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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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HairMapper: Removing Hair from Portraits Using GANs

HairMapper: Removing Hair from Portraits Using GANs

引用

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

作者： Wu, Yiqian Yang, Yong-Liang Jin, Xiaogang Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China ZJU Tencent Game & Intelligent Graph Innovat Tech Hangzhou Peoples R China Univ Bath Bath Avon England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Removing hair from portrait images is challenging due to the complex occlusions between hair and face, as well as the lack of paired portrait data with/without hair. To this end, we present a dataset and a baseline method for removing hair from portrait images using generative adversarial networks (GANs). Our core idea is to train a fully connected network Hair Mapper to find the direction of hair removal in the latent space of StyleGAN for the training stage. We develop a new separation boundary and diffuse method to generate paired training data for males, and a novel 'female-male-bald" pipeline for paired data of females. Experiments show that our method can naturally deal with portrait images with variations on gender, age, etc. We validate the superior performance of our method by comparing it to state-of-the-art methods through extensive experiments and user studies. We also demonstrate its applications in hair design and 3D face reconstruction.

关键词： Hair Training Photography computer vision Three-dimensional displays Face recognition Pipelines

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Hyperspectral Image Super-Resolution with Optimized RGB Guidance 32

Hyperspectral Image Super-Resolution with Optimized RGB Guid...

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Fu, Ying Zhang, Tao Zheng, Yinqiang Zhang, Debing Huang, Hua Beijing Inst Technol Beijing Peoples R China Natl Inst Informat Tokyo Japan DeepGlint Beijing Peoples R China

ISBN: (纸本)9781728132938

To overcome the limitations of existing hyperspectral cameras on spatial/temporal resolution, fusing a low resolution hyperspectral image (HSI) with a high resolution RGB (or multispectral) image into a high resolution HSI has been prevalent. Previous methods for this fusion task usually employ hand-crafted priors to model the underlying structure of the latent high resolution HSI, and the effect of the camera spectral response (CSR) of the RGB camera on super-resolution accuracy has rarely been investigated. In this paper, we first present a simple and efficient convolutional neural network (CNN) based method for HSI super-resolution in an unsupervised way, without any prior training. Later, we append a CSR optimization layer onto the HSI super-resolution network, either to automatically select the best CSR in a given CSR dataset, or to design the optimal CSR under some physical restrictions. Experimental results show our method outperforms the state-of-the-arts, and the CSR optimization can further boost the accuracy of HSI super-resolution.

关键词： Computational Photography Deep Learning Low-level vision Physics-based vision and Shape-from-X

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Exploring Adversarial Fake Images on Face Manifold

Exploring Adversarial Fake Images on Face Manifold

引用

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

作者： Li, Dongze Wang, Wei Fan, Hongxing Dong, Jing Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China CASIA Ctr Res Intelligent Percept & Comp Beijing Peoples R China

ISBN: (纸本)9781665445092

Images synthesized by powerful generative adversarial network (GAN) based methods have drawn moral and privacy concerns. Although image forensic models have reached great performance in detecting fake images from real ones, these models can be easily fooled with a simple adversarial attack. But, the noise adding adversarial samples are also arousing suspicion. In this paper, instead of adding adversarial noise, we optimally search adversarial points on face manifold to generate anti forensic fake face images. We iteratively do a gradient-descent with each small step in the latent space of a generative model, e.g. Style-GAN, to find an adversarial latent vector, which is similar to norm-based adversarial attack but in latent space. Then, the generated fake images driven by the adversarial latent vectors with the help of GANs can defeat main-stream forensic models. For examples, they make the accuracy of deepfake detection models based on Xception or EfficientNet drop from over 90% to nearly 0%, meanwhile maintaining high visual quality. In addition, we find manipulating noise vectors n at different levels have different impacts on attack success rate, and the generated adversarial images mainly have changes on facial texture or face attributes.

关键词： Manifolds Visualization Privacy Image forensics Ethics computer vision Face recognition

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FOCNet: A Fractional Optimal Control Network for Image Denoising 32

FOCNet: A Fractional Optimal Control Network for Image Denoi...

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Jia, Xixi Liu, Sanyang Feng, Xiangchu Zhang, Lei Xidian Univ Sch Math & Stat Xian Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China

ISBN: (纸本)9781728132938

Deep convolutional neural networks (DCNN) have been successfully used in many low-level vision problems such as image denoising. Recent studies on the mathematical foundation of DCNN has revealed that the forward propagation of DCNN corresponds to a dynamic system, which can be described by an ordinary differential equation (ODE) and solved by the optimal control method. However, most of these methods employ integer-order differential equation, which has local connectivity in time space and cannot describe the long-term memory of the system. Inspired by the fact that the fractional-order differential equation has long-term memory, in this paper we develop an advanced image denoising network, namely FOCNet, by solving a fractional optimal control (FOC) problem. Specifically, the network structure is designed based on the discretization of a fractional-order differential equation, which enjoys long-term memory in both forward and backward passes. Besides, multi-scale feature interactions are introduced into the FOCNet to strengthen the control of the dynamic system. Extensive experiments demonstrate the leading performance of the proposed FOCNet on image denoising. Code will be made available.

关键词： Deep Learning Low-level vision

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Maximum Consensus by Weighted Influences of Monotone Boolean Functions

Maximum Consensus by Weighted Influences of Monotone Boolean...

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

作者： Zhang, Erchuan Suter, David Tennakoon, Ruwan Chin, Tat-Jun Bab-Hadiashar, Alireza Truong, Giang Gilani, Syed Zulqarnain EdithCowanUniv Ctr AI & ML Perth WA Australia Edith Cowan Univ Nutr & Hlth Innovat Res Inst Perth WA Australia RMIT Univ Melbourne Vic Australia Univ Adelaide Adelaide SA Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Maximisation of Consensus (MaxCon) is one of the most widely used robust criteria in computer vision. Tennakoon et al. (CVPR2021), made a connection between MaxCon and estimation of influences of a Monotone Boolean function. In such, there are two distributions involved: the distribution defining the influence measure;and the distribution used for sampling to estimate the influence measure. This paper studies the concept of weighted influences for solving MaxCon. In particular, we study the Bernoulli measures. Theoretically, we prove the weighted influences, under this measure, of points belonging to larger structures are smaller than those of points belonging to smaller structures in general. We also consider another "natural" family of weighting strategies: sampling with uniform measure concentrated on a particular (Hamming) level of the cube. One can choose to have matching distributions: the same for defining the measure as for implementing the sampling. This has the advantage that the sampler is an unbiased estimator of the measure. Based on weighted sampling, we modify the algorithm of Tennakoon et al., and test on both synthetic and real datasets. We show some modest gains of Bernoulli sampling, and we illuminate some of the interactions between structure in data and weighted measures and weighted sampling.

关键词： Weight measurement computer vision Boolean functions Atmospheric measurements Machine vision Estimation Gain measurement

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Learning Affinity-Aware Upsampling for Deep Image Matting

Learning Affinity-Aware Upsampling for Deep Image Matting

引用

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

作者： Dai, Yutong Lu, Hao Shen, Chunhua Univ Adelaide Adelaide SA Australia Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9781665445092

We show that learning affinity in upsampling provides an effective and efficient approach to exploit pairwise interactions in deep networks. Second-order features are commonly used in dense prediction to build adjacent relations with a learnable module after upsampling such as non-local blocks. Since upsampling is essential, learning affinity in upsampling can avoid additional propagation layers, offering the potential for building compact models. By looking at existing upsampling operators from a unified mathematical perspective, we generalize them into a second-order form and introduce Affinity-Aware Upsampling (A(2)U) where upsampling kernels are generated using a light-weight low-rank bilinear model and are conditioned on second-order features. Our upsampling operator can also be extended to downsampling. We discuss alternative implementations of A(2)U and verify their effectiveness on two detail-sensitive tasks: image reconstruction on a toy dataset;and a large-scale image matting task where affinity-based ideas constitute mainstream matting approaches. In particular, results on the Composition-1k matting dataset show that A(2)U achieves a 14% relative improvement in the SAD metric against a strong baseline with negligible increase of parameters ( < 0.5%). Compared with the state-of-the-art matting network, we achieve 8% higher performance with only 40% model complexity.

关键词： Measurement Computational modeling Buildings Toy manufacturing industry Predictive models Mathematical models pattern recognition

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Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning

Predicting Goal-directed Human Attention Using Inverse Reinf...

引用

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

作者： Yang, Zhibo Huang, Lihan Chen, Yupei Wei, Zijun Ahn, Seoyoung Zelinsky, Gregory Samaras, Dimitris Hoai, Minh SUNY Stony Brook Stony Brook NY 11794 USA Adobe Inc San Jose CA USA

ISBN: (纸本)9781728171685

Human gaze behavior prediction is important for behavioral vision and for computer vision applications. Most models mainly focus on predicting free-viewing behavior using saliency maps, but do not generalize to goal-directed behavior, such as when a person searches for a visual target object. We propose the first inverse reinforcement learning (IRL) model to learn the internal reward function and policy used by humans during visual search. We modeled the viewer's internal belief states as dynamic contextual belief maps of object locations. These maps were learned and then used to predict behavioral scanpaths for multiple target categories. To train and evaluate our IRL model we created COCO-Search18, which is now the largest dataset of high-quality search fixations in existence. COCO-Search18 has 10 participants searching for each of 18 target-object categories in 6202 images, making about 300,000 goal-directed fixations. When trained and evaluated on COCO-Search18, the IRL model outperformed baseline models in predicting search fixation scanpaths, both in terms of similarity to human search behavior and search efficiency. Finally, reward maps recovered by the IRL model reveal distinctive target-dependent patterns of object prioritization, which we interpret as a learned object context.

关键词： computer vision

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Self-Supervised 3D Mesh Reconstruction from Single Images

Self-Supervised 3D Mesh Reconstruction from Single Images

引用

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

作者： Hu, Tao Wang, Liwei Xu, Xiaogang Liu, Shu Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Recent single-view 3D reconstruction methods reconstruct object's shape and texture from a single image with only 2D image-level annotation. However, without explicit 3D attribute-level supervision, it is still difficult to achieve satisfying reconstruction accuracy. In this paper, we propose a Self-supervised Mesh Reconstruction (SMR) approach to enhance 3D mesh attribute learning process. Our approach is motivated by observations that (1) 3D attributes from interpolation and prediction should be consistent, and (2) feature representation of landmarks from all images should be consistent. By only requiring silhouette mask annotation, our SMR can be trained in an end-to-end manner and generalizes to reconstruct natural objects of birds, cows, motorbikes, etc. Experiments demonstrate that our approach improves both 2D supervised and unsupervised 3D mesh reconstruction on multiple datasets. We also show that our model can be adapted to other image synthesis tasks, e.g., novel view generation, shape transfer, and texture transfer, with promising results.

关键词： Solid modeling Three-dimensional displays Annotations Shape Motorcycles Reconstruction algorithms pattern recognition

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***: Decomposition and Reasoning for Video Individual Counting

***: Decomposition and Reasoning for Video Individual Counti...

引用

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

作者： Han, Tao Bai, Lei Gao, Junyu Wang, Qi Ouyang, Wanli Northwestern Polytech Univ Xian 710072 Peoples R China Univ Sydney SenseTime Comp Vis Grp Camperdown NSW Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Pedestrian counting is a fundamental tool for understanding pedestrian patterns and crowd flow analysis. Existing works (e.g., image-level pedestrian counting, cross-line crowd counting et al.) either only focus on the image-level counting or are constrained to the manual annotation of lines. In this work, we propose to conduct the pedestrian counting from a new perspective - Video Individual Counting (VIC), which counts the total number of individual pedestrians in the given video (a person is only counted once). Instead of relying on the Multiple Object Tracking own techniques, we propose to solve the problem by decomposing all pedestrians into the initial pedestrians who existed in the first frame and the new pedestrians with separate identities in each following frame. Then, an end-to-end Decomposition and Reasoning Network (DRNet) is designed to predict the initial pedestrian count with the density estimation method and reason the new pedestrian's count of each frame with the differentiable optimal transport. Extensive experiments are conducted on two datasets with congested pedestrians and diverse scenes, demonstrating the effectiveness of our method over baselines with great superiority in counting the individual pedestrians. Code: https://***/taohan10200/DRNet.

关键词： computer vision Codes Annotations Estimation Manuals Cognition pattern recognition

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Cast shadow removal combining local and global features

Cast shadow removal combining local and global features

引用

IEEE conference on computer vision and pattern recognition

作者： Liu, Zhou Huang, Kaiqi Tan, Tieniu Wang, Liangsheng Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China

ISBN: (纸本)9781424411795

In this paper, we present a method using pixel-level information, local region-level information and global-level information to remove shadow. At the pixel-level, we employ GMM to model the behavior of cast shadow for every pixel in the HSV color space, as it can deal with complex illumination conditions. However, unlike the GMM for background which can obtain sample every frame, this model for shadow needs more frames to get the same number of sample, because shadow may not appear at the same pixel for each frame. Therefore, it will take a long time to converge. To overcome this drawback, we use the local region-level information to get more samples and global-level information to improve a preclassifier and then, by using it, we get samples which are more likely to be shadow. Also, at the local region-level, we use Markov random fields to represent dependencies between the label of single pixel and labels of its neighborhood. Moreover, to make global level information more robust, tracking information is used. Experimental results show that the proposed method is efficient and robust.

关键词： Feature extraction

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