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Neural Image Recolorization for Creative Domains

Neural Image Recolorization for Creative Domains

引用

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

作者： Li, Boyi Belongie, Serge Lim, Ser-nam Davis, Abe Cornell Univ Ithaca NY 14853 USA Univ Copenhagen Copenhagen Denmark Meta AI New York NY USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present a self-supervised approach to recolorization of images from design-oriented domains. Our approach can recolor images based on image exemplars or target color palettes provided by a user. In contrast with previous approaches, our method can reproduce color palettes with luminance distributions that differ significantly from input, and our method is the first palette-based approach to distinguish between recolorings that match reflectance and those that match illumination, making it particularly well-suited to visualizing different aesthetic decisions in design applications. The key to our approach is first to learn latent representations for texture and color in a setting where self-supervision is especially straightforward, and then to learn a mapping to our color representation from input color palettes and scene illumination, which offers a more intuitive space for controlling and exploring recolorization.

关键词： Reflectivity Deep learning Visualization computer vision Image color analysis conferences Lighting

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CoRe: Color Regression for Multicolor Fashion Garments

CoRe: Color Regression for Multicolor Fashion Garments

引用

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

作者： Rame, Alexandre Douillard, Arthur Ollion, Charles Sorbonne Univ Paris France Heuritech Paris France

ISBN: (纸本)9781665487399

Developing deep networks that analyze fashion garments has many real-world applications. Among all fashion attributes, color is one of the most important yet challenging to detect. Existing approaches are classification-based and thus cannot go beyond the list of discrete predefined color names. In this paper, we handle color detection as a regression problem to predict the exact RGB values. That's why in addition to a first color classifier, we include a second regression stage for refinement in our newly proposed architecture. This second step combines two attention models: the first depends on the type of clothing, the second depends on the color previously detected by the classifier. Our final prediction is the weighted spatial pooling over the image pixels RGB values, where the illumination has been corrected. This architecture is modular and easily expanded to detect the RGBs of all colors in a multicolor garment. In our experiments, we show the benefits of each component of our architecture.

关键词： Visualization computer vision Image color analysis conferences Clothing Lighting computer architecture

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Symmetric Parallax Attention for Stereo Image Super-Resolution

Symmetric Parallax Attention for Stereo Image Super-Resoluti...

引用

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

作者： Wang, Yingqian Ying, Xinyi Wang, Longguang Yang, Jungang An, Wei Guo, Yulan Natl Univ Def Technol Zunyi Guizhou Peoples R China

ISBN: (纸本)9781665448994

Although recent years have witnessed the great advances in stereo image super-resolution (SR), the beneficial information provided by binocular systems has not been fully used. Since stereo images are highly symmetric under epipolar constraint, in this paper, we improve the performance of stereo image SR by exploiting symmetry cues in stereo image pairs. Specifically, we propose a symmetric bi-directional parallax attention module (biPAM) and an inline occlusion handling scheme to effectively interact cross-view information. Then, we design a Siamese network equipped with a biPAM to super-resolve both sides of views in a highly symmetric manner. Finally, we design several illuminance-robust losses to enhance stereo consistency. Experiments on four public datasets demonstrate the superior performance of our method.

关键词： Training computer vision conferences Superresolution Estimation Bidirectional control Robustness

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Edge Guided Progressively Generative Image Outpainting

Edge Guided Progressively Generative Image Outpainting

引用

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

作者： Lin, Han Pagnucco, Maurice Song, Yang Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (纸本)9781665448994

Deep-learning based generative models are proven to be capable for achieving excellent results in numerous image processing tasks with a wide range of applications. One significant improvement of deep-learning approaches compared to traditional approaches is their ability to regenerate semantically coherent images by only relying on an input with limited information. This advantage becomes even more crucial when the input size is only a very minor proportion of the output size. Such image expansion tasks can be more challenging as the missing area may originally contain many semantic features that are critical in judging the quality of an image. In this paper we propose an edge-guided generative network model for producing semantically consistent output from a small image input. Our experiments show the proposed network is able to regenerate high quality images even when some structural features are missing in the input.

关键词： Measurement computer vision Image edge detection Computational modeling Image processing conferences Semantics

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Medusa: Universal Feature Learning via Attentional Multitasking

Medusa: Universal Feature Learning via Attentional Multitask...

引用

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

作者： Spencer, Jaime Bowden, Richard Hadfield, Simon Univ Surrey Ctr Vis Speech & Signal Proc CVSSP Guildford Surrey England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent approaches to multi-task learning (MTL) have focused on modelling connections between tasks at the decoder level. This leads to a tight coupling between tasks, which need retraining if a new task is inserted or removed. We argue that MTL is a stepping stone towards universal feature learning (UFL), which is the ability to learn generic features that can be applied to new tasks without retraining. We propose Medusa to realize this goal, designing task heads with dual attention mechanisms. The shared feature attention masks relevant backbone features for each task, allowing it to learn a generic representation. Meanwhile, a novel Multi-Scale Attention head allows the network to better combine per-task features from different scales when making the final prediction. We show the effectiveness of Medusa in UFL (+13.18% improvement), while maintaining MTL performance and being 25% more efficient than previous approaches.

关键词： Representation learning Couplings computer vision conferences Multitasking pattern recognition Decoding

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Artistic Style Novel View Synthesis Based on A Single Image

Artistic Style Novel View Synthesis Based on A Single Image

引用

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

作者： Tseng, Kuan-Wei Lee, Yao-Chih Chen, Chu-Song Natl Taiwan Univ Taipei Taiwan Tokyo Inst Technol Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent progress in 3D display technologies has raised the demand for stylized 3D digital content. Previous approaches either perform style transfer on stereoscopic image pairs or reconstruct 3D environment with multiple view images. In this paper, we propose a novel view stylization framework that can convert a single 2D image into multiple stylized views. It is a two-stage solution that contains view synthesis and neural style transfer. We estimate dense optical flow between the source and novel views so that the style transfer model can produce consistent results. Experimental results show that our method significantly improves the consistency among views compared to the baseline method.

关键词： computer vision Three-dimensional displays Stereo image processing conferences Computational modeling pattern recognition Optical flow

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SymDNN: Simple & Effective Adversarial Robustness for Embedded Systems

SymDNN: Simple & Effective Adversarial Robustness for Embedd...

引用

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

作者： Dey, Swarnava Dasgupta, Pallab Chakrabarti, Partha P. Indian Inst Technol Kharagpur Kharagpur 721302 W Bengal India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose SymDNN, a Deep Neural Network (DNN) inference scheme, to segment an input image into small patches, replace those patches with representative symbols, and use the reconstructed image for CNN inference. This approach of deconstruction of images, and the reconstruction from cluster centroids trained on clean images, enhances robustness against adversarial attacks. The input transform used in SymDNN is learned from very large datasets, making it difficult to approximate for adaptive adversarial attacks. For example, SymDNN achieves 23% and 42% robust accuracy at L-infinity attack strengths of 8/255 and 4/255 respectively, against BPDA under a complete white box setting, where most input processing based defenses break completely. SymDNN is not a future-proof adversarial defense that can defend any attack, but it is one of the few readily usable defenses in resource-limited embedded systems that defends against a wide range of attacks.

关键词： computer vision Adaptation models Visualization Embedded systems Computational modeling Symbols Transforms

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Instagram Filter Removal on Fashionable Images

Instagram Filter Removal on Fashionable Images

引用

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

作者： Kinli, Furkan Ozcan, Baris Kirac, Furkan Ozyegin Univ Video Vis & Graph Lab Istanbul Turkey

ISBN: (纸本)9781665448994

Social media images are generally transformed by filtering to obtain aesthetically more pleasing appearances. However, CNNs generally fail to interpret both the image and its filtered version as the same in the visual analysis of social media images. We introduce Instagram Filter Removal Network (IFRNet) to mitigate the effects of image filters for social media analysis applications. To achieve this, we assume any filter applied to an image substantially injects a piece of additional style information to it, and we consider this problem as a reverse style transfer problem. The visual effects of filtering can be directly removed by adaptively normalizing external style information in each level of the encoder. Experiments demonstrate that IFRNet outperforms all compared methods in quantitative and qualitative comparisons, and has the ability to remove the visual effects to a great extent. Additionally, we present the filter classification performance of our proposed model, and analyze the dominant color estimation on the images unfiltered by all compared methods.

关键词： computer vision Image color analysis Social networking (online) conferences Multimedia Web sites Estimation Information filters

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3D Room Layout Recovery Generalizing across Manhattan and Non-Manhattan Worlds

3D Room Layout Recovery Generalizing across Manhattan and No...

引用

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

作者： Jia, Haijing Yi, Hong Fujiki, Hirochika Zhang, Hengzhi Wang, Wei Odamaki, Makoto Ricoh Software Res Ctr Beijing Co Ltd Beijing Peoples R China Ricoh Co Ltd Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent 3D room layout recovery approaches mostly concentrate on Manhattan layouts, where the vertical walls are orthogonal with respect to each other, even though there are many rooms with non-Manhattan layouts in the real world. This paper presents a room layout recovery method generalizing across Manhattan and non-Manhattan worlds. Without introducing additional supervision, we extend current Manhattan layout recovery methods by predicting an extra surface normal feature, which is further used for an adaptive post-processing to reconstruct layouts of arbitrary shapes. Experimental results show that our method has a great improvement on non-Manhattan layouts while being capable of generalizing across Manhattan and non-Manhattan layouts.

关键词： Surface reconstruction computer vision Three-dimensional displays Shape conferences Layout Transformers

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Table Tennis Stroke recognition Using Two-Dimensional Human Pose Estimation

Table Tennis Stroke Recognition Using Two-Dimensional Human ...

引用

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

作者： Kulkarni, Kaustubh Milind Shenoy, Sucheth

ISBN: (纸本)9781665448994

We introduce a novel method for collecting table tennis video data and perform stroke detection and classification. A diverse dataset containing video data of 11 basic strokes obtained from 14 professional table tennis players, summing up to a total of 22111 videos has been collected using the proposed setup. The temporal convolutional neural network model developed using 2D pose estimation performs multiclass classification of these 11 table tennis strokes with a validation accuracy of 99.37%. Moreover, the neural network generalizes well over the data of a player excluded from the training and validation dataset, classifying the fresh strokes with an overall best accuracy of 98.72%. Various model architectures using machine learning and deep learning based approaches have been trained for stroke recognition and their performances have been compared and benchmarked. Inferences such as performance monitoring and stroke comparison of the players using the model have been discussed. Therefore, we are contributing to the development of a computer vision based sports analytics system for the sport of table tennis that focuses on the previously unexploited aspect of the sport i.e., a player's strokes, which is extremely insightful for performance improvement.

关键词： Training Deep learning computer vision Computational modeling Pose estimation Neural networks Stroke (medical condition)

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