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共 23198 条记录，以下是241-250 订阅

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Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity

Proposal-free Lidar Panoptic Segmentation with Pillar-level ...

引用

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

作者： Chen, Qi Vora, Sourabh Johns Hopkins Univ Baltimore MD 21218 USA Motional Boston MA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose a simple yet effective proposal-free architecture for lidar panoptic segmentation. We jointly optimize both semantic segmentation and class-agnostic instance classification in a single network using a pilla-rbased bird's-eye view representation. The instance classification head learns pairwise affinity between pillars to determine whether the pillars belong to the same instance or not. We further propose a local clustering algorithm to propagate instance ids by merging semantic segmentation and affinity predictions. Our experiments on nuScenes dataset show that our approach outperforms previous proposal-free methods and is comparable to proposal-based methods which requires extra annotation from object detection.

关键词： computer vision Laser radar conferences Semantics Merging Clustering algorithms Object detection

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Towards Detailed Characteristic-Preserving Virtual Try-On

Towards Detailed Characteristic-Preserving Virtual Try-On

引用

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

作者： Lee, Sangho Lee, Seoyoung Lee, Joonseok Seoul Natl Univ Seoul South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

While virtual try-on has rapidly progressed recently, existing virtual try-on methods still struggle to faithfully represent various details of the clothes when worn. In this paper, we propose a simple yet effective method to better preserve details of the clothing and person by introducing an additional fitting step after geometric warping. This minimal modification enables disentangling representations of the clothing from the wearer, hence we are able to preserve the wearer-agnostic structure and details of the clothing, to fit a garment naturally to a variety of poses and body shapes. Moreover, we propose a novel evaluation framework applicable to any metric, to better reflect the semantics of clothes fitting. From extensive experiments, we empirically verify that the proposed method not only learns to disentangle clothing from the wearer, but also preserves details of the clothing on the try-on results.

关键词： Measurement computer vision Shape conferences Clothing Fitting Semantics

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An Introduction to the 3rd Workshop on Egocentric (First-person) vision

An Introduction to the 3rd Workshop on Egocentric (First-per...

引用

27th ieee conference on computer vision and pattern recognition (CVPR)

作者： Mann, Steve Kitani, Kris M. Lee, Yong Jae Ryoo, M. S. Fathi, Alireza

ISBN: (纸本)9781479943098

Egocentric vision provides a unique perspective of the visual world that is inherently human-centric. Since egocentric cameras are mounted on the user (typically on the user's head), they are naturally primed to gather visual information from our everyday interactions, and can even act on that information in real-time (e. g. for a vision aid). We believe that this human-centric characteristic of egocentric vision can have a large impact on the way we approach central computer vision tasks such as visual detection, recognition, prediction, and socio-behavioral analysis. By taking advantage of the first-person point-of-view paradigm, there have been recent advances in areas such as personalized video summarization, understanding concepts of social saliency, activity analysis with inside-out cameras (a camera to capture eye gaze and an outward-looking camera), recognizing human interactions and modeling focus of attention. However, in many ways people are only beginning to understand the full potential (and limitations) of the first-person paradigm. In the 3rd workshop on Egocentric (First-Person) vision, we bring together researchers to discuss emerging topics such as: Personalization of visual analysis;Socio-behavioral modeling;Understanding group dynamics and interactions;Egocentric video as big data;First-person vision for robotics;and Egographical User Interfaces (EUIs).

关键词： Sight Cameras group dynamics workshops workshops full potential robotics

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Perceptual Image Compression using Relativistic Average Least Squares GANs

Perceptual Image Compression using Relativistic Average Leas...

引用

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

作者： Cheng, Zhengxue Fu, Ting Hu, Jiapeng Guo, Li Wang, Shihao Zhao, Xiongxin Zhou, Dajiang Song, Yang Ant Grp Hangzhou Peoples R China

ISBN: (纸本)9781665448994

In this work, we provide a detailed description on our submitted methods ANTxNN and ANTxNN SSIM to Workshop and Challenge on Learned Image Compression (CLIC) 2021. We propose to incorporate Relativistic average Least Squares GANs (RaLSGANs) into Rate-Distortion Optimization for end-to-end training, to achieve perceptual image compression. We also compare two types of discriminator networks and visualize their reconstructed images. Experimental results have validated our method optimized by RaLSGANs can achieve higher subjective quality compared to PSNR, MS-SSIM or LPIPS-optimized models.

关键词： Training Visualization computer vision Image coding conferences Rate-distortion Generative adversarial networks

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OutfitGAN: Learning Compatible Items for Generative Fashion Outfits

OutfitGAN: Learning Compatible Items for Generative Fashion ...

引用

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

作者： Moosaei, Maryam Lin, Yusan Akhazhanov, Ablaikhan Chen, Huiyuan Wang, Fei Yang, Hao Visa Res San Francisco CA 94158 USA Univ Calif Los Angeles Los Angeles CA 90024 USA Nazarbayev Univ Astana Kazakhstan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Fashion-on-demand is becoming an important concept for fashion industries. Many attempts have been made to leverage machine learning methods to generate fashion designs tailored to customers' tastes. However, how to assemble items together (e.g., compatibility) is crucial in designing high-quality outfits for synthesis images. Here we propose a fashion generation model, named OutfitGAN, which contains two core modules: a Generative Adversarial Network and a Compatibility Network. The generative module is able to generate new realistic high quality fashion items from a specific category, while the compatibility network ensures reasonable compatibility among all items. The experimental results show the superiority of our OutfitGAN.

关键词： Industries computer vision conferences Machine learning Generative adversarial networks pattern recognition

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Anomaly Detection in Autonomous Driving: A Survey

Anomaly Detection in Autonomous Driving: A Survey

引用

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

作者： Bogdoll, Daniel Nitsche, Maximilian Zoellner, J. Marius FZI Res Ctr Informat Technol Karlsruhe Germany KIT Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9781665487399

Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the unexpected. This survey provides an extensive overview of anomaly detection techniques based on camera, lidar, radar, multimodal and abstract object level data. We provide a systematization including detection approach, corner case level, ability for an online application, and further attributes. We outline the state-of-the-art and point out current research gaps.

关键词： computer vision Laser radar Roads conferences Radar detection Benchmark testing Cameras

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Three Gaps for Quantisation in Learned Image Compression

Three Gaps for Quantisation in Learned Image Compression

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

作者： Pan, Shi Finlay, Chris Besenbruch, Chri Knottenbelt, William Imperial Coll London Dept Comp London England DeepRender London England

ISBN: (纸本)9781665448994

Learned lossy image compression has demonstrated impressive progress via end-to-end neural network training. However, this end-to-end training belies the fact that lossy compression is inherently not differentiable, due to the necessity of quantisation. To overcome this difficulty in training, researchers have used various approximations to the quantisation step. However, little work has studied the mechanism of quantisation approximation itself. We address this issue, identifying three gaps arising in the quantisation approximation problem. These gaps are visualised, and show the effect of applying different quantisation approximation methods. Following this analysis, we propose a Soft-STE quantisation approximation method, which closes these gaps and demonstrates better performance than other quantisation approaches on the Kodak dataset.

关键词： Training Visualization computer vision Quantization (signal) Image coding conferences Neural networks

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Fuse-PN: A Novel Architecture for Anomaly pattern Segmentation in Aerial Agricultural Images

Fuse-PN: A Novel Architecture for Anomaly Pattern Segmentati...

引用

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

作者： Innani, Shubham Dutande, Prasad Baheti, Bhakti Talbar, Sanjay Baid, Ujjwal SGGS Inst Engn & Technol Ctr Excellence Signal & Image Proc Nanded 431606 India

ISBN: (纸本)9781665448994

Deep learning and pattern recognition in smart farming has seen rapid growth as a building bridge between crop science and computer vision. One of the important application is anomaly segmentation in agriculture like weed, standing water, cloud shadow, etc. Our research work focuses on aerial farmland image dataset known as Agriculture vision. We propose to have data fusion of R, G, B, and NIR modalities that enhances the feature extraction and also propose Efficient Fused Pyramid Network (Fuse-PN) for anomaly pattern segmentation. The proposed encoder module is a bottom-up pathway having a compound scaled network and decoder module is a top-down pyramid network enhancing features at different scales having rich semantic features with lateral connections of low-level features. This proposed approach achieved a mean dice similarity score of 0.8271 for six agricultural anomaly patterns of Agriculture vision dataset and outperforms various approaches in literature.

关键词： Deep learning Training Image segmentation computer vision Semantics Data integration computer architecture

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Modeling Fashion Compatibility with Explanation by using Bidirectional LSTM

Modeling Fashion Compatibility with Explanation by using Bid...

引用

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

作者： Pang Kaicheng Zou Xingxing Wong, Wai Keung Hong Kong Polytech Univ Inst Text & Clothing Hong Kong Peoples R China Lab Artificial Intelligence Design Hong Kong Peoples R China

ISBN: (纸本)9781665448994

The goal of this paper is to model the fashion compatibility of an outfit and provide the explanations. We first extract features of all attributes of all items via convolutional neural networks, and then train the bidirectional Long Shortterm Memory (Bi-LSTM) model to learn the compatibility of an outfit by treating these attribute features as a sequence. Gradient penalty regularization is exploited for training inter-factor compatibility net which is used to compute the loss for judgment and provide its explanation which is generated from the recognized reasons related to the judgment. To train and evaluate the proposed approach, we expanded the EVALUATION3 dataset in terms of the number of items and attributes. Experiment results show that our approach can successfully evaluate compatibility with reason.

关键词： Training Industries computer vision Computational modeling conferences Feature extraction pattern recognition

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Conditioned and composed image retrieval combining and partially fine-tuning CLIP-based features

Conditioned and composed image retrieval combining and parti...

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

作者： Baldrati, Alberto Bertini, Marco Uricchio, Tiberio Del Bimbo, Alberto Univ Firenze MICC Florence Italy Univ Pisa Pisa Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we present an approach for conditioned and composed image retrieval based on CLIP features. In this extension of content-based image retrieval (CBIR) an image is combined with a text that provides information regarding user intentions, and is relevant for application domains like e-commerce. The proposed method is based on an initial training stage where a simple combination of visual and textual features is used, to fine-tune the CLIP text encoder. Then in a second training stage we learn a more complex combiner network that merges visual and textual features. Contrastive learning is used in both stages. The proposed approach obtains state-of-the-art performance for conditioned CBIR on the FashionIQ dataset and for composed CBIR on the more recent CIRR dataset.

关键词： Training Visualization computer vision conferences Image retrieval Solids pattern recognition

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