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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024"

共 4655 条记录，以下是181-190 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/cvf conference on computer vision and pattern recognition (CVPR)

作者： Conde, Marcos, V Zadtootaghaj, Saman Barman, Nabajeet Timofte, Radu He, Chenlong Zheng, Qi Zhu, Ruoxi Tu, Zhengzhong Wang, Haiqiang Chen, Xiangguang Meng, Wenhui Pan, Xiang Shi, Huiying Zhu, Han Xu, Xiaozhong Sun, Lei Chen, Zhenzhong Liu, Shan Zhang, Zicheng Wu, Haoning Zhou, Yingjie Li, Chunyi Liu, Xiaohong Lin, Weisi Zhai, Guangtao Sun, Wei Cao, Yuqin Jiang, Yanwei Jia, Jun Zhang, Zhichao Chen, Zijian Zhang, Weixia Min, Xiongkuo Goring, Steve Qi, Zihao Feng, Chen Univ Wurzburg Wurzburg Germany Sony Interact Entertainment FTG Tokyo Japan

ISBN: (纸本)9798350365474

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： AIS Image Quality Assessment IQA video video quality assessment VQA

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CPARR: Category-based Proposal Analysis for Referring Relationships

CPARR: Category-based Proposal Analysis for Referring Relati...

引用

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

作者： He, Chuanzi Zhu, Haidong Gao, Jiyang Chen, Kan Nevatia, Ram Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9781728193601

The task of referring relationships is to localize subject and object entities in an image satisfying a relationship query, which is given in the form of . This requires simultaneous localization of the subject and object entities in a specified relationship. We introduce a simple yet effective proposal-based method for referring relationships. Different from the existing methods such as SSAS, our method can generate a high-resolution result while reducing its complexity and ambiguity. Our method is composed of two modules: a category-based proposal generation module to select the proposals related to the entities and a predicate analysis module to score the compatibility of pairs of selected proposals. We show state-of-the-art performance on the referring relationship task on two public datasets: Visual Relationship Detection and Visual Genome.

关键词： computer vision

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Learning Personal Tastes in Choosing Fashion Outfits 32

Learning Personal Tastes in Choosing Fashion Outfits

引用

32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Lin, Yusan Moosaei, Maryam Yang, Hao Visa Res Palo Alto CA 94306 USA

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125060

With the emergence of fashion recommendation, many researchers have attempted to recommend fashion items that fit consumers' tastes. However, few have looked into fashion outfits as a whole when making recommendations. In this paper, we propose a neural network that learns one's fashion taste and predicts whether an individual likes a fashion outfit. To improve learning, we also develop a fashion outfit negative sampling scheme to sample fashion outfits that are different enough. With experiments on the collected Polyvore dataset, we find that using complete images offashion outfits performs well when learning individuals' tastes toward fashion outfits. Our proposed negative sampling scheme also improves the model's performance significantly, compared to random negative sampling.

关键词： Neural networks Predictive models Visualization Data models computer vision conferences Ions

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Multistage Fusion of Face Matchers

Multistage Fusion of Face Matchers

引用

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

作者： Tulyakov, Sergey Sankaran, Nishant Mohan, Deen Setlur, Srirangaraj Govindaraju, Venu Univ Buffalo Ctr Unified Biometr & Sensors Buffalo NY 14260 USA

ISBN: (纸本)9781665448994

Multistage, or serial, fusion refers to the algorithms sequentially fusing an increased number of matching results at each step and making decisions about accepting or rejecting the match hypothesis, or going to the next step. Such fusion methods are beneficial in the situations where running additional matching algorithms needed for later stages is time consuming or expensive. The construction of multistage fusion methods is challenging, since it requires both learning fusion functions and finding optimal decision thresholds for each stage. In this paper, we propose the use of single neural network for learning the multistage fusion. In addition we discuss the choices for the performance measurements of the trained algorithms and for the selection of network training optimization criteria. We perform the experiments using three face matching algorithms and IJB-A and IJB-C databases.

关键词： Training Measurement computer vision Error analysis Databases Face recognition conferences

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Road detection with EOSResUNet and post vectorizing algorithm 31

Road detection with EOSResUNet and post vectorizing algorith...

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

作者： Filin, Oleksandr Zapara, Anton Panchenko, Serhii EOS Data Analyt Menlo Pk CA 94027 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Object recognition on the satellite images is one of the most relevant and popular topics in the problem of pattern recognition. This was facilitated by many factors, such as a high number of satellites with high-resolution imagery, the significant development of computer vision, especially with a major breakthrough in the field of convolutional neural networks, a wide range of industry verticals for usage and still a quite empty market. Roads are one of the most popular objects for recognition. In this article, we want to present you the combination of work of neural network and postprocessing algorithm, due to which we get not only the coverage mask but also the vectors of all of the individual roads that are present in the image and can be used to address the higher-level tasks in the future. This approach was used to solve the DeepGlobe Road Extraction Challenge.

关键词： Roads Neural networks Task analysis Satellites computer vision Earth Observing System Data analysis

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Stacked U-Nets with Multi-Output for Road Extraction 31

Stacked U-Nets with Multi-Output for Road Extraction

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

作者： Sun, Tao Chen, Zehui Yang, Wenxiang Wang, Yin Tongji Univ Shanghai Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

With the recent advances of Convolutional Neural Networks (CNN) in computer vision, there have been rapid progresses in extracting roads and other features from satellite imagery for mapping and other purposes. In this paper, we propose a new method for road extraction using stacked U-Nets with multiple output. A hybrid loss function is used to address the problem of unbalanced classes of training data. Post-processing methods, including road map vectorization and shortest path search with hierarchical thresholds, help improve recall. The overall improvement of mean IoU compared to the vanilla VGG network is more than 20%.

关键词： Roads Decoding computer vision Encoding Feature extraction Neural networks Satellites

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Fusing Visual Saliency for Material recognition 31

Fusing Visual Saliency for Material Recognition

引用

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

作者： Qi, Lin Xu, Ying Shang, Xiaowei Dong, Junyu Ocean Univ China 238 Songling Rd Qingdao 266100 Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Material recognition is researched in both computer vision and vision science fields. In this paper, we investigated how humans observe material images and found the eye fixation information improves the performance of material image classification models. We first collected eye-tracking data from human observers and used it to fine-tune a generative adversarial network for saliency prediction (SalGAN). We then fused the predicted saliency map with material images and fed them to CNN models for material classification. The experiment results show that the classification accuracy is improved than those using original images. This indicates that human's visual cues could benefit computational models as priors.

关键词： Computational modeling computer vision Visualization Data models Task analysis Training pattern recognition

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CE-PeopleSeg: Real-time people segmentation with 10% CPU usage for video conference

CE-PeopleSeg: Real-time people segmentation with 10% CPU usa...

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

作者： Jiang, Ziyu He, Zhenhua Huang, Xueqin Yang, Zibin Tan, Pearl Texas A&M Univ College Stn TX 77843 USA EEO Washington DC USA

ISBN: (纸本)9781665448994

Nowadays, video conference solutions are widely adopted for companies, education, and government. People segmentation is crucial for supporting virtual background, an essential video conference function to protect users' privacy. This paper demonstrated a people segmentation framework called CE-PeopleSeg, which employed an efficient segmentation method, structural pruning, and dynamic frame skipping techniques, leading to a fast inference speed on CPU. Our extensive experiments show that the proposed CE-PeopleSeg can achieve a high prediction mIoU of 87.9% on Supervised People Dataset while reaching a real-time inference speed of 32.40 fps on CPU with very low usage of 10%. Our code would be released at https://***/geekJZY/***.

关键词： Visualization Privacy Government Education Graphics processing units Switches Streaming media

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Parallel Generative Adversarial Network for Third-person to First-person Image Generation

Parallel Generative Adversarial Network for Third-person to ...

引用

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

作者： Liu, Gaowen Latapie, Hugo Kilic, Ozkan Lawrence, Adam Cisco Res Emerging Technol & Incubat San Jose CA 95134 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Cross-view image generation has been recently proposed to generate images of one view from another dramatically different view. In this paper, we investigate third-person (exocentric) view to first-person (egocentric) view image generation. This is a challenging task since egocentric view sometimes is remarkably different from exocentric view. Thus, transforming the appearances across the two views is a non-trivial task. To this end, we propose a novel Parallel Generative Adversarial Network (P-GAN) with a novel cross-cycle loss to learn the shared information for generating egocentric images from exocentric view. We also incorporate a novel contextual feature loss in the learning procedure to capture the contextual information in images. Extensive experiments on the Exo-Ego datasets [5] show that our model outperforms the state-of-the-art approaches.

关键词： computer vision Image synthesis conferences Computational modeling Generative adversarial networks pattern recognition Task analysis

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Can domain adaptation make object recognition work for everyone?

Can domain adaptation make object recognition work for every...

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

作者： Prabhu, Viraj Selvaraju, Ramprasaath R. Hoffman, Judy Naik, Nikhil Georgia Tech Atlanta GA 30332 USA Artera AI Berkeley CA USA Salesforce Res Washington DC USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Despite the rapid progress in deep visual recognition, modern computer vision datasets significantly overrepresent the developed world and models trained on such datasets underperform on images from unseen geographies. We investigate the effectiveness of unsupervised domain adaptation (UDA) of such models across geographies at closing this performance gap. To do so, we first curate two shifts from existing datasets to study the Geographical DA problem, and discover new challenges beyond data distribution shift: context shift, wherein object surroundings may change significantly across geographies, and subpopulation shift, wherein the intra-category distributions may shift. We demonstrate the inefficacy of standard DA methods at Geographical DA, highlighting the need for specialized geographical adaptation solutions to address the challenge of making object recognition work for everyone.

关键词： Geography computer vision Adaptation models Visualization Image recognition Computational modeling conferences

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