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

共 30988 条记录，以下是4561-4570 订阅

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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...

引用

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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DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

引用

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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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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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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Activity-Biometrics: Person Identification from Daily Activities

Activity-Biometrics: Person Identification from Daily Activi...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Shehreen Azad Yogesh Singh Rawat Center for Research in Computer Vision University of Central Florida

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In this work, we study a novel problem which focuses on person identification while performing daily activities. Learning biometric features from RGB videos is challenging due to spatio-temporal complexity and presence of ap-pearance biases such as clothing color and background. We propose ABNet, a novel framework which leverages dis-entanglement of biometric and non-biometric features to perform effective person identification from daily activities. ABNet relies on a bias-less teacher to learn biometric features from RGB videos and explicitly disentangle non-biometric features with the help of biometric distortion. In addition, ABNet also exploits activity prior for biometrics which is enabled by joint biometric and activity learning. We perform comprehensive evaluation of the proposed approach across five different datasets which are derived from existing activity recognition benchmarks. Furthermore, we extensively compare ABNet with existing works in person identification and demonstrate its effectiveness for activity-based biometrics across all five datasets. The code and dataset can be accessed at: https: //***/ sacrcv/Activity-Biometrics/

关键词： Biometrics computer vision Accuracy Biological system modeling Benchmark testing Activity recognition Distortion

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Multi-Session SLAM with Differentiable Wide-Baseline Pose Optimization

Multi-Session SLAM with Differentiable Wide-Baseline Pose Op...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Lahav Lipson Jia Deng Princeton University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce a new system for Multi-Session SLAM, which tracks camera motion across multiple disjoint videos under a single global reference. Our approach couples the prediction of optical flow with solver layers to estimate camera pose. The backbone is trained end-to-end using a novel differentiable solver for wide-baseline two-view pose. The full system can connect disjoint sequences, perform visualodometry, and global optimization. Compared to existing approaches, our design is accurate and robust to catas-trophic failures. Code is available at h t tps://github. com/princeton-v1/MultiSlam_DiffPose

关键词： Simultaneous localization and mapping Codes Accuracy Tracking Cameras pattern recognition Optimization

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A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification

A Realistic Evaluation of Semi-Supervised Learning for Fine-...

引用

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

作者： Su, Jong-Chyi Cheng, Zezhou Maji, Subhransu Univ Massachusetts Amherst Amherst MA 01003 USA

ISBN: (纸本)9781665445092

We evaluate the effectiveness of semi-supervised learning (SSL) on a realistic benchmark where data exhibits considerable class imbalance and contains images from novel classes. Our benchmark consists of two fine-grained classification datasets obtained by sampling classes from the Aves and Fungi taxonomy. We find that recently proposed SSL methods provide significant benefits, and can effectively use out-of-class data to improve performance when deep networks are trained from scratch. Yet their performance pales in comparison to a transfer learning baseline, an alternative approach for learning from a few examples. Furthermore, in the transfer setting, while existing SSL methods provide improvements, the presence of out-of-class is often detrimental. In this setting, standard fine-tuning followed by distillation-based self-training is the most robust. Our work suggests that semi-supervised learning with experts on realistic datasets may require different strategies than those currently prevalent in the literature.

关键词： Fungi Technological innovation Transfer learning Taxonomy Benchmark testing Semisupervised learning pattern recognition

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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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On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective

On Focal Loss for Class-Posterior Probability Estimation: A ...

引用

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

作者： Charoenphakdee, Nontawat Vongkulbhisal, Jayakorn Chairatanakul, Nuttapong Sugiyama, Masashi Univ Tokyo Tokyo Japan RIKEN AIP Tokyo Japan IBM Res Yorktown Hts NY USA Tokyo Inst Technol Tokyo Japan AIST RWBC OIL Tokyo Japan

ISBN: (纸本)9781665445092

The focal loss has demonstrated its effectiveness in many real-world applications such as object detection and image classification, but its theoretical understanding has been limited so far. In this paper, we first prove that the focal loss is classification-calibrated, i.e., its minimizer surely yields the Bayes-optimal classifier and thus the use of the focal loss in classification can be theoretically justified. However, we also prove a negative fact that the focal loss is not strictly proper, i.e., the confidence score of the classifier obtained by focal loss minimization does not match the true class-posterior probability. This may cause the trained classifier to give an unreliable confidence score, which can be harmful in critical applications. To mitigate this problem, we prove that there exists a particular closed-form transformation that can recover the true class-posterior probability from the outputs of the focal risk minimizer. Our experiments show that our proposed transformation successfully improves the quality of class-posterior probability estimation and improves the calibration of the trained classifier, while preserving the same prediction accuracy.

关键词： computer vision Estimation Object detection Minimization pattern recognition Calibration Image classification

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Pose-Guided Human Animation from a Single Image in the Wild

Pose-Guided Human Animation from a Single Image in the Wild

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

作者： Yoon, Jae Shin Liu, Lingjie Golyanik, Vladislav Sarkar, Kripasindhu Park, Hyun Soo Theobalt, Christian Univ Minnesota Minneapolis MN 55455 USA Max Planck Inst Informat SIC Saarbrucken Germany

ISBN: (纸本)9781665445092

We present a new pose transfer method for synthesizing a human animation from a single image of a person controlled by a sequence of body poses. Existing pose transfer methods exhibit significant visual artifacts when applying to a novel scene, resulting in temporal inconsistency and failures in preserving the identity and textures of the person. To address these limitations, we design a compositional neural network that predicts the silhouette, garment labels, and textures. Each modular network is explicitly dedicated to a subtask that can be learned from the synthetic data. At the inference time, we utilize the trained network to produce a unified representation of appearance and its labels in UV coordinates, which remains constant across poses. The unified representation provides an incomplete yet strong guidance to generating the appearance in response to the pose change. We use the trained network to complete the appearance and render it with the background. With these strategies, we are able to synthesize human animations that can preserve the identity and appearance of the person in a temporally coherent way without any fine-tuning of the network on the testing scene. Experiments show that our method outperforms the state-of-the-arts in terms of synthesis quality, temporal coherence, and generalization ability.

关键词： Visualization computer vision Clothing Neural networks Coherence Animation pattern recognition

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