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Blind Deblurring for Saturated Images

Blind Deblurring for Saturated Images

引用

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

作者： Chen, Liang Zhang, Jiawei Lin, Songnan Fang, Faming Ren, Jimmy S. East China Normal Univ Proc Sch Comp Sci & Technol Shanghai Key Lab Multidimens Informat Shanghai Peoples R China SenseTime Res Shanghai Peoples R China Shanghai Jiao Tong Univ Qing Yuan Res Inst Shanghai Peoples R China

ISBN: (纸本)9781665445092

Blind deblurring has received considerable attention in recent years. However, state-of-the-art methods often fail to process saturated blurry images. The main reason is that pixels around saturated regions are not conforming to the commonly used linear blur model. Pioneer arts suggest excluding these pixels during the deblurring process, which sometimes simultaneously removes the informative edges around saturated regions and results in insufficient information for kernel estimation when large saturated regions exist. To address this problem, we introduce a new blur model to fit both saturated and unsaturated pixels, and all informative pixels can be considered during the deblurring process. Based on our model, we develop an effective maximum a posterior (MAP)-based optimization framework. Quantitative and qualitative evaluations on benchmark datasets and challenging real-world examples show that the proposed method performs favorably against existing methods.

关键词： computer vision Art Image edge detection Estimation Benchmark testing pattern recognition Image restoration

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SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation

SSTVOS: Sparse Spatiotemporal Transformers for Video Object ...

引用

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

作者： Duke, Brendan Ahmed, Abdalla Wolf, Christian Aarabi, Parham Taylor, Graham W. Univ Toronto Toronto ON Canada Univ Guelph Guelph ON Canada Univ Lyon INSA Lyon LIRIS Lyon France Modiface Inc Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9781665445092

In this paper we introduce a Transformer-based approach to video object segmentation (VOS). To address compounding error and scalability issues of prior work, we propose a scalable, end-to-end method for VOS called Sparse Spatiotemporal Transformers (SST). SST extracts per-pixel representations for each object in a video using sparse attention over spatiotemporal features. Our attention-based formulation for VOS allows a model to learn to attend over a history of multiple frames and provides suitable inductive bias for performing correspondence-like computations necessary for solving motion segmentation. We demonstrate the effectiveness of attention-based over recurrent networks in the spatiotemporal domain. Our method achieves competitive results on YouTube-VOS and DAVIS 2017 with improved scalability and robustness to occlusions compared with the state of the art.

关键词： computer vision Codes Runtime Scalability Computational modeling Object segmentation Transformers

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Discrete-continuous Action Space Policy Gradient-based Attention for Image-Text Matching

Discrete-continuous Action Space Policy Gradient-based Atten...

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

作者： Yan, Shiyang Yu, Li Xie, Yuan Nanjing Univ Informat Sci & Technol Nanjing Peoples R China East China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

Image-text matching is an important multi-modal task with massive applications. It tries to match the image and the text with similar semantic information. Existing approaches do not explicitly transform the different modalities into a common space. Meanwhile, the attention mechanism which is widely used in image-text matching models does not have supervision. We propose a novel attention scheme which projects the image and text embedding into a common space and optimises the attention weights directly towards the evaluation metrics. The proposed attention scheme can be considered as a kind of supervised attention and requiring no additional annotations. It is trained via a novel Discrete-continuous action space policy gradient algorithm, which is more effective in modelling complex action space than previous continuous action space policy gradient. We evaluate the proposed methods on two widely-used benchmark datasets: Flickr3Ok and MS-COCO, out-performing the previous approaches by a large margin.

关键词： Measurement computer vision Annotations Computational modeling Semantics Transforms Benchmark testing

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Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion

Semantic Segmentation for Real Point Cloud Scenes via Bilate...

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

作者： Qiu, Shi Anwar, Saeed Barnes, Nick Australian Natl Univ Canberra ACT Australia CSIRO Data61 Sydney NSW Australia

ISBN: (纸本)9781665445092

Given the prominence of current 3D sensors, a fine-grained analysis on the basic point cloud data is worthy of further investigation. Particularly, real point cloud scenes can intuitively capture complex surroundings in the real world, but due to 3D data's raw nature, it is very challenging for machine perception. In this work, we concentrate on the essential visual task, semantic segmentation, for large-scale point cloud data collected in reality. On the one hand, to reduce the ambiguity in nearby points, we augment their local context by fully utilizing both geometric and semantic features in a bilateral structure. On the other hand, we comprehensively interpret the distinctness of the points from multiple resolutions and represent the feature map following an adaptive fusion method at point-level for accurate semantic segmentation. Further, we provide specific ablation studies and intuitive visualizations to validate our key modules. By comparing with state-of-the-art networks on three different benchmarks, we demonstrate the effectiveness of our network.

关键词： Visualization computer vision Three-dimensional displays Semantics Object detection Benchmark testing Real-time systems

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AlphaMatch: Improving Consistency for Semi-supervised Learning with Alpha-divergence

AlphaMatch: Improving Consistency for Semi-supervised Learni...

引用

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

作者： Gong, Chengyue Wang, Dilin Liu, Qiang Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665445092

Semi-supervised learning (SSL) is a key approach toward more data-efficient machine learning by jointly leverage both labeled and unlabeled data. We propose AlphaMatch, an efficient SSL method that leverages data augmentations, by efficiently enforcing the label consistency between the data points and the augmented data derived from them. Our key technical contribution lies on: 1) using alpha-divergence to prioritize the regularization on data with high confidence, achieving similar effect as FixMatch [32] but in a more flexible fashion, and 2) proposing an optimization-based, EM-like algorithm to enforce the consistency, which enjoys better convergence than iterative regularization procedures used in recent SSL methods such as FixMatch, UDA, and MixMatch. AlphaMatch is simple and easy to implement, and consistently outperforms prior arts on standard benchmarks, e.g. CIFAR-10, SVHN, CIFAR-100, STL-10. Specifically, we achieve 91.3% test accuracy on CIFAR-10 with just 4 labelled data per class, substantially improving over the previously best 88.7% accuracy achieved by FixMatch.

关键词： Object detection Machine learning Semisupervised learning Benchmark testing Iterative algorithms pattern recognition Machine translation

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Double low-rank representation with projection distance penalty for clustering

Double low-rank representation with projection distance pena...

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

作者： Fu, Zhiqiang Zhao, Yao Chang, Dongxia Zhang, Xingxing Wang, Yiming Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

This paper presents a novel, simple yet robust self-representation method, i.e., Double Low-Rank Representation with Projection Distance penalty (DLRRPD) for clustering. With the learned optimal projected representations, DLRRPD is capable of obtaining an effective similarity graph to capture the multi-subspace structure. Besides the global low-rank constraint, the local geometrical structure is additionally exploited via a projection distance penalty in our DLRRPD, thus facilitating a more favorable graph. Moreover, to improve the robustness of DLRRPD to noises, we introduce a Laplacian rank constraint, which can further encourage the learned graph to be more discriminative for clustering tasks. Meanwhile, Frobenius norm (instead of the popularly used nuclear norm) is employed to enforce the graph to be more block-diagonal with lower complexity. Extensive experiments have been conducted on synthetic, real, and noisy data to show that the proposed method outperforms currently available alternatives by a margin of 1.0%similar to 10.1%.

关键词： computer vision Laplace equations Robustness pattern recognition Complexity theory Noise measurement Task analysis

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Progressive Temporal Feature Alignment Network for Video Inpainting

Progressive Temporal Feature Alignment Network for Video Inp...

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

作者： Zou, Xueyan Yang, Linjie Liu, Ding Lee, Yong Jae ByteDance Inc Beijing Peoples R China Univ Calif Davis Davis CA 95616 USA

ISBN: (纸本)9781665445092

Video inpainting aims to fill spatio-temporal "corrupted" regions with plausible content. To achieve this goal, it is necessary to find correspondences from neighbouring frames to faithfully hallucinate the unknown content. Current methods achieve this goal through attention, flow-based warping, or 3D temporal convolution. However, flow-based warping can create artifacts when optical flow is not accurate, while temporal convolution may suffer from spatial misalignment. We propose 'Progressive Temporal Feature Alignment Network', which progressively enriches features extracted from the current frame with the feature warped from neighbouring frames using optical flow. Our approach corrects the spatial misalignment in the temporal feature propagation stage, greatly improving visual quality and temporal consistency of the inpainted videos. Using the proposed architecture, we achieve state-of-the-art performance on the DAVIS and FVI datasets compared to existing deep learning approaches. Code is available at https://***/MaureenZOU/TSAM.

关键词： Deep learning Visualization computer vision Three-dimensional displays Image resolution Convolution Feature extraction

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Synthesize-It-Classifier: Learning a Generative Classifier through Recurrent Self-analysis

Synthesize-It-Classifier: Learning a Generative Classifier t...

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

作者： Pal, Arghya Phan, Raphael C-W Wong, KokSheik Monash Univ Sch Informat Technol Malaysia Campus Subang Jaya Selangor Malaysia

ISBN: (纸本)9781665445092

We show the generative capability of an image classifier network by synthesizing high-resolution, photo-realistic, and diverse images at scale. The overall methodology, called Synthesize-It-Classifier (STIC), does not require an explicit generator network to estimate the density of the data distribution and sample images from that, but instead uses the classifier's knowledge of the boundary to perform gradient ascent wrt. class logits and then synthesizes images using the Gram Matrix Metropolis Adjusted Langevin Algorithm (GRMALA) by drawing on a blank canvas. During training, the classifier iteratively uses these synthesized images as fake samples and re-estimates the class boundary in a recurrent fashion to improve both the classification accuracy and quality of synthetic images. The STIC shows that mixing of the hard fake samples (i.e. those synthesized by the one-hot class conditioning), and the soft fake samples (which are synthesized as a convex combination of classes, i.e. a mixup of classes 36]) improves class interpolation. We demonstrate an Attentive-STIC network that shows iterative drawing of synthesized images on the ImageNet dataset that has thousands of classes. In addition, we introduce the synthesis using a class conditional score classifier (Score-STIC) instead of a normal image classifier and show improved results on several real world datasets, i.e. ImageNet, LSUN and CIFAR 10.

关键词： Training Knowledge engineering Interpolation computer vision Computational modeling Generators pattern recognition

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POSEFusion: Pose-guided Selective Fusion for Single-view Human Volumetric Capture

POSEFusion: Pose-guided Selective Fusion for Single-view Hum...

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

作者： Li, Zhe Yu, Tao Zheng, Zerong Guo, Kaiwen Liu, Yebin Tsinghua Univ Dept Automat Beijing Peoples R China Google Zurich Switzerland

ISBN: (纸本)9781665445092

We propose POse-guided SElective Fusion (POSEFusion), a single-view human volumetric capture method that leverages tracking-based methods and tracking-free inference to achieve high-fidelity and dynamic 3D reconstruction. By contributing a novel reconstruction framework which contains pose-guided keyframe selection and robust implicit surface fusion, our method fully utilizes the advantages of both tracking-based methods and tracking-free inference methods, and finally enables the high-fidelity reconstruction of dynamic surface details even in the invisible regions. We formulate the keyframe selection as a dynamic programming problem to guarantee the temporal continuity of the reconstructed sequence. Moreover, the novel robust implicit surface fusion involves an adaptive blending weight to preserve high-fidelity surface details and an automatic collision handling method to deal with the potential self-collisions. Overall, our method enables high-fidelity and dynamic capture in both visible and invisible regions from a single RGBD camera, and the results and experiments show that our method outperforms state-of-the-art methods.

关键词： Surface reconstruction computer vision Three-dimensional displays Cameras pattern recognition Dynamic programming

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Recorrupted-to-Recorrupted: Unsupervised Deep Learning for Image Denoising

Recorrupted-to-Recorrupted: Unsupervised Deep Learning for I...

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

作者： Pang, Tongyao Zheng, Huan Quan, Yuhui Ji, Hui Natl Univ Singapore Dept Math Singapore 119076 Singapore South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

ISBN: (纸本)9781665445092

Deep denoiser, the deep network for denoising, has been the focus of the recent development on image denoising. In the last few years, there is an increasing interest in developing unsupervised deep denoisers which only call unorganized noisy images without ground truth for training. Nevertheless, the performance of these unsupervised deep denoisers is not competitive to their supervised counterparts. Aiming at developing a more powerful unsupervised deep denoiser, this paper proposed a data augmentation technique, called recorrupted-to-recorrupted (R2R), to address the overfitting caused by the absence of truth images. For each noisy image, we showed that the cost function defined on the noisy/noisy image pairs constructed by the R2R method is statistically equivalent to its supervised counterpart defined on the noisy/truth image pairs. Extensive experiments showed that the proposed R2R method noticeably outperformed existing unsupervised deep denoisers, and is competitive to representative supervised deep denoisers.

关键词： Training Deep learning computer vision AWGN Noise reduction Cost function pattern recognition

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