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Learning Multi-Scale Photo Exposure Correction

Learning Multi-Scale Photo Exposure Correction

引用

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

作者： Afifi, Mahmoud Derpanis, Konstantinos G. Ommer, Bjoern Brown, Michael S. Samsung AI Ctr SAIC Toronto ON Canada York Univ N York ON Canada Heidelberg Univ Heidelberg Germany

ISBN: (纸本)9781665445092

Capturing photographs with wrong exposures remains a major source of errors in camera-based imaging. Exposure problems are categorized as either: (i) overexposed, where the camera exposure was too long, resulting in bright and washed-out image regions, or (ii) underexposed, where the exposure was too short, resulting in dark regions. Both under- and overexposure greatly reduce the contrast and visual appeal of an image. Prior work mainly focuses on underexposed images or general image enhancement. In contrast, our proposed method targets both over- and underexposure errors in photographs. We formulate the exposure correction problem as two main sub-problems: (i) color enhancement and (ii) detail enhancement. Accordingly, we propose a coarse-to-fine deep neural network (DNN) model, trainable in an end-to-end manner, that addresses each subproblem separately. A key aspect of our solution is a new dataset of over 24,000 images exhibiting the broadest range of exposure values to date with a corresponding properly exposed image. Our method achieves results on par with existing state-of-the-art methods on underexposed images and yields significant improvements for images suffering from overexposure errors.

关键词： Deep learning Visualization computer vision Image color analysis Cameras pattern recognition Image enhancement

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SurFree: a fast surrogate-free black-box attack

SurFree: a fast surrogate-free black-box attack

引用

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

作者： Maho, Thibault Furon, Teddy Le Merrer, Erwan Univ Rennes CNRS INRIA IRISA Rennes France

ISBN: (纸本)9781665445092

Machine learning classifiers are critically prone to evasion attacks. Adversarial examples are slightly modified inputs that are then misclassified, while remaining perceptively close to their originals. Last couple of years have witnessed a striking decrease in the amount of queries a black box attack submits to the target classifier, in order to forge adversarials. This particularly concerns the black box score-based setup, where the attacker has access to top predicted probabilites: the amount of queries went from to millions of to less than a thousand. This paper presents SurFree, a geometrical approach that achieves a drastic reduction in the amount of queries in the hardest setup: black box decision-based attacks (only the top-1 label is available). We first highlight that the most recent attacks in that setup, HSJA [3], QEBA [14] and GeoDA [23] all perform costly gradient surrogate estimations. SurFree proposes to bypass these, by instead focusing on careful trials along diverse directions, guided by precise indications of geometrical properties of the classifier decision boundaries. We motivate this geometric approach before performing a head-to-head comparison with previous attacks with the amount of queries as a first class citizen. We exhibit a faster distortion decay under low query amounts (few hundreds to a thousand), while remaining competitive at higher query budgets.(1)

关键词： computer vision Estimation Focusing Machine learning Distortion pattern recognition Convergence

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Progressively Complementary Network for Fisheye Image Rectification Using Appearance Flow

Progressively Complementary Network for Fisheye Image Rectif...

引用

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

作者： Yang, Shangrong Lin, Chunyu Liao, Kang Zhang, Chunjie Zhao, Yao Beijing Jiaotong Univ Beijing Key Lab Adv Informat Sci & Network Inst Informat Sci Beijing 100044 Peoples R China

ISBN: (纸本)9781665445092

Distortion rectification is often required for fisheye images. The generation-based method is one mainstream solution due to its label-free property, but its naive skip-connection and overburdened decoder will cause blur and incomplete correction. First, the skip-connection directly transfers the image features, which may introduce distortion and cause incomplete correction. Second, the decoder is overburdened during simultaneously reconstructing the content and structure of the image, resulting in vague performance. To solve these two problems, in this paper, we focus on the interpretable correction mechanism of the distortion rectification network and propose a feature-level correction scheme. We embed a correction layer in skip-connection and leverage the appearance flows in different layers to pre-correct the image features. Consequently, the decoder can easily reconstruct a plausible result with the remaining distortion-less information. In addition, we propose a parallel complementary structure. It effectively reduces the burden of the decoder by separating content reconstruction and structure correction. Subjective and objective experiment results on different datasets demonstrate the superiority of our method.

关键词： computer vision Distortion Decoding pattern recognition Image reconstruction

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Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion

Modular Interactive Video Object Segmentation: Interaction-t...

引用

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

作者： Cheng, Ho Kei Tai, Yu-Wing Tang, Chi-Keung UIUC Champaign IL 61820 USA HKUST Hong Kong Peoples R China Kuaishou Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

We present Modular interactive VOS (MiVOS) framework which decouples interaction-to-mask and mask propagation, allowing for higher generalizability and better performance. Trained separately, the interaction module converts user interactions to an object mask, which is then temporally propagated by our propagation module using a novel top-k filtering strategy in reading the space-time memory. To effectively take the user's intent into account, a novel difference-aware module is proposed to learn how to properly fuse the masks before and after each interaction, which are aligned with the target frames by employing the space-time memory. We evaluate our method both qualitatively and quantitatively with different forms of user interactions (e.g., scribbles, clicks) on DAVIS to show that our method outperforms current state-of-the-art algorithms while requiring fewer frame interactions, with the additional advantage in generalizing to different types of user interactions. We contribute a large-scale synthetic VOS dataset with pixel-accurate segmentation of 4.8M frames to accompany our source codes to facilitate future research.

关键词： computer vision Codes Fuses Filtering Object segmentation pattern recognition Sparks

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DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

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

作者： Rozumnyi, Denys Oswald, Martin R. Ferrari, Vittorio Matas, Jiri Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Google Res Mountain View CA 94043 USA Microsoft Mixed Real & AI Zurich Lab Zurich Switzerland Czech Tech Univ Visual Recognit Grp Prague Czech Republic

ISBN: (纸本)9781665445092

Objects moving at high speed appear significantly blurred when captured with cameras. The blurry appearance is especially ambiguous when the object has complex shape or texture. In such cases, classical methods, or even humans, are unable to recover the object's appearance and motion. We propose a method that, given a single image with its estimated background, outputs the object's appearance and position in a series of sub frames as if captured by a high-speed camera (i.e. temporal super-resolution). The proposed generative model embeds an image of the blurred object into a latent space representation, disentangles the background, and renders the sharp appearance. Inspired by the image formation model, we design novel self-supervised loss function terms that boost performance and show good generalization capabilities. The proposed DeFMO method is trained on a complex synthetic dataset, yet it performs well on real-world data from several datasets. DeFMO outperforms the state of the art and generates high-quality temporal super-resolution frames.

关键词： Training computer vision Shape Computational modeling Superresolution Cameras pattern recognition

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computer vision-Based Detection and recognition Model of Objects for any Autonomous Systems in Low Illumination Condition 2

Computer Vision-Based Detection and Recognition Model of Obj...

引用

2nd International conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Ashok, P. Kavinilavu, A. Ananthi, S. Suganthi, P. Iniyan, E.U. Sri Sai Ram Institute of Technology Department of Computer Science and Engineering Chennai India 5Prathyusha Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665437899

The theories and methods it studies have received extensive attention in many disciplines and fields, promoting the development of artificial intelligence systems and expanding the possibilities of computer applications. Image processing is an important field of pattern recognition methods. The widely used character recognition (OCR) is a typicalapplication of pattern recognition in image processing. The pattern recognition process can be seen as a mapping process from the sample space to the category space. If you divide an n-dimensional feature space with n features asparameters into different regions, each region corresponds to a class of pattern classes. Among them, feature selection and extraction is an important part of pattern recognition. Ifthe selected features can fully reflect the essential features of the class, the classifier is easier to design. According to the presence or absence of standard samples, pattern recognition can be divided into surveillance identification methods and non-supervisory identification methods. The supervisory identification method is to design a classifierunder the condition that the class to which the training sample belongs is known, and the method for recognizing the sample is performed by the classifier. © 2022 ieee.

关键词： Character recognition

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Automated Log-Scale Quantization for Low-Cost Deep Neural Networks

Automated Log-Scale Quantization for Low-Cost Deep Neural Ne...

引用

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

作者： Oh, Sangyun Sim, Hyeonuk Lee, Sugil Lee, Jongeun UNIST Dept Elect Engn Ulsan South Korea UNIST Dept Comp Sci & Engn Ulsan South Korea

ISBN: (纸本)9781665445092

Quantization plays an important role in deep neural network (DNN) hardware. In particular, logarithmic quantization has multiple advantages for DNN hardware implementations, and its weakness in terms of lower performance at high precision compared with linear quantization has been recently remedied by what we call selective two-word logarithmic quantization (STLQ). However, there is a lack of training methods designed for STLQ or even logarithmic quantization in general. In this paper we propose a novel STLQ-aware training method, which significantly outperforms the previous state-of-the-art training method for STLQ. Moreover, our training results demonstrate that with our new training method, STLQ applied to weight parameters of ResNet-18 can achieve the same level of performance as state-of-the-art quantization method, APoT, at 3-bit precision. We also apply our method to various DNNs in image enhancement and semantic segmentation, showing competitive results.

关键词： Training Deep learning Image segmentation computer vision Quantization (signal) Semantics computer architecture

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Spatially-Varying Outdoor Lighting Estimation from Intrinsics

Spatially-Varying Outdoor Lighting Estimation from Intrinsic...

引用

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

作者： Zhu, Yongjie Zhang, Yinda Li, Si Shi, Boxin Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Google Mountain View CA 94043 USA Peking Univ Dept Comp Sci & Technol NELVT Beijing Peoples R China Peking Univ Inst Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781665445092

We present SOLID-Net, a neural network for spatially-varying outdoor lighting estimation from a single outdoor image for any 2D pixel location. Previous work has used a unified sky environment map to represent outdoor lighting. Instead, we generate spatially-varying local lighting environment maps by combining global sky environment map with warped image information according to geometric information estimated from intrinsics. As no outdoor dataset with image and local lighting ground truth is readily available, we introduce the SOLID-Img dataset with physically-based rendered images and their corresponding intrinsic and lighting information. We train a deep neural network to regress intrinsic cues with physically-based constraints and use them to conduct global and local lightings estimation. Experiments on both synthetic and real datasets show that SOLID-Net significantly outperforms previous methods.

关键词： Deep learning computer vision Neural networks Lighting Estimation pattern recognition

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Cascaded Prediction Network via Segment Tree for Temporal Video Grounding

Cascaded Prediction Network via Segment Tree for Temporal Vi...

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

作者： Zhao, Yang Zhao, Zhou Zhang, Zhu Lin, Zhijie Zhejiang Univ Coll Comp Sci Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

Temporal video grounding aims to localize the target segment which is semantically aligned with the given sentence in an untrimmed video. Existing methods can be divided into two main categories, including proposal-based approaches and proposal-free approaches. However, the former ones suffer from the extra cost of generating proposals and inflexibility in determining fine-grained boundaries, and the latter ones usually attempt to decide the start and end timestamps directly, which brings about much difficulty and inaccuracy. In this paper, we convert this task into a multi-step decision problem and propose a novel Cascaded Prediction Network (CPN) to generate the grounding result in a coarse-to-fine manner. Concretely, we first encode video and query into the same latent space and fuse them into integrated representations. Afterwards, we construct a segment-tree-based structure and make predictions via decision navigation and signal decomposition in a cascaded way. We evaluate our proposed method on three large-scale publicly available benchmarks, namely ActivityNet Caption, Charades-STA and TACoS, where our CPN surpasses the performance of the state-of-the-art methods.

关键词： computer vision Costs Grounding Navigation Fuses Benchmark testing pattern recognition

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CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning

CReST: A Class-Rebalancing Self-Training Framework for Imbal...

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

作者： Wei, Chen Sohn, Kihyuk Mellina, Clayton Yuille, Alan Yang, Fan Johns Hopkins Univ Baltimore MD 21218 USA Google Cloud AI Mountain View CA USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Semi-supervised learning on class-imbalanced data, although a realistic problem, has been under studied. While existing semi-supervised learning (SSL) methods are known to perform poorly on minority classes, we find that they still generate high precision pseudo-labels on minority classes. By exploiting this property, in this work, we propose Class-Rebalancing Self-Training (CReST), a simple yet effective framework to improve existing SSL methods on class-imbalanced data. CReST iteratively retrains a baseline SSL model with a labeled set expanded by adding pseudo labeled samples from an unlabeled set, where pseudo labeled samples from minority classes are selected more frequently according to an estimated class distribution. We also propose a progressive distribution alignment to adaptively adjust the rebalancing strength dubbed CReST+. We show that CReST and CReST+ improve state-of-the-art SSL algorithms on various class-imbalanced datasets and consistently outperform other popular rebalancing methods. Code has been made available at ht-sps//***/google-research/crest.

关键词： computer vision Adaptation models Codes Semisupervised learning pattern recognition

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