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Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing

Dressing in Order: Recurrent Person Image Generation for Pos...

引用

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

作者： Cui, Aiyu McKee, Daniel Lazebnik, Svetlana Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9781665448994

We propose a flexible person generation framework called Dressing in Order (DiOr), which supports 2D pose transfer, virtual try-on, and several fashion editing tasks. The key to DiOr is a novel recurrent generation pipeline to sequentially put garments on a person, so that trying on the same garments in different orders will result in different looks. Our system can produce dressing effects not achievable by existing work, including different interactions of garments (e.g., wearing a top tucked into the bottom or over it), as well as layering of multiple garments of the same type (e.g., jacket over shirt over t-shirt). DiOr explicitly encodes the shape and texture of each garment, enabling these elements to be edited separately. Extensive evaluations show that DiOr outperforms other recent methods like ADGAN [18] in terms of output quality, and handles a wide range of editing functions for which there is no direct supervision.

关键词： computer vision Shape Image synthesis conferences Clothing Pipelines pattern recognition

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Dual Contrastive Learning for Unsupervised Image-to-Image Translation

Dual Contrastive Learning for Unsupervised Image-to-Image Tr...

引用

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

作者： Han, Junlin Shoeiby, Mehrdad Petersson, Lars Armin, Mohammad Ali DATA61 CSIRO Canberra ACT Australia Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9781665448994

Unsupervised image-to-image translation tasks aim to find a mapping between a source domain X and a target domain Y from unpaired training data. Contrastive learning for Unpaired image-to-image Translation (CUT) yields state-of-the-art results in modeling unsupervised image-to-image translation by maximizing mutual information between input and output patches using only one encoder for both domains. In this paper, we propose a novel method based on contrastive learning and a dual learning setting (exploiting two encoders) to infer an efficient mapping between unpaired data. Additionally, while CUT suffers from mode collapse, a variant of our method efficiently addresses this issue. We further demonstrate the advantage of our approach through extensive ablation studies demonstrating superior performance comparing to recent approaches in multiple challenging image translation tasks. Lastly, we demonstrate that the gap between unsupervised methods and supervised methods can be efficiently closed.

关键词： computer vision conferences Training data pattern recognition Task analysis Mutual information

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Multi-View Mesh Reconstruction with Neural Deferred Shading

Multi-View Mesh Reconstruction with Neural Deferred Shading

引用

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

作者： Worchel, Markus Diaz, Rodrigo Hu, Weiwen Schreer, Oliver Feldmann, Ingo Eisert, Peter Fraunhofer HHI Berlin Germany TU Berlin Berlin Germany Queen Mary Univ London London England HU Berlin Berlin Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose an analysis-by-synthesis method for fast multi-view 3D reconstruction of opaque objects with arbitrary materials and illumination. State-of-the-art methods use both neural surface representations and neural rendering. While flexible, neural surface representations are a significant bottleneck in optimization runtime. Instead, we represent surfaces as triangle meshes and build a differentiable rendering pipeline around triangle rasterization and neural shading. The renderer is used in a gradient descent optimization where both a triangle mesh and a neural shader are jointly optimized to reproduce the multi-view images. We evaluate our method on a public 3D reconstruction dataset and show that it can match the reconstruction accuracy of traditional baselines and neural approaches while surpassing them in optimization runtime. Additionally, we investigate the shader and find that it learns an interpretable representation of appearance, enabling applications such as 3D material editing.

关键词： Surface reconstruction computer vision Three-dimensional displays Runtime Pipelines Lighting Rendering (computer graphics)

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Sign Language Production: A Review

Sign Language Production: A Review

引用

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

作者： Rastgoo, Razieh Kiani, Kourosh Escalera, Sergio Sabokrou, Mohammad Semnan Univ Semnan Iran Inst Res Fundamental Sci IPM Tehran Iran Univ Barcelona Barcelona Spain Comp Vis Ctr Barcelona Spain

ISBN: (纸本)9781665448994

Sign Language is the dominant yet non-primary form of communication language used in the deaf and hearing-impaired community. To make an easy and mutual communication between the hearing-impaired and the hearing communities, building a robust system capable of translating the spoken language into sign language and vice versa is fundamental. To this end, sign language recognition and production are two necessary parts for making such a two-way system. Sign language recognition and production need to cope with some critical challenges. In this survey, we review recent advances in Sign Language Production (SLP) and related areas using deep learning. This survey aims to briefly summarize recent achievements in SLP, discussing their advantages, limitations, and future directions of research.

关键词： Deep learning computer vision Assistive technology conferences Buildings Gesture recognition Production

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Manipulation Detection in Satellite Images Using vision Transformer

Manipulation Detection in Satellite Images Using Vision Tran...

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

作者： Horvath, Janos Baireddy, Sriram Hao, Hanxiang Montserrat, Daniel Mas Delp, Edward J. Purdue Univ Sch Elect & Comp Engn Video & Image Proc Lab VIPER W Lafayette IN 47907 USA

ISBN: (纸本)9781665448994

A growing number of commercial satellite companies provide easily accessible satellite imagery. Overhead imagery is used by numerous industries including agriculture, forestry, natural disaster analysis, and meteorology. Satellite images, just as any other images, can be tampered with image manipulation tools. Manipulation detection methods created for images captured by "consumer cameras" tend to fail when used on satellite images due to the differences in image sensors, image acquisition, and processing. In this paper we propose an unsupervised technique that uses a vision Transformer to detect spliced areas within satellite images. We introduce a new dataset which includes manipulated satellite images that contain spliced objects. We show that our proposed approach performs better than existing unsupervised splicing detection techniques.

关键词： Image sensors computer vision Satellites Splicing conferences Forestry Tools

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SBNet: Segmentation-based Network for Natural Language-based Vehicle Search

SBNet: Segmentation-based Network for Natural Language-based...

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

作者： Lee, Sangrok Woo, Taekang Lee, Sang Hun MODULABS Seoul South Korea NAVER Corp Seongnam Si South Korea Kookmin Univ Seoul South Korea

ISBN: (纸本)9781665448994

Natural language-based vehicle retrieval is a task to find a target vehicle within a given image based on a natural language description as a query. This technology can be applied to various areas including police searching for a suspect vehicle. However, it is challenging due to the ambiguity of language descriptions and the difficulty of processing multi-modal data. To tackle this problem, we propose a deep neural network called SBNet that performs natural language-based segmentation for vehicle retrieval. We also propose two task-specific modules to improve performance: a substitution module that helps features from different domains to be embedded in the same space and a future prediction module that learns temporal information. SBnet has been trained using the CityFlow-NL dataset that contains 2,498 tracks of vehicles with three unique natural language descriptions each and tested 530 unique vehicle tracks and their corresponding query sets. SBNet achieved a significant improvement over the baseline in the natural language-based vehicle tracking track in the AI City Challenge 2021. Source Code: https://***/lsrock1/nlp_search

关键词： Deep learning Image segmentation computer vision Law enforcement conferences Urban areas Natural languages

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DarkLight Networks for Action recognition in the Dark

DarkLight Networks for Action Recognition in the Dark

引用

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

作者： Chen, Rui Chen, Jiajun Liang, Zixi Gao, Huaien Lin, Shan Guangzhou Xi Ma Informat Technol Co 101 Waihuan Xi Rd Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781665448994

Human action recognition in the dark is a significant task with various applications, e.g., night surveillance and self-driving at night. However, the lack of video datasets for human actions in the dark hinders its development. Recently, a public dataset ARID has been introduced to stimulate progress for the task of human action recognition in dark videos. Currently, there are multiple models that perform well for action recognition in videos shot under normal illumination. However, research shows that these methods may not be effective in recognizing actions in dark videos. In this paper, we construct a novel neural network architecture: DarkLight Networks, which involves (i) a dual-pathway structure where both dark videos and its brightened counterpart are utilized for effective video representation;and (ii) a self-attention mechanism, which fuses and extracts corresponding and complementary features from the two pathways. Our approach achieves state-of-the-art results on ARID.

关键词： computer vision Fuses Surveillance conferences Neural networks Lighting Feature extraction

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Shot in the Dark: Few-Shot Learning with No Base-Class Labels

Shot in the Dark: Few-Shot Learning with No Base-Class Label...

引用

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

作者： Chen, Zitian Maji, Subhransu Learned-Miller, Erik Univ Massachusetts Amherst Amherst MA 01003 USA

ISBN: (纸本)9781665448994

Few-shot learning aims to build classifiers for new classes from a small number of labeled examples and is commonly facilitated by access to examples from a distinct set of 'base classes'. The difference in data distribution between the test set (novel classes) and the base classes used to learn an inductive bias often results in poor generalization on the novel classes. To alleviate problems caused by the distribution shift, previous research has explored the use of unlabeled examples from the novel classes, in addition to labeled examples of the base classes, which is known as the transductive setting. In this work, we show that, surprisingly, off-the-shelf self-supervised learning outperforms transductive few-shot methods by 3.9% for 5-shot accuracy on miniImageNet without using any base class labels. This motivates us to examine more carefully the role of features learned through self-supervision in few-shot learning. Comprehensive experiments are conducted to compare the transferability, robustness, efficiency, and the complementarity of supervised and self-supervised features.

关键词： computer vision conferences Supervised learning Robustness pattern recognition

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Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer

Pastiche Master: Exemplar-Based High-Resolution Portrait Sty...

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

作者： Yang, Shuai Jiang, Liming Liu, Ziwei Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent studies on StyleGAN show high performance on artistic portrait generation by transfer learning with limited data. In this paper, we explore more challenging exemplar-based high-resolution portrait style transfer by introducing a novel DualStyleGAN with flexible control of dual styles of the original face domain and the extended artistic portrait domain. Different from StyleGAN, DualStyleGAN provides a natural way of style transfer by characterizing the content and style of a portrait with an intrinsic style path and a new extrinsic style path, respectively. The delicately designed extrinsic style path enables our model to modulate both the color and complex structural styles hierarchically to precisely pastiche the style example. Furthermore, a novel progressive fine-tuning scheme is introduced to smoothly transform the generative space of the model to the target domain, even with the above modifications on the network architecture. Experiments demonstrate the superiority of DualStyleGAN over state-of-the-art methods in high-quality portrait style transfer and flexible style control. Code is available at https://***/williamyang1991/DualStyleGAN.

关键词： computer vision Codes Image color analysis Face recognition Computational modeling Transfer learning Transforms

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Engineering Sketch Generation for computer-Aided Design

Engineering Sketch Generation for Computer-Aided Design

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

作者： Willis, Karl D. D. Jayaraman, Pradeep Kumar Lambourne, Joseph G. Chu, Hang Pu, Yewen Autodesk Res Shanghai Peoples R China

ISBN: (纸本)9781665448994

Engineering sketches form the 2D basis of parametric computer-Aided Design (CAD), the foremost modeling paradigm for manufactured objects. In this paper we tackle the problem of learning based engineering sketch generation as a first step towards synthesis and composition of parametric CAD models. We propose two generative models, CurveGen and TurtleGen, for engineering sketch generation. Both models generate curve primitives without the need for a sketch constraint solver and explicitly consider topology for downstream use with constraints and 3D CAD modeling operations. We find in our perceptual evaluation using human subjects that both CurveGen and TurtleGen produce more realistic engineering sketches when compared with the current state-of-the-art for engineering sketch generation.

关键词： Solid modeling Design automation Three-dimensional displays Computational modeling conferences Solids Topology

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