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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是2021-2030 订阅

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AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results

AIS 2024 Challenge on Video Quality Assessment of User-Gener...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Marcos V. Conde Saman Zadtootaghaj Nabajeet Barman Radu Timofte Chenlong He Qi Zheng Ruoxi Zhu Zhengzhong Tu Haiqiang Wang Xiangguang Chen Wenhui Meng Xiang Pan Huiying Shi Han Zhu Xiaozhong Xu Lei Sun Zhenzhong Chen Shan Liu Zicheng Zhang Haoning Wu Yingjie Zhou Chunyi Li Xiaohong Liu Weisi Lin Guangtao Zhai Wei Sun Yuqin Cao Yanwei Jiang Jun Jia Zhichao Zhang Zijian Chen Weixia Zhang Xiongkuo Min Steve Göring Zihao Qi Chen Feng CAIDAS & IFI Computer Vision Lab. University of Würzburg Sony Interactive Entertainment FTG

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based methods capable of estimating the perceptual quality of UGC videos. The user-generated videos from the YouTube UGC Dataset include diverse content (sports, games, lyrics, anime, etc.), quality and resolutions. The proposed methods must process 30 FHD frames under 1 second. In the challenge, a total of 102 participants registered, and 15 submitted results during the challenge period. The performance of the top-5 submissions is reviewed and provided here as a survey of diverse deep models for Video Quality Assessment of user-generated content.

关键词： Surveys Learning systems Video on demand Reviews User-generated content Quality assessment pattern recognition

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Divide and Conquer: High-Resolution Industrial Anomaly Detection via Memory Efficient Tiled Ensemble

Divide and Conquer: High-Resolution Industrial Anomaly Detec...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Blaž Rolih Dick Ameln Ashwin Vaidya Samet Akcay Faculty of Computer and Information Science University of Ljubljana Intel

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Industrial anomaly detection is an important task within computer vision with a wide range of practical use cases. The small size of anomalous regions in many real-world datasets necessitates processing the images at a high resolution. This frequently poses significant challenges concerning memory consumption during the model training and inference stages, leaving some existing methods impractical for widespread adoption. To overcome this challenge, we present the tiled ensemble approach, which reduces memory consumption by dividing the input images into a grid of tiles and training a dedicated model for each tile location. The tiled ensemble is compatible with any existing anomaly detection model without the need for any modification of the underlying architecture. By introducing overlapping tiles, we utilize the benefits of traditional stacking ensembles, leading to further improvements in anomaly detection capabilities beyond high resolution alone. We perform a comprehensive analysis using diverse underlying architectures, including Padim, PatchCore, FastFlow, and Reverse Distillation, on two standard anomaly detection datasets: MVTec and VisA. Our method demonstrates a notable improvement across setups while remaining within GPU memory constraints, consuming only as much GPU memory as a single model needs to process a single tile. 1 2

关键词： Training computer vision Image resolution Tiles Memory management Stacking Graphics processing units

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Deep Portrait Quality Assessment. A NTIRE 2024 Challenge Survey

Deep Portrait Quality Assessment. A NTIRE 2024 Challenge Sur...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Nicolas Chahine Marcos V. Conde Daniela Carfora Gabriel Pacianotto Benoit Pochon Sira Ferradans Radu Timofte Zhichao Duan Xinrui Xu Yipo Huang Quan Yuan Xiangfei Sheng Zhichao Yang Leida Li Haotian Fan Fangyuan Kong Yifang Xu Wei Sun Weixia Zhang Yanwei Jiang Haoning Wu Zicheng Zhang Jun Jia Yingjie Zhou Zhongpeng Ji Xiongkuo Min Weisi Lin Guangtao Zhai Xiaoqi Wang Junqi Liu Zixi Guo Yun Zhang Zewen Chen Wen Wang Juan Wang Bing Li DXOMARK CAIDAS & IFI Computer Vision Lab University of Würzburg Xidian University ByteDance Inc Shanghai Jiao Tong University Nanyang Technological University Huawei School of Electronics and Communication Engineering Sun Yat-sen University China State Key Laboratory of Multimodal Artificial Intelligence Systems CASIA School of Artificial Intelligence University of Chinese Academy of Sciences Beijing Jiaotong University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the NTIRE 2024 Portrait Quality Assessment Challenge, highlighting the proposed solutions and results. This challenge aims to obtain an efficient deep neural network capable of estimating the perceptual quality of real portrait photos. The methods must generalize to diverse scenes and diverse lighting conditions (indoor, outdoor, low-light), movement, blur, and other challenging conditions. In the challenge, 140 participants registered, and 35 submitted results during the challenge period. The performance of the top 5 submissions is reviewed and provided here as a gauge for the current state-of-the-art in Portrait Quality Assessment.

关键词： Surveys computer vision Reviews conferences Lighting Artificial neural networks Quality assessment pattern recognition

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Fully Test-time Adaptation for Object Detection

Fully Test-time Adaptation for Object Detection

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xiaoqian Ruan Wei Tang University of Illinois Chicago

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Though the object detection performance on standard benchmarks has been improved drastically in the last decade, current object detectors are often vulnerable to domain shift between the training data and testing images. Domain adaptation techniques have been developed to adapt an object detector trained in a source domain to a target domain. However, they assume that the target domain is known and fixed and that a target dataset is available for training, which cannot be satisfied in many real-world applications. To close this gap, this paper investigates fully test-time adaptation for object detection. It means to update a trained object detector on a single testing image before making a prediction, without access to the training data. Through a diagnostic study of a baseline self-training framework, we show that a great challenge of this task is the unreliability of pseudo labels caused by domain shift. We then propose a simple yet effective method, termed the IoU Filter, to address this challenge. It consists of two new IoU-based indicators, both of which are complementary to the detection confidence. Experimental results on five datasets demonstrate that our approach could effectively adapt a trained detector to various kinds of domain shifts at test time and bring substantial performance gains. Code is available at https://***/XiaoqianRuan1/IoU-filter.

关键词： Training computer vision Codes conferences Training data Detectors Object detection

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SAM-PM: Enhancing Video Camouflaged Object Detection using Spatio-Temporal Attention

SAM-PM: Enhancing Video Camouflaged Object Detection using S...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Muhammad Nawfal Meeran Gokul Adethya T Bhanu Pratyush Mantha National Institute of Technology Tiruchirappalli

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the domain of large foundation models, the Segment Anything Model (SAM) has gained notable recognition for its exceptional performance in image segmentation. However, tackling the video camouflage object detection (VCOD) task presents a unique challenge. Camouflaged objects typically blend into the background, making them difficult to distinguish in still images. Additionally, ensuring temporal consistency in this context is a challenging problem. As a result, SAM encounters limitations and falls short when applied to the VCOD task. To overcome these challenges, we propose a new method called the SAM Propagation Module (SAM-PM). Our propagation module enforces temporal consistency within SAM by employing spatio-temporal cross-attention mechanisms. Moreover, we exclusively train the propagation module while keeping the SAM network weights frozen, allowing us to integrate task-specific insights with the vast knowledge accumulated by the large model. Our method effectively incorporates temporal consistency and domain-specific expertise into the segmentation network with an addition of less than 1% of SAM’s parameters. Extensive experimentation reveals a substantial performance improvement in the VCOD benchmark when compared to the most recent state-of-the-art techniques. Code and pre-trained weights are open-sourced at https://***/SpiderNitt/SAM-PM

关键词： Training Knowledge engineering Image segmentation computer vision Image recognition Codes conferences

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

Domain Targeted Synthetic Plant Style Transfer using Stable ...

引用

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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Prompting Foundational Models for Omni-supervised Instance Segmentation

Prompting Foundational Models for Omni-supervised Instance S...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Arnav M. Das Ritwick Chaudhry Kaustav Kundu Davide Modolo University of Washington Seattle AWS AI Labs

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Pixel-level mask annotation costs are a major bottleneck in training deep neural networks for instance segmentation. Recent promptable foundation models like the Segment Anything Model (SAM) and GroundedDINO (GDino) have shown impressive zero-shot performance in segmentation and object detection benchmarks. While these models are not capable of performing inference without prompts, they are ideal for omnisupervised learning, where weak labels are used to derive supervisory signals for complex tasks. In our work, we use SAM and GDino as teacher models and prompt them with weak annotations to create high-quality pseudomasks. These pseudomasks are then used to train student instance segmentation models, which do not require prompts at inference time. We explore various weak annotations, such as bounding boxes, points, and image-level class labels, and show that a student model can achieve roughly 95% of a fully-supervised model’s performance while reducing annotation costs by 7×. We show the effectiveness of our approach on challenging instance segmentation benchmarks such as COCO [15], ADE20K [30], Cityscapes [9]. Our approach can be used to reduce annotation cost to train instance segmentation models, making it more accessible to a wider range of applications.

关键词： Instance segmentation Training computer vision Costs Annotations Computational modeling conferences

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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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Learning Surface Terrain Classifications from Ground Penetrating Radar

Learning Surface Terrain Classifications from Ground Penetra...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Anja Sheppard Jason Brown Nilton Renno Katherine A. Skinner The University of Michigan Ann Arbor MI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Terrain classification is an important problem for mobile robots operating in extreme environments as it can aid downstream tasks such as autonomous navigation and planning. While RGB cameras are widely used for terrain identification, vision-based methods can suffer due to poor lighting conditions and occlusions. In this paper, we propose the novel use of Ground Penetrating Radar (GPR) for terrain characterization for mobile robot platforms. Our approach leverages machine learning for surface terrain classification from GPR data. We collect a new dataset consisting of four different terrain types, and present qualitative and quantitative results. Our results demonstrate that classification networks can learn terrain categories from GPR signals. Additionally, we integrate our GPR-based classification approach into a multimodal semantic mapping framework to demonstrate a practical use case of GPR for surface terrain classification on mobile robots. Overall, this work extends the usability of GPR sensors deployed on robots to enable terrain classification in addition to GPR’s existing scientific use cases.

关键词： Ground penetrating radar Surface waves Semantics Robot vision systems Sensor systems Sensors Planning

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SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-Image Translation while Maintaining Stereo Constraint

SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Vasudha Venkatesan Daniel Panangian Mario Fuentes Reyes Ksenia Bittner University of Freiburg German Aerospace Center (DLR)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of remote sensing, the scarcity of stereo-matched and particularly lack of accurate ground truth data often hinders the training of deep neural networks. The use of synthetically generated images as an alternative, alleviates this problem but suffers from the problem of domain generalization. Unifying the capabilities of image-to-image translation and stereo-matching presents an effective solution to address the issue of domain generalization. Current methods involve combining two networks—an unpaired image-to-image translation network and a stereo-matching network—while jointly optimizing them. We propose an edge-aware GAN-based network that effectively tackles both tasks simultaneously. We obtain edge maps of input images from the Sobel operator and use it as an additional input to the encoder in the generator to enforce geometric consistency during translation. We additionally include a warping loss calculated from the translated images to maintain the stereo consistency. We demonstrate that our model produces qualitatively and quantitatively superior results than existing models, and its applicability extends to diverse domains, including autonomous driving.

关键词： Training computer vision Accuracy Image edge detection conferences Generative adversarial networks Generators

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