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检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

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Deep Constrained Least Squares for Blind Image Super-Resolution

Deep Constrained Least Squares for Blind Image Super-Resolut...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Luo, Ziwei Huang, Haibin Yu, Lei Li, Youwei Fan, Haoqiang Liu, Shuaicheng Megvii Technol Beijing Peoples R China Kuaishou Technol Beijing Peoples R China Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we tackle the problem of blind image superresolution(SR) with a reformulated degradation model and two novel nwdules. Following the common practices of blind SR, our method proposes to improve both the kernel estimation as well as the kernel based high resolution image restoration. To be more specific, we first reformulate the degradation model such that the deblurring kernel estimation can be transferred into the low resolution space. On top of this, we introduce a dynamic deep linear filter module. Instead of learning a fixed kernel for all images, it can adaptively generate deblurring kernel weights conditional on the input and yields more robust kernel estimation. Subsequently, a deep constrained least square filtering module is applied to generate clean features based on the reformulation and estimated kernel. The deblurred feature and the low input image feature are then fed into a dual-path structured SR network and restore the final high resolution result. To evaluate our method, we further conduct evaluations on several benchmarks, including Gaussian8 and DIV2KRK. Our experiments demonstrate that the proposed method achieves better accuracy and visual improvements against state-of-the-art methods.

关键词： Degradation Photography visualization Image resolution Superresolution Estimation Nonlinear filters

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Identifying Obviously Artificial Medical Images Produced by a Generative Adversarial Network 44

Identifying Obviously Artificial Medical Images Produced by ...

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44th Annual International conference of the ieee-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： O'Reilly, Jamie A. Asadi, Fawad Rangsit Univ Coll Biomed Engn Pathum Thani 12130 Thailand

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Synthetic medical images have an important role to play in developing data-driven medical image processing systems. Using a relatively small amount of patient data to train generative models that can produce an abundance of additional samples could bridge the gap towards big-data in niche medical domains. These generative models are evaluated in terms of the synthetic data they generate using the Visual Turing Test (VTT), Frechet Inception Distance (FID), and other metrics. However, these are generally interpreted at the group level, and do not measure the artificiality of individual synthetic images. The present study attempts to address the challenge of automatically identifying artificial images that are obviously-artificial-looking, which may be necessary for filtering out poorly constructed synthetic images that might otherwise deteriorate the performance of assimilating systems. Synthetic computed tomography (CT) images from a progressively-grown generative adversarial network (PGGAN) were evaluated with a VTT and their image embeddings were analyzed for correlation with artificiality. Images categorized as obviously-artificial (>= 0.7 probability of being rated as fake) were classified using a battery of algorithms. The topperforming classifier, a support vector machine, exhibited accuracy of 75.5%, sensitivity of 0.743, and specificity of 0.769. This is an encouraging result that suggests a potential approach for validating synthetic medical image datasets.

关键词： Support vector machines visualization Correlation Sensitivity Filtering Computed tomography Generative adversarial networks

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UTC: A Unified Transformer with Inter-Task Contrastive Learning for Visual Dialog

UTC: A Unified Transformer with Inter-Task Contrastive Learn...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chen, Cheng Tan, Zhenshan Cheng, Qingrong Jiang, Xin Liu, Qun Zhu, Yudong Gu, Xiaodong Fudan Univ Dept Elect Engn Shanghai Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Visual Dialog aims to answer multi-round, interactive questions based on the dialog history and image content. Existing methods either consider answer ranking and generating individually or only weakly capture the relation across the two tasks implicitly by two separate models. The research on a universal framework that jointly learns to rank and generate answers in a single model is seldom explored. In this paper, we propose a contrastive learning-based framework UTC to unify and facilitate both discriminative and generative tasks in visual dialog with a single model. Specifically, considering the inherent limitation of the previous learning paradigm, we devise two inter-task contrastive losses i.e., context contrastive loss and answer contrastive loss to make the discriminative and generative tasks mutually reinforce each other. These two com-plementary contrastive losses exploit dialog context and target answer as anchor points to provide representation learning signals from different perspectives. We evaluate our proposed UTC on the VisDial v1.0 dataset, where our method outperforms the state-of-the-art on both discriminative and generative tasks and surpasses previous state-of-the-art generative methods by more than 2 absolute points on Recall@1.

关键词： Training Representation learning visualization Computer vision Correlation Transformers Pattern recognition

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VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution

VideoINR: Learning Video Implicit Neural Representation for ...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chen, Zeyuan Chen, Yinbo Liu, Jingwen Xu, Xingqian Goel, Vidit Wang, Zhangyang Shi, Humphrey Wang, Xiaolong USTC Hefei Peoples R China Univ Calif San Diego La Jolla CA 92093 USA UIUC Champaign IL 61820 USA UT Austin Austin TX USA Univ Oregon Eugene OR 97403 USA Picsart AI Res PAIR Champaign IL 61820 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Videos typically record the streaming and continuous visual data as discrete consecutive frames. Since the storage cost is expensive for videos of high fidelity, most of them are stored in a relatively low resolution and frame rate. Recent works of Space-Time Video Super-Resolution (STVSR) are developed to incorporate temporal interpolation and spatial super-resolution in a unified framework. However;most of them only support a fixed upsampling scale, which limits their flexibility and applications. In this work, instead of following the discrete representations, we propose Video Implicit Neural Representation (VideoINR), and we show its applications for STVSR. The learned implicit neural representation can be decoded to videos of arbitrary spatial resolution and frame rate. We show that VideoINR achieves competitive performances with state-of-the-art STVSR methods on common up-sampling scales and significantly outperforms prior works on continuous and out-of-training-distribution scales. Our project page is at here and code is available at https://***/Picsart-AI-Research/VideoINR-Continuous-Space-Tune-Super-Resolution.

关键词： visualization Interpolation Computer vision Costs Codes Superresolution Pattern recognition

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Deep Visual Geo-localization Benchmark

Deep Visual Geo-localization Benchmark

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Berton, Gabriele Mereu, Riccardo Trivigno, Gabriele Masone, Carlo Csurka, Gabriela Sattler, Torsten Caputo, Barbara Politecn Torino Turin Italy CINI Turin Italy NAVER LABS Europe Meylan France Czech Tech Univ CIIRC Prague Czech Republic

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we propose a new open-source benchmarking framework for Visual Geo-localization (VG) that allows to build, train, and test a wide range of commonly used architectures, with the flexibility to change individual components of a geo-localization pipeline. The purpose of this framework is twofold: i) gaining insights into how different components and design choices in a VG pipeline impact the final results, both in terms of performance (recall@N metric) and system requirements (such as execution time and memory consumption);ii) establish a systematic evaluation protocol for comparing different methods. Using the proposed framework, we perform a large suite of experiments which provide criteria for choosing backbone, aggregation and negative mining depending on the use-case and requirements. We also assess the impact of engineering techniques like pre/post-processing, data augmentation and image resizing, showing that better performance can be obtained through somewhat simple procedures: for example, downscaling the images' resolution to 80% can lead to similar results with a 36% savings in extraction time and dataset storage requirement. Code and trained models are available at dataset storage requirement. https://deep-vg- ***/.

关键词： Measurement visualization Computer vision Protocols Image resolution Computational modeling Pipelines

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Visual Vibration Tomography: Estimating Interior Material Properties from Monocular Video

Visual Vibration Tomography: Estimating Interior Material Pr...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Feng, Berthy T. Ogren, Alexander C. Daraio, Chiara Bouman, Katherine L. CALTECH Pasadena CA 91125 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

An object's interior material properties, while invisible to the human eve, determine motion observed on its surface. We propose an approach that estimates heterogeneous material properties of an object from a monocular video of its surface vibrations. Specifically, we show how to estimate Young's modulus and density throughout a 3D object with known geometry. Knowledge of how these values change across the object is useful for simulating its motion and characterizing any defects. Traditional nondestructive testing approaches, which often require expensive instruments, generally estimate only homogenized material properties or simply identify the presence of defects. In contrast, our approach leverages monocular video to (1) identify image-space modes from an object's sub-pixel motion, and (2) directly infer spatially-varying Young's modulus and density values from the observed modes. We demonstrate our approach on both simulated and real videos.

关键词： Vibrations Young's modulus visualization Three-dimensional displays Tomography Sensor phenomena and characterization Turning

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Detection of Distorted Points on Images of Micro-Objects Based on The Properties and Peculiarities of the Wavelet - Transformation

Detection of Distorted Points on Images of Micro-Objects Bas...

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2022 International Russian Automation conference, RusAutoCon 2022

作者： Jumanov, Isroil I. Safarov, Rustam A. Djumanov, Olimjon I. Samarkand State University Digital Technologies Samarkand Uzbekistan

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

Scientific and methodological foundations for the identification of images of unknown objects with the allocation of local areas of objects of interest, the use of traditional Gaussian, median filtering, fast Fourier transform, cosine transform, wavelet transforms, shift transforms, segmentation, factorization based on the use of redundancy of information structural components and their features. Mechanisms for detecting and correcting distorted points in fragment pictures have been developed based on the use of statistical, dynamic and neural network models. A wide range of mechanisms for filtering, segmentation, factorization, recognition and classification of images of non-stationary objects in conditions of a priori insufficiency, parametric uncertainty, and low reliability of information have been investigated and implemented. Operators of wavelet, shift and other transformations, color coding, visualization of objects with regulation of their formalized parameters are investigated. Tested realizations of the generalized mechanism of identification, recognition and classification of pictures of a human brain tumor have been tested, in particular, unicellular elements of the brain with and without pathology have been investigated. A software package for visualization, recognition, classification of images of unknown objects has been developed, the are carried out of which are carried out in conditions of a priori insufficiency, parametric uncertainty, and low reliability of information. © 2022 ieee.

关键词： information filtering

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Hypergraph-Induced Semantic Tuplet Loss for Deep Metric Learning

Hypergraph-Induced Semantic Tuplet Loss for Deep Metric Lear...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Lim, Jongin Yun, Sangdoo Park, Seulki Choi, Jin Young Seoul Natl Univ Dept ECE ASRI Seoul South Korea Samsung Adv Inst Technol Giheung South Korea NAVER AI Lab Seongnam Si South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we propose Hypergraph-Induced Semantic Tuplet (HIST) loss for deep metric learning that leverages the multilateral semantic relations of multiple samples to multiple classes via hypergraph modeling. We formulate deep metric learning as a hypergraph node classification problem in which each sample in a mini-batch is regarded as a node and each hyperedge models class-specific semantic relations represented by a semantic tuplet. Unlike previous graph-based losses that only use a bundle of pairwise relations, our HIST loss takes advantage of the multilateral semantic relations provided by the semantic tuplets through hypergraph modeling. Notably, by leveraging the rich multilateral semantic relations, HIST loss guides the embedding model to learn class-discriminative visual semantics, contributing to better generalization performance and model robustness against input corruptions. Extensive experiments and ablations provide a strong motivation for the proposed method and show that our HIST loss leads to improved feature learning, achieving state-of-the-art results on three widely used benchmarks. Code is available at https://***/ljin0429/HIST.

关键词： Measurement Representation learning visualization Computational modeling Semantics Neural networks Benchmark testing

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Balanced MSE for Imbalanced Visual Regression

Balanced MSE for Imbalanced Visual Regression

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ren, Jiawei Zhang, Mingyuan Yu, Cunjun Liu, Ziwei Nanyang Technol Univ S Lab Singapore Singapore Natl Univ Singapore Sch Comp Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Data imbalance exists ubiquitously in real-world visual regressions, e.g., age estimation and pose estimation, hurting the model's generalizability and fairness. Thus, imbalanced regression gains increasing research attention recently. Compared to imbalanced classification, imbalanced regression focuses on continuous labels, which can be boundless and high-dimensional and hence more challenging. In this work, we identify that the widely used Mean Square Error (MSE) loss function can be ineffective in imbalanced regression. We revisit MSE from a statistical view and propose a novel loss function, Balanced MSE, to accommodate the imbalanced training label distribution. We further design multiple implementations of Balanced MSE to tackle different real-world scenarios, particularly including the one that requires no prior knowledge about the training label distribution. Moreover, to the best of our knowledge, Balanced MSE is the first general solution to high-dimensional imbalanced regression in modern context. Extensive experiments on both synthetic and three real-world benchmarks demonstrate the effectiveness of Balanced MSE. Code and models are available at ***/jiawei-ren/BalancedMSE.

关键词： Training visualization Computer vision Codes Computational modeling Pose estimation Mean square error methods

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Advance in Researches of proprioception:A Bibliometrics and visualization Study 10

Advance in Researches of proprioception:A Bibliometrics and ...

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10th International conference on Orange Technology, ICOT 2022

作者： Chen, Yiyi Yu, Bo He, Fei Sun, Wenjiang Chen, Wenhua School of International Medical Technology Shanghai Sanda University Department of Rehabilitation China Shanghai General Hospital Affiliated to Shanghai Jiaotong University of Medicine Department of Rehabilitation China

ISBN: (纸本)9781665454193

This paper reports an analysis of the bioinformatics and medical informatics literature with the objective to identify upcoming trends that are shared among both research fields to derive benefits on proprioception for their future. This paper summarizes global research hot topics of proprioception recently based on qualitative evidence from bibliometrics. The research of proprioception is becoming more mature. Domestic scholars can further study proprioception in vision, balance, and other field, and constantly improve the research level in this field in China. © 2022 ieee.

关键词： visualization

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