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检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022"

共 3917 条记录，以下是41-50 订阅

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Boundary-aware Image Inpainting with Multiple Auxiliary Cues

Boundary-aware Image Inpainting with Multiple Auxiliary Cues

引用

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

作者： Yamashita, Yohei Shimosato, Kodai Ukita, Norimichi Toyota Technol Inst Nagoya Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Image inpainting (a.k.a. image completion) allows us to remove unexpected foreground objects from an observed image and to restore the removed region with background pixels. The performance of image inpainting is improved by auxiliary cues such as edge boundaries and segmentation regions. As a new auxiliary cue, this paper focuses on a depth image that is estimated from an input RGB image by monocular depth estimation. In the depth image, boundaries between different objects (e.g., objects located in different distances) with similar pixel values might be available, while those boundaries are difficult to be detected by edge detection and segmentation. Our proposed method employs those boundaries in the edge and depth images as auxiliary cues. Experiments demonstrate that our proposed method augmented by the depth image outperforms its baseline quantitatively (i.e., 1.17dB and 0.74dB PSNR gains on the Paris-StreetView and Places datasets, respectively) and qualitatively.

关键词： Image segmentation computer vision Image edge detection conferences Estimation pattern recognition Image restoration

来源：评论

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Rethinking Supervised Depth Estimation for 360° Panoramic Imagery

Rethinking Supervised Depth Estimation for 360° Panoramic I...

引用

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

作者： He, Lu Jian, Bing Wen, Yangming Zhu, Haichao Liu, Kelin Feng, Weiwei Liu, Shan Tencent Amer Palo Alto CA 94306 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Depth estimation from a single 360 degrees panorama image is a difficult task. It is an ill-posed problem to estimate depth maps from an RGB panorama image due to the intrinsic scale ambiguity issue. To mitigate the scale inconsistency issue in the ground truth depth map, we propose a simple yet effective method to normalize the depth data based on estimated camera height. In addition, we design a multiple head planar-guided depth network, to provide more geometric constraints for depth estimation. Experimental results show that our relative depth estimation task is more accurate than the absolute depth estimation task, and our proposed model produces state-of-the-art performance on both Matterport3D and Stanford2D3D datasets.

关键词： Measurement computer vision Head conferences Estimation Data models pattern recognition

来源：评论

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Residual Feature Pyramid Network for Enhancement of Vascular patterns

Residual Feature Pyramid Network for Enhancement of Vascular...

引用

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

作者： Kotwal, Ketan Marcel, Sebastien Idiap Res Inst Martigny Switzerland Univ Lausanne Lausanne Switzerland

ISBN: (纸本)9781665487399

The accuracy of finger vein recognition systems gets degraded due to low and uneven contrast between veins and surroundings, often resulting in poor detection of vein patterns. We propose a finger-vein enhancement technique, ResFPN (Residual Feature Pyramid Network), as a generic preprocessing method agnostic to the recognition pipeline. A bottom-up pyramidal architecture using the novel Structure Detection block (SDBlock) facilitates extraction of veins of varied widths. Using a feature aggregation module (FAM), we combine these vein-structures, and train the proposed ResFPN for detection of veins across scales. With enhanced presentations, our experiments indicate a reduction upto 5% in the average recognition errors for commonly used recognition pipeline over two publicly available datasets. These improvements are persistent even in cross-dataset scenario where the dataset used to train the ResFPN is different from the one used for recognition.

关键词： Histograms Protocols Veins Pipelines Fingers computer architecture Network architecture

来源：评论

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DRHDR: A Dual branch Residual Network for Multi-Bracket High Dynamic Range Imaging

DRHDR: A Dual branch Residual Network for Multi-Bracket High...

引用

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

作者： Marin-Vega, Juan Sloth, Michael Schneider-Kamp, Peter Rottger, Richard Univ Southern Denmark Dept Math & Comp Sci IMADA Odense Denmark Esoft Syst Odense Denmark

ISBN: (纸本)9781665487399

We introduce DRHDR, a Dual branch Residual Convolutional Neural Network for Multi-Bracket HDR Imaging. To address the challenges of fusing multiple brackets from dynamic scenes, we propose an efficient dual branch network that operates on two different resolutions. The full resolution branch uses a Deformable Convolutional Block to align features and retain high-frequency details. A low resolution branch with a Spatial Attention Block aims to attend wanted areas from the non-reference brackets, and suppress displaced features that could incur on ghosting artifacts. By using a dual branch approach we are able to achieve high quality results while constraining the computational resources required to estimate the HDR results.

关键词： computer vision conferences Imaging Dynamic range pattern recognition Complexity theory Convolutional neural networks

来源：评论

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Generative Flows as a General Purpose Solution for Inverse Problems

Generative Flows as a General Purpose Solution for Inverse P...

引用

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

作者： Chavez, Jose A. San Pablo Catholic Univ Arequipa Peru

ISBN: (纸本)9781665487399

Due to the success of generative flows to model data distributions, they have been explored in inverse problems. Given a pre-trained generative flow, previous work proposed to minimize the 2-norm of the latent variables as a regularization term. The intuition behind it was to ensure high likelihood latent variables that produce the closest restoration. However, high-likelihood latent variables may generate unrealistic samples as we show in our experiments. We therefore propose a solver to directly produce high-likelihood reconstructions. We hypothesize that our approach could make generative flows a general purpose solver for inverse problems. Furthermore, we propose 1x1 coupling functions to introduce permutations in a generative flow. It has the advantage that its inverse does not require to be calculated in the generation process. Finally, we evaluate our method for denoising, deblurring, inpainting, and colorization. We observe a compelling improvement of our method over prior works.

关键词： Couplings computer vision Inverse problems conferences Noise reduction Data models pattern recognition

来源：评论

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OutfitTransformer: Outfit Representations for Fashion Recommendation

OutfitTransformer: Outfit Representations for Fashion Recomm...

引用

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

作者： Sarkar, Rohan Bodla, Navaneeth Vasileva, Mariya Lin, Yen-Liang Beniwal, Anurag Lu, Alan Medioni, Gerard Purdue Univ W Lafayette IN 47907 USA Amazon Seattle WA 98109 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Predicting outfit compatibility and retrieving complementary items are critical components for a fashion recommendation system. We present a scalable framework, Out-fitTransformer, that learns compatibility of the entire out-fit and supports large-scale complementary item retrieval. We model outfits as an unordered set of items and leverage self-attention mechanism to learn the relationships between items. We train the framework using a proposed set-wise outfit ranking loss to generate a target item embedding given an outfit, and a target item specification. The generated target item embedding is then used to retrieve compatible items that match the outfit. Experimental results demonstrate that our approach outperforms state-of-the-art methods on compatibility prediction, fill-in-the-blank, and complementary item retrieval tasks.

关键词： computer vision conferences Task analysis Recommender systems pattern matching

来源：评论

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Perceptual in-Loop Filter for Image and Video Compression

Perceptual in-Loop Filter for Image and Video Compression

引用

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

来源：评论

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AAFormer: A Multi-Modal Transformer Network for Aerial Agricultural Images

AAFormer: A Multi-Modal Transformer Network for Aerial Agric...

引用

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

作者： Shen, Yao Wang, Lei Jin, Yue China Pacific Insurance Grp Co Ltd Shanghai Peoples R China East China Normal Univ Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The semantic segmentation of agricultural aerial images is very important for the recognition and analysis of farmland anomaly patterns, such as drydown, endrow, nutrient deficiency, etc. Methods for general semantic segmentation such as Fully Convolutional Networks can extract rich semantic features, but are difficult to exploit the long-range information. Recently, vision Transformer architectures have made outstanding performances in image segmentation tasks, but transformer-based models have not been fully explored in the field of ***, we propose a novel architecture called Agricultural Aerial Transformer (AAFormer) to solve the semantic segmentation of aerial farmland images. We adopt Mix Transformer (MiT) in the encoder stage to enhance the ability of field anomaly pattern recognition and leverage the Squeeze-and-Excitation (SE) module in the decoder stage to improve the effectiveness of key channels. The boundary maps of farmland are introduced into the decoder. Evaluated on the Agriculture-vision validation set, the mIoU of our proposed model reaches 45.44%.

关键词： Image segmentation Image recognition conferences Semantics computer architecture Transformers Feature extraction

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Zero-shot Learning Using Multimodal Descriptions

Zero-shot Learning Using Multimodal Descriptions

引用

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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Multi-Class Cell Detection Using Modified Self-Attention

Multi-Class Cell Detection Using Modified Self-Attention

引用

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

作者： Sugimoto, Tatsuhiko Ito, Hiroaki Teramoto, Yuki Yoshizawa, Akihiko Bise, Ryoma Kyushu Univ Fukuoka Japan Kyoto Univ Hosp Kyoto Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Multi-class cell detection (cancer or non-cancer) from a whole slide image (WSI) is an important task for pathological diagnosis. Cancer and non-cancer cells often have a similar appearance, so it is difficult even for experts to classify a cell from a patch image of individual cells. They usually identify the cell type not only on the basis of the appearance of a single cell but also on the context of the surrounding cells. For using such information, we propose a multi-class cell-detection method that introduces a modified self-attention to aggregate the surrounding image features of both classes. Experimental results demonstrate the effectiveness of the proposed method;our method achieved the best performance compared with a method, which simply uses the standard self-attention method.

关键词： Heating systems Pathology computer vision Aggregates conferences Feature extraction pattern recognition

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