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NTIRE 2021 Challenge for Defocus Deblurring Using Dual-pixel Images: Methods and Results

NTIRE 2021 Challenge for Defocus Deblurring Using Dual-pixel...

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

作者： Abuolaim, Abdullah Timofte, Radu Brown, Michael S. Zhang, Dafeng Wang, Xiaobing Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Shao, Ling Liu, Shuai Lei, Lei Feng, Chaoyu Xiong, Zhiwei Xiao, Zeyu Xu, Ruikang Zhu, Yunan Liu, Dong Vo, Tu Miao, Si Shah, Nisarg A. Liang, Pengwei Zhong, Zhiwei Hu, Xingyu Chen, Yiqun Li, Chenghua Bai, Xiaoying Zhang, Chi Yao, Yiheng Gang, Ruipeng Nathan, Sabari Ragavendran, Thangavelu Srinija, Venkatakrishnan Srivatsav, Venkatakrishnan York Univ N York ON Canada Swiss Fed Inst Technol Zurich Switzerland Samsung Res China Beijing SRC B Beijing Peoples R China Incept Inst Artificial Intelligence IIAI Abu Dhabi U Arab Emirates North China Univ Technol Beijing Peoples R China Xiaomi Beijing Peoples R China Univ Sci & Technol China Hefei Anhui Peoples R China Bridge AI Inc Seongnam South Korea Chinese Acad Sci Shanghai Adv Res Inst Shanghai Peoples R China Indian Inst Technol Jodhpur Karwar Rajasthan India Harbin Inst Technol Harbin Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Couger Inc Meerut Uttar Pradesh India Jeppair Coll Engn Chennai Tamil Nadu India Sri Sai Ram Inst Technol Chennai Tamil Nadu India Prince Shri Venkateshwara Padmavathy Engn Coll Chennai Tamil Nadu India

ISBN: (纸本)9781665448994

This paper provides a review of the NTIRE 2021 challenge targeting defocus deblurring using dual-pixel (DP) data. The goal of this single-track challenge was to reduce spatially varying defocus blur present in images captured with a shallow depth of field. The images used in this challenge were obtained using a DP sensor that provided a pair of DP views per captured image. Submitted solutions were evaluated using conventional signal processing metrics, namely peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Out of 185 registered participants, nine teams provided methods and competed in the final stage. The paper describes the methods proposed by the participating teams and their results. The winning teams represent the state-of-the-art in terms of defocus deblurring using DP images.

关键词： computer vision PSNR Atmospheric measurements conferences Signal processing Particle measurements pattern recognition

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Investigating Calibration and Corruption Robustness of Post-hoc Pruned Perception CNNs: An Image Classification Benchmark Study

Investigating Calibration and Corruption Robustness of Post-...

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

作者： Pallavi Mitra Gesina Schwalbe Nadja Klein Continental AG Germany University of Lübeck Germany TU Dortmund Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many computer vision tasks. However, high computational and storage demands hinder their deployment into resource-constrained environments, such as embedded devices. Model pruning helps to meet these restrictions by reducing the model size, while maintaining superior performance. Meanwhile, safety-critical applications pose more than just resource and performance constraints. In particular, predictions must not be overly confident, i.e., provide properly calibrated uncertainty estimations (proper uncertainty calibration), and CNNs must be robust against corruptions like naturally occurring input perturbations (natural corruption robustness). This work investigates the important trade-off between uncertainty calibration, natural corruption robustness, and performance for current state-of-research post-hoc CNN pruning techniques in the context of image classification tasks. Our study reveals that post-hoc pruning substantially improves the model’s uncertainty calibration, performance, and natural corruption robustness, sparking hope for safe and robust embedded CNNs. Furthermore, uncertainty calibration and natural corruption robustness are not mutually exclusive targets under pruning, as evidenced by the improved safety aspects obtained by post-hoc unstructured pruning with increasing compression.

关键词： Performance evaluation computer vision Uncertainty Image coding Computational modeling Robustness Calibration

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Multi-modal Aerial View Image Challenge: Translation from Synthetic Aperture Radar to Electro-Optical Domain Results - PBVS 2023

Multi-modal Aerial View Image Challenge: Translation from Sy...

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

作者： Spencer Low Oliver Nina Angel D. Sappa Erik Blasch Nathan Inkawhich Brigham Young University Provo Utah Air Force Research Laboratory Dayton OH ESPOL Polytechnic University Ecuador Computer Vision Center Spain Air Force Research Laboratory Arlington VA Air Force Research Laboratory Rome NY

This paper unveils the discoveries and outcomes of the inaugural iteration of the Multi-modal Aerial View Image Challenge (MAVIC) aimed at image translation. The primary objective of this competition is to stimulate research efforts towards the development of models capable of translating co-aligned images between multiple modalities. To accomplish the task of image translation, the competition utilizes images obtained from both synthetic aperture radar (SAR) and electro-optical (EO) sources. Specifically, the challenge centers on the translation from the SAR modality to the EO modality, an area of research that has garnered attention. The inaugural challenge demonstrates the feasibility of the task. The dataset utilized in this challenge is derived from the UNIfied COincident Optical and Radar for recognition (UNICORN) dataset. We introduce an new version of the UNICORN dataset that is focused on enabling the sensor translation task. Performance evaluation is conducted using a combination of measures to ensure high fidelity and high accuracy translations.

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Domain Targeted Synthetic Plant Style Transfer using Stable Diffusion, LoRA and ControlNet

Domain Targeted Synthetic Plant Style Transfer using Stable ...

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

作者： Zane K. J. Hartley Rob J. Lind Michael P. Pound Andrew P. French University of Nottingham Syngenta Jealott’s Hill International Research Centre

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Synthetic images can help alleviate much of the cost in the creation of training data for plant phenotyping-focused AI development. Synthetic-to-real style transfer is of particular interest to users of artificial data because of the domain shift problem created by training neural networks on images generated in a digital environment. In this paper we present a pipeline for synthetic plant creation and image-to-image style transfer, with a particular interest in synthetic to real domain adaptation targeting specific real datasets. Utilizing new advances in generative AI, we employ a combination of Stable diffusion, Low Ranked Adapters (LoRA) and ControlNets to produce an advanced system of style transfer. We focus our work on the core task of leaf instance segmentation, exploring both synthetic to real style transfer as well as inter-species style transfer and find that our pipeline makes numerous improvements over CycleGAN for style transfer, and the images we produce are comparable to real images when used as training data.

关键词： Training Instance segmentation computer vision Costs Generative AI conferences Pipelines

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NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results

NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset,...

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

作者： Perez-Pellitero, Eduardo Catley-Chandar, Sibi Leonardis, Ales Timofte, Radu Wang, Xian Li, Yong Wang, Tao Song, Fenglong Liu, Zhen Lin, Wenjie Li, Xinpeng Rao, Qing Jiang, Ting Han, Mingyan Fan, Haoqiang Sun, Jian Liu, Shuaicheng Chen, Xiangyu Liu, Yihao Zhang, Zhengwen Qiao, Yu Dong, Chao Chee, Evelyn Yi Lyn Shen, Shanlan Duan, Yubo Chen, Guannan Sun, Mengdi Gao, Yan Zhang, Lijie Akhil, K. A. Jiji, C., V Sharif, S. M. A. Naqvi, Rizwan Ali Biswas, Mithun Kim, Sungjun Xia, Chenjie Zhao, Bowen Ye, Zhangyu Lu, Xiwen Cao, Yanpeng Yang, Jiangxin Cao, Yanlong Rosh, Green K. S. Lomte, Sachin Deepak Krishnan, Nikhil Prasad, B. H. Pawan Huawei Noahs Ark Lab Shenzhen Peoples R China Megvii Technol Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Black Sesame Technol Singapore Singapore BOE Technol Co Ltd Beijing Peoples R China Coll Engn Trivandrum Trivandrum Kerala India Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou Peoples R China Zhejiang Univ Key Lab Adv Mfg Technol Hangzhou Peoples R China Sejong Univ Seoul South Korea Rigel IT Dhaka Bangladesh Samsung R&D Inst India Bangalore SRI B Bangalore Karnataka India

ISBN: (纸本)9781665448994

This paper reviews the first challenge on high-dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2021. This manuscript focuses on the newly introduced dataset, the proposed methods and their results. The challenge aims at estimating a HDR image from one or multiple respective low-dynamic range (LDR) observations, which might suffer from underor over-exposed regions and different sources of noise. The challenge is composed by two tracks: In Track 1 only a single LDR image is provided as input, whereas in Track 2 three differently-exposed LDR images with inter-frame motion are available. In both tracks, the ultimate goal is to achieve the best objective HDR reconstruction in terms of PSNR with respect to a ground-truth image, evaluated both directly and with a canonical tonemapping operation.

关键词： computer vision Tracking conferences Imaging Dynamic range Market research pattern recognition

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PUDD: Towards Robust Multi-modal Prototype-based Deepfake Detection

PUDD: Towards Robust Multi-modal Prototype-based Deepfake De...

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

作者： Alvaro Lopez Pellcier Yi Li Plamen Angelov School of Computing and Communications Lancaster University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deepfake techniques generate highly realistic data, making it challenging for humans to discern between actual and artificially generated images. Recent advancements in deep learning-based deepfake detection methods, particularly with diffusion models, have shown remarkable progress. However, there is a growing demand for real-world applications to detect unseen individuals, deepfake techniques, and scenarios. To address this limitation, we propose a Prototype-based Unified Framework for Deepfake Detection (PUDD). PUDD offers a detection system based on similarity, comparing input data against known prototypes for video classification and identifying potential deepfakes or previously unseen classes by analyzing drops in similarity. Our extensive experiments reveal three key findings: (1) PUDD achieves an accuracy of 95.1% on Celeb-DF, outperforming state-of-the-art deepfake detection methods; (2) PUDD leverages image classification as the upstream task during training, demonstrating promising performance in both image classification and deepfake detection tasks during inference; (3) PUDD requires only 2.7 seconds for retraining on new data and emits 10 5 times less carbon compared to the state-of-the-art model, making it significantly more environmentally friendly.

关键词： Training Deepfakes computer vision conferences Computational modeling Prototypes Diffusion models

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Learnable Global Spatio-Temporal Adaptive Aggregation for Bracketing Image Restoration and Enhancement

Learnable Global Spatio-Temporal Adaptive Aggregation for Br...

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

作者： Xinwei Dai Yuanbo Zhou Xintao Qiu Hui Tang Wei Deng Qingquan Gao Tong Tong Fuzhou University Imperial Vision Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Employing specific networks to address different types of degradation often proved to be complex and time-consuming in practical applications. The Bracket Image Restoration and Enhancement (BIRE) aimed to address various image restoration tasks in a unified manner by restoring clear single-frame images from multiple-frame shots, including denoising, deblurring, enhancing high dynamic range (HDR), and achieving super-resolution under various degradation conditions. In this paper, we propose LGSTANet, an efficient aggregation restoration network for BIRE. Specifically, inspired by video restoration methods, we adopt an efficient architecture comprising alignment, aggregation, and reconstruction. Additionally, we introduce a Learnable Global Spatio-Temporal Adaptive (LGSTA) aggregation module to effectively aggregate inter-frame complementary information. Furthermore, we propose an adaptive restoration modulator to address specific degradation disturbances of various types, thereby achieving high-quality restoration outcomes. Extensive experiments demonstrate the effectiveness of our method. LGSTANet outperforms other state-of-the-art methods in Bracket Image Restoration and Enhancement and achieves competitive results in the NTIRE2024 BIRE challenge.

关键词： Degradation Aggregates Perturbation methods Superresolution Noise reduction Modulation computer architecture

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Stereo Cross Global Learnable Attention Module for Stereo Image Super-Resolution

Stereo Cross Global Learnable Attention Module for Stereo Im...

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

作者： Yuanbo Zhou Yuyang Xue Wei Deng Ruofeng Nie Jiajun Zhang Jiaqi Pu Qinquan Gao Junlin Lan Tong Tong Fuzhou University University of Edinburgh Imperial Vision Technology

Stereo super-resolution is a technique that utilizes corresponding information from multiple viewpoints to enhance the texture of low-resolution images. In recent years, numerous impressive works have advocated attention mechanisms based on epipolar constraints to boost the performance of stereo super-resolution. However, techniques that exclusively depend on epipolar constraint attention are insufficient to recover realistic and natural textures for heavily corrupted low-resolution images. We noticed that global self-similarity features within the image and across the views can proficiently fix the texture details of low-resolution images that are severely damaged. Therefore, in the current paper, we propose a stereo cross global learnable attention module (SCGLAM), aiming to improve the performance of stereo super-resolution. The experimental outcomes show that our approach outperforms others when dealing with heavily damaged low-resolution images. The relevant code is made available on this link as open source.

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Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-Resolution

Training Transformer Models by Wavelet Losses Improves Quant...

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

作者： Cansu Korkmaz A. Murat Tekalp College of Engineering and KUIS AI Center Koc University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Transformer-based models have achieved remarkable results in low-level vision tasks including image super-resolution (SR). However, early Transformer-based approaches that rely on self-attention within non-overlapping windows encounter challenges in acquiring global information. To activate more input pixels globally, hybrid attention models have been proposed. Moreover, training by solely minimizing pixel-wise RGB losses, such as l 1 , have been found inadequate for capturing essential high-frequency details. This paper presents two contributions: i) We introduce convolutional non-local sparse attention (NLSA) blocks to extend the hybrid transformer architecture in order to further enhance its receptive field. ii) We employ wavelet losses to train Transformer models to improve quantitative and subjective performance. While wavelet losses have been explored previously, showing their power in training Transformer-based SR models is novel. Our experimental results demonstrate that the proposed model provides state-of-the-art PSNR results as well as superior visual performance across various benchmark datasets.

关键词： Wavelet transforms Training Visualization Computational modeling conferences Superresolution computer architecture

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MonoSelfRecon: Purely Self-Supervised Explicit Generalizable 3D Reconstruction of Indoor Scenes from Monocular RGB Views

MonoSelfRecon: Purely Self-Supervised Explicit Generalizable...

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

作者： Runfa Li Upal Mahbub Vasudev Bhaskaran Truong Nguyen UC San Diego Qualcomm

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Current monocular 3D scene reconstruction (3DR) works are either fully-supervised, or not generalizable, or implicit in 3D representation. We propose a novel framework - MonoSelfRecon that for the first time achieves explicit 3D mesh reconstruction for generalizable indoor scenes with monocular RGB views by purely self-supervision on voxel-SDF (signed distance function). MonoSelfRecon follows an Autoencoder-based architecture, decodes voxel-SDF and a generalizable Neural Radiance Field (NeRF), which is used to guide voxel-SDF in self-supervision. We propose novel self-supervised losses, which not only support pure self-supervision, but can be used together with supervised signals to further boost supervised training. Our experiments show that "MonoSelfRecon" trained in pure self-supervision outperforms current best self-supervised indoor depth estimation models and is comparable to 3DR models trained in fully supervision with depth annotations. MonoSelfRecon is not restricted by specific model design, which can be used to any models with voxel-SDF for purely self-supervised manner.

关键词： Training Solid modeling computer vision Three-dimensional displays Annotations conferences Estimation

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