia University United States

ISBN: (纸本)9798350302493

In this paper, we propose a new, simple, and effective Self-supervised Spatio-temporal Transformers (SPARTAN) approach to Group Activity recognition (GAR) using unlabeled video data. Given a video, we create local and global Spatio-temporal views with varying spatial patch sizes and frame rates. The proposed self-supervised objective aims to match the features of these contrasting views representing the same video to be consistent with the variations in spatiotemporal domains. To the best of our knowledge, the proposed mechanism is one of the first works to alleviate the weakly supervised setting of GAR using the encoders in video transformers. Furthermore, using the advantage of transformer models, our proposed approach supports long-term relationship modeling along spatio-temporal dimensions. The proposed SPARTAN approach performs well on two group activity recognition benchmarks, including NBA and Volleyball datasets, by surpassing the state-of-the-art results by a significant margin in terms of MCA and MPCA metrics1. © 2023 ieee.

关键词： computer vision

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Unmasking Your Expression: Expression-Conditioned GAN for Masked Face Inpainting

Unmasking Your Expression: Expression-Conditioned GAN for Ma...

引用

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

作者： Sola, Sridhar Gera, Darshan University of Birmingham Birmingham United Kingdom Sri Sathya Sai Institute of Higher Learning Bengaluru India

ISBN: (纸本)9798350302493

As face masks continue to be a part of our daily lives, the challenge of reconstructing occluded faces remains relevant. While several approaches have been proposed for removing masks from neutral facial images, few have explored the use of facial expressions as a dominant feature for reconstruction of expressive faces. To address this gap, we propose an expression-conditioned GAN (ECGAN) for reconstructing masked faces with a specified expression. Our approach leverages both the binary segmentation map of the mask and an expression label to generate high-quality images. To train our ECGAN in a supervised manner, we synthesize masked images using the RAFDB dataset to create non-masked-masked pairs of images for training. We evaluate of our approach on the RAFDB test set, demonstrating its effectiveness in generating realistic images that convincingly belong to the given expression class. This is further highlighted by comparing it to a baseline model and a state-of-the-art approach without expression-input. The code is available at https://***/SridharSola/ECGAN. © 2023 ieee.

关键词： computer vision

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PanoPoint: Self-Supervised Feature Points Detection and Description for 360° Panorama

PanoPoint: Self-Supervised Feature Points Detection and Desc...

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

作者： Zhang, Hengzhi Yi, Hong Jia, Haijing Wang, Wei Odamaki, Makoto Co. Ltd. Beijing China Ricoh Company Ltd. Japan

ISBN: (纸本)9798350302493

We introduce PanoPoint, the joint feature point detection and description applied to the nonlinear distortions and the multi-view geometry problems between 360° panoramas. Our fully convolutional model operates directly in panoramas and computes pixel-level feature point locations and associated descriptors in a single forward pass rather than performing image preprocessing (e.g. panorama to Cubemap) followed by feature detection and description. To train the PanoPoint model, we propose PanoMotion, which simulates the representation between different viewpoints and generates warped panoramas. Moreover, we propose PanoMotion Adaptation, a multi-viewpoint adaptation annotation approach for boosting feature point detection repeatability instead of manual labelling. We train on the annotated synthetic dataset generated by the above method, which outperforms the traditional and other learned approaches and achieves state-of-the-art results on repeatability, localization accuracy, point correspondence precision and real-time metrics, especially for panoramas with significant viewpoint and illumination changes. © 2023 ieee.

关键词： computer vision

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Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning

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

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

作者： Becking, Daniel Kirchhoffer, Heiner Tech, Gerhard Haase, Paul Mueller, Karsten Schwarz, Heiko Samek, Wojciech Fraunhofer Heinrich Hertz Inst HHI Berlin Germany

ISBN: (纸本)9781665487399

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 377x.

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

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Deep density estimation based on multi-spectral remote sensing data for in-field crop yield forecasting

Deep density estimation based on multi-spectral remote sensi...

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

作者： Baghdasaryan, Liana Melikbekyan, Razmik Dolmajain, Arthur Hobbs, Jennifer Intelinair Inc Yerevan Armenia Intelinair Inc Chicago IL USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Yield forecasting has been a central task in computational agriculture because of its impact on agricultural management from the individual farmer to the government level. With advances in remote sensing technology, computational processing power, and machine learning, the ability to forecast yield has improved substantially over the past years. However, most previous work has been done leveraging low-resolution satellite imagery and forecasting yield at the region, county, or occasionally farm-level. In this work, we use high-resolution aerial imagery and output from high-precision harvesters to predict in-field harvest values for corn-raising farms in the US Midwest. By using the harvester information, we are able to cast the problem of yield-forecasting as a density estimation problem and predict a harvest rate, in bushels/acre, at every pixel in the field image. This approach provides the farmer with a detailed view of which areas of the farm may be performing poorly so he can make the appropriate management decisions in addition to providing an improved prediction of total yield. We evaluate both traditional machine learning approaches with hand-crafted features alongside deep learning methods. We demonstrate the superiority of our pixel-level approach based on an encoder-decoder framework which produces a 5.41% MAPE at the field-level.

关键词： Deep learning Satellites conferences Government Estimation Crops pattern recognition

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VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolution

VFHQ: A High-Quality Dataset and Benchmark for Video Face Su...

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

作者： Xie, Liangbin Wang, Xintao Zhang, Honglun Dong, Chao Shan, Ying Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Comp Vis & Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Tencent PCG ARC Lab Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Most of the existing video face super-resolution (VFSR) methods are trained and evaluated on VoxCeleb1, which is designed specifically for speaker identification and the frames in this dataset are of low quality. As a consequence, the VFSR models trained on this dataset can not output visual-pleasing results. In this paper, we develop an automatic and scalable pipeline to collect a high-quality video face dataset (VFHQ), which contains over 16, 000 high-fidelity clips of diverse interview scenarios. To verify the necessity of VFHQ, we further conduct experiments and demonstrate that VFSR models trained on our VFHQ dataset can generate results with sharper edges and finer textures than those trained on VoxCeleb1. In addition, we show that the temporal information plays a pivotal role in eliminating video consistency issues as well as further improving visual performance. Based on VFHQ, by analyzing the benchmarking study of several state-of-the-art algorithms under bicubic and blind settings.

关键词： Training Visualization Privacy Face recognition Superresolution Pipelines Benchmark testing

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