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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261-270 订阅

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Conjugate Adder Net (CAddNet) - a Space-Efficient Approximate CNN

Conjugate Adder Net (CAddNet) - a Space-Efficient Approximat...

引用

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

作者： Shen, Lulan Ziaeefard, Maryam Meyer, Brett Gross, Warren Clark, James J. McGill Univ Montreal PQ Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The AdderNet was recently developed as a way to implement deep neural networks without needing multiplication operations to combine weights and inputs. Instead, absolute values of the difference between weights and inputs are used, greatly reducing the gate-level implementation complexity. Training of AdderNets is challenging, however, and the loss curves during training tend to fluctuate significantly. In this paper we propose the Conjugate Adder Network, or CAddNet, which uses the difference between the absolute values of conjugate pairs of inputs and the weights. We show that this can be implemented simply via a single minimum operation, resulting in a roughly 50% reduction in logic gate complexity as compared with AdderNets. The CAddNet method also stabilizes training as compared with AdderNets, yielding training curves similar to standard CNNs.

关键词： Training Deep learning conferences Neural networks Logic gates Complexity theory pattern recognition

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Unseen And Adverse Outdoor Scenes recognition Through Event-based Captions

Unseen And Adverse Outdoor Scenes Recognition Through Event-...

引用

ieee/cvf International conference on computer vision (ICCV)

作者： Sakaino, Hidetomo Weathernews Inc Weather Transportat Lab Visual Recognit Grp Chiba Japan

ISBN: (纸本)9798350307443

This paper presents EventCAP, i.e., event-based captions, for refined and enriched qualitative and quantitative captions by Deep Learning (DL) models and vision Language Models (VLMs) with different tasks in a complementary manner. Indoor and outdoor images are used for object recognition and captioning. However, outdoor images in events change in wide ranges due to natural phenomena, i.e., weather changes. Such dynamical changes may degrade segmentation by illumination and object shape changes. This increases unseen objects and scenes under such adverse conditions. On the other hand, single state-of-art (SOTA) DLs and VLMs work with single or limited tasks, Therefore, this paper proposes EventCAP with captions with physical scales and objects' surface properties. Moreover, an iterative VQA model is proposed to refine incomplete segmented images with the prompts. A higher semantic level in captions for real-world scene descriptions is experimentally shown compared to SOTA VLMs.

关键词： adverse condition caption deep learning recognition segmentation vision language

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Zero-shot Learning Using Multimodal Descriptions

Zero-shot Learning Using Multimodal Descriptions

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

作者： Mall, Utkarsh Hariharan, Bharath Bala, Kavita Cornell Univ Ithaca NY 14853 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Zero-shot learning (ZSL) tackles the problem of recognizing unseen classes using only semantic descriptions, e.g., attributes. Current zero-shot learning techniques all assume that a single vector of attributes suffices to describe each category. We show that this assumption is incorrect. Many classes in real world problems have multiple modes of appearance: male and female birds vary in appearance, for instance. Domain experts know this and can provide attribute descriptions of the chief modes of appearance for each class. Motivated by this, we propose the task of multi-modal zero-shot learning, where the learner must learn from these multimodal attribute descriptions. We present a technique for addressing this problem of multimodal ZSL that outperforms the unimodal counterpart significantly. We posit that multimodal ZSL is more practical for real-world problems where complex intra-class variation is common.

关键词： computer vision conferences Computational modeling Semantics Manuals Benchmark testing Birds

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StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN

StyleCineGAN: Landscape Cinemagraph Generation using a Pre-t...

引用

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

作者： Choi, Jongwoo Seo, Kwanggyoon Ashtari, Amirsaman Noh, Junyong Korea Adv Inst Sci & Technol Visual Media Lab Daejeon South Korea

ISBN: (纸本)9798350353013;9798350353006

We propose a method that can generate cinemagraphs automatically from a still landscape image using a pre-trained StyleGAN. Inspired by the success of recent unconditional video generation, we leverage a powerful pre-trained image generator to synthesize high-quality cinemagraphs. Unlike previous approaches that mainly utilize the latent space of a pre-trained StyleGAN, our approach utilizes its deep feature space for both GAN inversion and cinemagraph generation. Specifically, we propose multi-scale deep feature warping (MSDFW), which warps the intermediate features of a pre-trained StyleGAN at different resolutions. By using MSDFW, the generated cinemagraphs are of high resolution and exhibit plausible looping animation. We demonstrate the superiority of our method through user studies and quantitative comparisons with state-of-the-art cinemagraph generation methods and a video generation method that uses a pre-trained StyleGAN.

关键词： Image and video synthesis and generation vision + graphics vision applications and systems

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Perceptual in-Loop Filter for Image and Video Compression

Perceptual in-Loop Filter for Image and Video Compression

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

作者： Wang, Huairui Ren, Guangjie Ouyang, Tong Zhang, Junxi Han, Wenwei Liu, Zizheng Chen, Zhenzhong Wuhan Univ Wuhan Peoples R China Tencent Media Lab Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we introduce our hybrid image and video compression scheme enhanced by CNN-optimized in-loop filter. Specifically, a Structure Preserving in-Loop Filter (SPiLF) is incorporated in the hybrid video codec Enhanced Compression Model (ECM), where two branches, i.e., gradient branch and pixel branch, are developed based on the dense residual unit (DRU). To provide pleasant visual quality, the Generative adversarial networks (GAN) loss and LPIPS loss are further considered. Therefore, the proposal is mainly focusing on perceptual-friendly image compression for human vision, whilst video compression could be further investigated. The experiments show that the proposed method achieves advanced visual quality when compared to the traditional methods.

关键词： Visualization computer vision Image coding conferences Focusing Video compression Generative adversarial networks

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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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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

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

ISBN: (纸本)9798350302493

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. © 2023 ieee.

关键词： Textures

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Face image quality for actor profile image curation

Face image quality for actor profile image curation

引用

23rd ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Pandya, Yash Aggarwal, Abhinav Sethu, Manivel Ahire, Laxmi Nandy, Kaustav Amazon Bangalore Karnataka India

ISBN: (纸本)9798350320565

Selecting an ideal profile image to represent a person is a common problem with many applications. The ideal characteristics of a representative or profile image differ based on the application. In this work, we focus on selecting a representative face which is easy to recognise and aesthetically pleasing. Manually curating these images is time consuming, repetitive, and subjective. This makes the quality of curated images inconsistent. We have built a solution to automate this process and make it efficient, scalable, and consistent. In this work, we describe the various factors which affect the suitability of a face image for recognition by humans. We propose efficient solutions which can solve the problem without the use of ground truth data. We train a regression model using weak supervision provided by heuristics based on features which affect face quality. Finally, we use professional photography techniques to create standardized and aesthetically pleasing profile images.

关键词： Photography Image quality computer vision Image recognition Face recognition conferences Computational modeling

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Improving Deep Learning-based Automatic Checkout System Using Image Enhancement Techniques

Improving Deep Learning-based Automatic Checkout System Usin...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Pham, Long Hoang Tran, Duong Nguyen-Ngoc Nguyen, Huy-Hung Jeon, Hyung-Joon Tran, Tai Huu-Phuong Jeon, Hyung-Min Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

ISBN: (纸本)9798350302493

The retail sector has experienced significant growth in artificial intelligence and computer vision applications, particularly with the emergence of automatic checkout (ACO) systems in stores and supermarkets. ACO systems encounter challenges such as object occlusion, motion blur, and similarity between scanned items while acquiring accurate training images for realistic checkout scenarios is difficult due to constant product updates. This paper improves existing deep learning-based ACO solutions by incorporating several image enhancement techniques in the data pre-processing step. The proposed ACO system employs a detect-and-track strategy, which involves: (1) detecting objects in areas of interest;(2) tracking objects in consecutive frames;and (3) counting objects using a track management pipeline. Several data generation techniques - including copy-and-paste, random placement, and augmentation - are employed to create diverse training data. Additionally, the proposed solution is designed as an open-ended framework that can be easily expanded to accommodate multiple tasks. The system has been evaluated on the AI City Challenge 2023 Track 4 dataset, showcasing outstanding performance by achieving a top-1 ranking on test-set A with an F1 score of 0.9792. © 2023 ieee.

关键词： Image enhancement

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Self Supervised Scanpath Prediction Framework for Painting Images

Self Supervised Scanpath Prediction Framework for Painting I...

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

作者： Tliba, Marouane Kerkouri, Mohamed Amine Chetouani, Aladine Bruno, Alessandro Univ Orleans Orleans France Bournemouth Univ Poole Dorset England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In our paper, we propose a novel strategy to learn distortion invariant latent representation from painting pictures for visual attention modelling downstream task. In further detail, we design an unsupervised framework that jointly maximises the mutual information over different painting styles. To show the effectiveness of our approach, we firstly propose a lightweight scanpath baseline model and compare its performance to some state-of-the-art methods. Secondly, we train the encoder of our baseline model on large-scale painting images to study the efficiency of the proposed self-supervised strategy. The lightweight decoder proves effective in learning from the self-supervised pre-trained encoder with better performances than the end-to-end fine-tuned supervised baseline on two painting datasets, including a proposed new visual attention modelling dataset.

关键词： Visualization Self-supervised learning Predictive models Observers Quality assessment pattern recognition Task analysis

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Diversified and Multi-Class Controllable Industrial Defect Synthesis for Data Augmentation and Transfer

Diversified and Multi-Class Controllable Industrial Defect S...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Wei, Jing Shen, Fei Lv, Chengkan Zhang, Zhengtao Zhang, Feng Yang, Huabin Institute of Automation Chinese Academy of Sciences China The School of Artificial Intelligence University of Chinese Academy of Sciences China Casi Vision Technology CO. China

ISBN: (纸本)9798350302493

Data augmentation is crucial to solve few-sample issues in industrial inspection based on deep learning. However, current industrial data augmentation methods have not yet demonstrated on-par ability in the synthesis of complex defects with pixel-level annotations. This paper proposes a new defect synthesis framework to fill the gap. Firstly, DCDGANc (Diversified and multi-class Controllable Defect Generation Adversarial Networks based on constant source images) is proposed to employ class labels to construct source inputs to control the category and random codes to generate diversified styles of defects. DCDGANc can generate defect content images with pure backgrounds, which avoids the influence of non-defect information and makes it easy to obtain binary masks by segmentation. Secondly, the Poisson blending is improved to avoid content loss when blending generated defect contents to the normal backgrounds. Finally, the complete defect samples and accurate pixel-level annotations are obtained by fine image processing. Experiments are conducted to verify the effectiveness of our work in wood, fabric, metal, and marble. The results show that our methods yield significant improvement in the segmentation performance of industrial products. Moreover, our work enables zero-shot inspection by facilitating defect transfer between datasets with different backgrounds but similar defects, which can greatly reduce the cost of data collection in industrial inspection. © 2023 ieee.

关键词： Defects

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