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NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results

NTIRE 2023 Challenge on Light Field Image Super-Resolution: ...

引用

2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany Eth Zürich Switzerland Sun Yat-sen University The Shenzhen Campus of Sun Yat-sen University China Bigo Technology Pte. Ltd. Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

ISBN: (纸本)9798350302493

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023 IEEE.

关键词： Image enhancement

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QuantNAS: Quantization-aware Neural Architecture Search For Efficient Deployment On Mobile Device

QuantNAS: Quantization-aware Neural Architecture Search For ...

引用

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

作者： Tianxiao Gao Li Guo Shanwei Zhao Peihan Xu Yukun Yang Xionghao Liu Shihao Wang Shiai Zhu Dajiang Zhou Ant Group

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep convolutional networks are increasingly applied in mobile AI scenarios. To achieve efficient deployment, researchers combine neural architecture search (NAS) and quantization to find the best quantized architecture. However, existing methods overlook the on-device implementation of quantization. The searching result is usually sub-optimal or has limited latency reduction. To this end, we propose QuantNAS, a novel quantization-aware NAS based on a two-stage one-shot method. Different from the previous method, our method considers the on-device implementation of the quantized network and searches for the architecture from a fully quantized supernet. During training, we propose a batch-statistics-based strategy to alleviate the non-convergence problem. Besides, a scale predictor is proposed and is jointly trained with the supernet. During search, the scale predictor can provide optimal scale for different subnets without retraining. At different latency levels on Kirin 9000 mobile CPU, the proposed method achieves 1.53%-1.68% Top-1 accuracy improvement on ImageNet 1K dataset and 1.7% mAP improvement.

关键词： Training computer vision Quantization (signal) conferences Computational modeling computer architecture Mobile handsets

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A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms

A Comparison and Evaluation of Multi-View Stereo Reconstruct...

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conference on computer vision and pattern recognition (CVPR)

作者： S.M. Seitz B. Curless J. Diebel D. Scharstein R. Szeliski University of Washington USA University of Stanford USA Middlebury College Microsoft Research Limited UK

This paper presents a quantitative comparison of several multi-view stereo reconstruction algorithms. Until now, the lack of suitable calibrated multi-view image datasets with known ground truth (3D shape models) has prevented such direct comparisons. In this paper, we first survey multi-view stereo algorithms and compare them qualitatively using a taxonomy that differentiates their key properties. We then describe our process for acquiring and calibrating multiview image datasets with high-accuracy ground truth and introduce our evaluation methodology. Finally, we present the results of our quantitative comparison of state-of-the-art multi-view stereo reconstruction algorithms on six benchmark datasets. The datasets, evaluation details, and instructions for submitting new models are available online at http://***/mview.

关键词： Reconstruction algorithms Stereo image processing Stereo vision Layout Taxonomy Image reconstruction Shape measurement Educational institutions Cameras Image databases

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Scene understanding from propagation and consistency of polarization-based constraints

Scene understanding from propagation and consistency of pola...

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

作者： Wolff Computer Vision Laboratory Department of Computer Science Johns Hopkins University Baltimore MD USA

We show how the more general capabilities of polarization vision can be used to significantly enhance the understanding of imaged scenes particularly at regions containing specular and mirror reflections, and occluding contours. Intensity-based methods are easily confounded by specular reflection. Specular mirror reflection of an object is indistinguishable from the object itself when using intensity and color, but is shown to be distinguishable when reflected polarization is analyzed. Polarization-based methods also enhance the identification of occluding contour edges, and regions that are in the vicinity of occluding contour edges. In past work local polarization constraints have been shown to accurately label specularities and occluding contours in simple scenes. In more complicated scenes under general conditions local polarization constraints are usually not sufficient to produce an accurate labeling of these phenomena. The main new idea presented in this paper is to extract much more additional information by enforcing physical consistency between neighboring local polarization constraints and propagating these constraints across pixel edges and regions with significant partial polarization. Initial labeling of pixels is produced from local polarization constraints at edges. We present well-defined physically motivated rules for relabeling pixels where physical inconsistencies result between neighboring local polarization constraints. Propagation rules allow physically consistent labeling of regions of significant partial polarization, and a refinement rule allows some more detailed labeling of edges. We include the previously unstudied case of specular reflection at/near occluding contours. In all 10 edge and region labels are generated. Experimental results on a complex scene is presented demonstrating the vast amount of information that can be extracted from these new physical principles of polarization vision.< >

关键词： Polarization Machine vision Image edge analysis Image region analysis

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Generating Diverse Agricultural Data for vision-Based Farming Applications

Generating Diverse Agricultural Data for Vision-Based Farmin...

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

作者： Mikolaj Cieslak Umabharathi Govindarajan Alejandro Garcia Anuradha Chandrashekar Torsten Hädrich Aleksander Mendoza-Drosik Dominik L. Michels Sören Pirk Chia-Chun Fu Wojciech Pałubicki GreenMatterAI Blue River Technology King Abdullah University of Science and Technology Saudi Arabia and Technical University of Darmstadt Germany Christian-Albrecht University of Kiel Germany Adam Mickiewicz University in Poznań Poland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. The model simulates distinct growth stages of these plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data. We validate our model’s effectiveness by comparing the synthetic data against real agricultural images, demonstrating its potential to significantly augment training data for machine learning models in agriculture. This approach not only provides a cost-effective solution for generating high-quality, diverse data but also addresses specific needs in agricultural vision tasks that are not fully covered by general-purpose models.

关键词： Training Adaptation models Semantic segmentation Crops Training data Soil Data augmentation

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Diffusion Distance for Histogram Comparison

Diffusion Distance for Histogram Comparison

引用

conference on computer vision and pattern recognition (CVPR)

作者： Haibin Ling K. Okada Center for Automation Research Computer Science Department University of Maryland College Park MD USA Imaging and Visualization Department Siemens Corporate Research Inc. Princeton NJ USA

In this paper we propose diffusion distance, a new dissimilarity measure between histogram-based descriptors. We define the difference between two histograms to be a temperature field. We then study the relationship between histogram similarity and a diffusion process, showing how diffusion handles deformation as well as quantization effects. As a result, the diffusion distance is derived as the sum of dissimilarities over scales. Being a cross-bin histogram distance, the diffusion distance is robust to deformation, lighting change and noise in histogram-based local descriptors. In addition, it enjoys linear computational complexity which significantly improves previously proposed cross-bin distances with quadratic complexity or higher. We tested the proposed approach on both shape recognition and interest point matching tasks using several multi-dimensional histogram-based descriptors including shape context, SIFT, and spin images. In all experiments, the diffusion distance performs excellently in both accuracy and efficiency in comparison with other state-of-the-art distance measures. In particular, it performs as accurately as the Earth Mover’s Distance with much greater efficiency.

关键词： Histograms Shape Temperature Diffusion processes Quantization Noise robustness Multi-stage noise shaping Computational complexity Testing Image recognition

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Accurately Estimating Sherd 3D Surface Geometry with Application to Pot Reconstruction

Accurately Estimating Sherd 3D Surface Geometry with Applica...

引用

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

作者： Andrew Willis Xavier Orriols David B. Cooper Brown University RI USA Computer Vision Laboratory UAB Bellaterra Spain

This paper deals with the problem of precise automatic estimation of the surface geometry of pot sherds uncovered at archaeological excavation sites using dense 3D laser-scan data. Critical to ceramic fragment analysis is the ability to geometrically classify excavated sherds, and, if possible, reconstruct the original pots using the sherd fragments. To do this, archaelogists must estimate the pot geometry in terms of an axis and associated profile curve from the discovered fragments. In this paper, we discuss an automatic method for accurately estimating an axis/profile curve pair for each archeological sherd (even when they are small) based on axially symmetric implicit polynomial surface models. Our method estimates the axis/profile curve for a sherd by finding the axially symmetric algebraic surface which best fits the measured set of dense 3D points and associated normals. We note that this method will work on 3D point data alone and does not require any local surface computations such as differentiation. Axis/profile curve estimates are accompanied by a detailed statistical error analysis. Estimation and error analysis are illustrated with application to a number of sherds. These fragments, excavated from Petra, Jordan, are chosen as exemplars of the families of geometrically diverse sherds commonly found on an archeological excavation site. We then briefly discuss how the estimation results may be integrated into a larger pot reconstruction program.

关键词： Three dimensional displays Surface fitting Estimation Fitting Data models Geometry Shape

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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...

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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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Meta-Evaluation of Image Segmentation Using Machine Learning

Meta-Evaluation of Image Segmentation Using Machine Learning

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conference on computer vision and pattern recognition (CVPR)

作者： Hui Zhang S. Cholleti S.A. Goldman J.E. Fritts Department of Computer Science and Engineering Washington University Saint Louis MO USA Department of Mathematics and Computer Science Saint Louis University Saint Louis MO USA

Image segmentation is a fundamental step in many computer vision applications. Generally, the choice of a segmentation algorithm, or parameterization of a given algorithm, is selected at the application level and fixed for all images within that application. Our goal is to create a stand-alone method to evaluate segmentation quality. Stand-alone methods have the advantage that they do not require a manually-segmented reference image for comparison, and can therefore be used for real-time evaluation. Current stand-alone evaluation methods often work well for some types of images, but poorly for others. We propose a meta-evaluation method in which any set of base evaluation methods are combined by a machine learning algorithm that coalesces their evaluations based on a learned weighting function, which depends upon the image to be segmented. The training data used by the machine learning algorithm can be labeled by a human, based on similarity to a human-generated reference segmentation, or based upon system-level performance. Experimental results demonstrate that our method performs better than the existing stand-alone segmentation evaluation methods.

关键词： Image segmentation Machine learning Application software computer vision Humans computer science Machine learning algorithms Drives Mathematics Training data

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Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets

Conv-Adapter: Exploring Parameter Efficient Transfer Learnin...

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

作者： Hao Chen Ran Tao Han Zhang Yidong Wang Xiang Li Wei Ye Jindong Wang Guosheng Hu Marios Savvides Carneige Mellon University Peking University Microsoft Research Asia Oosto

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

While parameter efficient tuning (PET) methods have shown great potential with transformer architecture on Natural Language Processing (NLP) tasks, their effectiveness with large-scale ConvNets is still under-studied on computer vision (CV) tasks. This paper proposes Conv-Adapter, a PET module designed for ConvNets. Conv-Adapter is light-weight, domain-transferable, and architecture-agnostic with generalized performance on different tasks. When transferring on downstream tasks, Conv- Adapter learns tasks-specific feature modulation to the intermediate representations of backbones while keeping the pre-trained parameters frozen. By introducing only a tiny amount of learnable parameters, e.g., only 3.5% full finetuning parameters of ResNet50. It can also be applied for transformer-based backbones. Conv-Adapter outperforms previous PET baseline methods and achieves comparable or surpasses the performance of full fine-tuning on 23 classification tasks of various domains. It also presents superior performance on the few-shot classification with an average margin of 3.39%. Beyond classification, Conv-Adapter can generalize to detection and segmentation tasks with more than 50% reduction of parameters but comparable performance to the traditional full fine-tuning 1

关键词： computer vision conferences Transfer learning Modulation Transformers Natural language processing Robustness

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