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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6678 条记录，以下是411-420 订阅

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A robust non-blind deblurring method using deep denoiser prior

A robust non-blind deblurring method using deep denoiser pri...

引用

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

作者： Fang, Yingying Zhang, Hao Wong, Hok Shing Zeng, Tieyong Imperial Coll London London England Chinese Univ Hong Kong Shatin Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The existing non-blind deblurring methods are mostly susceptible to noise in the given blurring kernel, which is usually estimated from the observed image. This will produce undesirable ringing artifacts around the recovered edges when the given kernel is not accurate enough. Besides, the noise and outliers in the observed images may also severely degrade the performance of the deblurring methods. Considering these factors, we designed a robust non-blind deblurring method taking all these noises into account. In this paper, we propose a kernel error term to rectify the given kernel in the midst of the deconvolution process. A residual error term is also introduced to deal with the outliers caused by noise or saturation. A deep learning denoiser prior is adopted to reserve the fine textures in the recovered image. The experiments show clearly that the proposed method achieves remarkable progress in both the visual quality and the numerical results of the recovered images compared to the state-of-the-art deblurring methods.

关键词： Deep learning Measurement Visualization Deconvolution Image edge detection conferences pattern recognition

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Semi-Supervised Few-Shot Learning from A Dependency-Discriminant Perspective

Semi-Supervised Few-Shot Learning from A Dependency-Discrimi...

引用

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

作者： Hou, Zejiang Kung, Sun-Yuan Princeton Univ Princeton NJ 08544 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We study the few-shot learning (FSL) problem, where a model learns to recognize new objects with extremely few labeled training data per category. Most of previous FSL approaches resort to the meta-learning paradigm, where the model accumulates inductive bias through learning from many training tasks, in order to solve new unseen few-shot tasks. In contrast, we propose a simple semi-supervised FSL approach to exploit unlabeled data accompanying the few-shot task to improve FSL performance. More exactly, to train a classifier, we propose a Dependency Maximization loss based on the Hilbert-Schmidt norm of the cross-covariance operator, which maximizes the statistical dependency between the embedded feature of the unlabeled data and their label predictions, together with the supervised loss over the support set. The obtained classifier is used to infer the pseudo-labels of the unlabeled data. Furthermore, we propose an Instance Discriminant Analysis to evaluate the credibility of the pseudo-labeled examples and select the faithful ones into an augmented support set, which is used to retrain the classifier. We iterate the process until the pseudo-labels of the unlabeled data becomes stable. Through extensive experiments on four widely used few-shot classification benchmarks, including mini-ImageNet, tiered-ImageNet, CUB, and CIFARFS, the proposed method outperforms previous state-of-the-art FSL methods.

关键词： Training computer vision conferences Training data Benchmark testing Data models pattern recognition

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Cross-dataset Learning for Generalizable Land Use Scene Classification

Cross-dataset Learning for Generalizable Land Use Scene Clas...

引用

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

作者： Gominski, Dimitri Gouet-Brunet, Valerie Chen, Liming Univ Copenhagen Geog Copenhagen Denmark IGN LaSTIG St Mande France Ecole Cent Lyon LIRIS Ecully France

ISBN: (纸本)9781665487399

Few-shot and cross-domain land use scene classification methods propose solutions to classify unseen classes or unseen visual distributions, but are hardly applicable to realworld situations due to restrictive assumptions. Few-shot methods involve episodic training on restrictive training subsets with small feature extractors, while cross-domain methods are only applied to common classes. The underlying challenge remains open: can we accurately classify new scenes on new datasets? In this paper, we propose a new framework for few-shot, cross-domain classification. Our retrieval-inspired approach(1) exploits the interrelations in both the training and testing data to output class labels using compact descriptors. Results show that our method can accurately produce land-use predictions on unseen datasets and unseen classes, going beyond the traditional few-shot or cross-domain formulation, and allowing cross-dataset training.

关键词： Training Visualization computer vision Image analysis conferences Image retrieval Feature extraction

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Can the Mathematical Correctness of Object Configurations Affect the Accuracy of Their Perception?

Can the Mathematical Correctness of Object Configurations Af...

引用

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

作者： Jiang, Han Li, Zeqian Whitehill, Jacob Worcester Polytech Inst Worcester MA 01609 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We investigate a new type of dataset bias based on the mathematical correctness of object configurations in visual scenes, and how this bias can affect the accuracy of computer vision models. Our experiments demonstrate how CNNs trained to detect and recognize individual objects are capable of implicitly learning simple mathematical relationships between them directly from pixel data;moreover, models that are trained with a dataset bias (e.g., all examples are mathematically correct) can suffer in performance when evaluated on test data without this bias. We found evidence for this effect in two settings: (1) object detection of math symbols in images of arithmetic expressions, and (2) object detection of moving particles from images produced by a physics simulator. Importantly, the semantic bias that we study is based not just on simple co-occurrence patterns in each image, but rather on higher-order semantic rules that generalize to unique combinations of objects not seen during training. While the magnitude of the effect was small, the accuracy difference was statistically reliable.

关键词： Training computer vision Computational modeling Semantics Symbols Object detection Mathematical models

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A Categorized Reflection Removal Dataset with Diverse Real-world Scenes

A Categorized Reflection Removal Dataset with Diverse Real-w...

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

作者： Lei, Chenyang Huang, Xuhua Qi, Chenyang Zhao, Yankun Sun, Wenxiu Yan, Qiong Chen, Qifeng HKUST Hong Kong Peoples R China CMU Pittsburgh PA USA SenseTime Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Due to the lack of a large-scale reflection removal dataset with diverse real-world scenes, many existing reflection removal methods are trained on synthetic data plus a small amount of real-world data, which makes it difficult to evaluate the strengths or weaknesses of different reflection removal methods thoroughly. Furthermore, existing real-world benchmarks and datasets do not categorize image data based on the types and appearances of reflection (e.g., smoothness, intensity), making it hard to analyze reflection removal methods. Hence, we construct a new reflection removal dataset that is categorized, diverse, and real-world (CDR). A pipeline based on RAW data is used to capture perfectly aligned input images and transmission images. The dataset is constructed using diverse glass types under various environments to ensure diversity. By analyzing several reflection removal methods and conducting extensive experiments on our dataset, we show that state-of-the-art reflection removal methods generally perform well on blurry reflection but fail in obtaining satisfying performance on other types of real-world reflection. We believe our dataset can help develop novel methods to remove real-world reflection better.

关键词： computer vision conferences Pipelines Glass Benchmark testing Reflection pattern recognition

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Multi-Camera Vehicle Tracking Based on Occlusion-aware and Inter-vehicle Information

Multi-Camera Vehicle Tracking Based on Occlusion-aware and I...

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

作者： Liu, Yuming Zhang, Xiaochun Zhang, Bingzhen Zhang, Xiaoyong Wang, Sen Xu, Jianrong Shenzhen Urban Transport Planning Ctr Co Ltd Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With the demands of analyzing and predicting traffic flow for applications in smart cities, Multi-Target Multi-Camera vehicle Tracking(MTMCT) at the city scale has become a fundamental problem. The MTMCT is challenging due to the view variations, frequent occlusions, and similar vehicle models in the same camera. This work proposes an MTMCT framework based on occlusion-aware and intervehicle information that can effectively match vehicle tracklets. The occlusion-aware module segments the tracklets of an occluded and occluding vehicle pair. It recalculates the similarity of the complete tracklets, which can handle the occlusions and suppress false detections. This work proposes an inter-vehicle information module to improve the matching accuracy. The module can enhance the ability to distinguish similar vehicles under the same camera at different times. The proposed whole framework consists of four modules: (1) vehicle detection and feature extraction by re-identification models, (2) single-camera tracking (SCT) to produce initial tracklets with an occlusion-aware module, (3) tracklets similarity by inter-vehicle association, (4) clustering in adjacent cameras for multi-camera tracklets matching. The proposed method obtains IDF1 score of 0.8285 on the Track-1 multi-camera vehicle tracking task of the 2022 AI City Challenge.

关键词： computer vision Smart cities Vehicle detection conferences Cameras Feature extraction pattern recognition

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SaR: Self-adaptive Refinement on Pseudo Labels for Multiclass-Imbalanced Semi-supervised Learning

SaR: Self-adaptive Refinement on Pseudo Labels for Multiclas...

引用

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

作者： Lai, Zhengfeng Wang, Chao Cheung, Sen-ching Chuah, Chen-Nee Univ Calif Davis Davis CA 95616 USA Southern Univ Sci & Technol Shenzhen Peoples R China Univ Kentucky Lexington KY 40506 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Class-imbalanced datasets can severely deteriorate the performance of semi-supervised learning (SSL). This is due to the confirmation bias especially when the pseudo labels are highly biased towards the majority classes. Traditional resampling or reweighting techniques may not be directly applicable when the unlabeled data distribution is unknown. Inspired by the threshold-moving method that performs well in supervised learning-based binary classification tasks, we provide a simple yet effective scheme to address the multiclass imbalance issue of SSL. This scheme, named SaR, is a Self-adaptive Refinement of soft labels before generating pseudo labels. The pseudo labels generated post-SaR will be less biased, resulting in higher quality data for training the classifier. We show that SaR can consistently improve recent consistency-based SSL algorithms on various image classification problems across different imbalanced ratios. We also show that SaR is robust to the situations where unlabeled data have different distributions as labeled data. Hence, SaR does not rely on the assumptions that unlabeled data share the same distribution as the labeled data.

关键词： Training computer vision conferences Semisupervised learning pattern recognition Classification algorithms Task analysis

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Visual Goal-Directed Meta-Imitation Learning

Visual Goal-Directed Meta-Imitation Learning

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

作者： Rivera, Corban G. Handelman, David A. Ratto, Christopher R. Patrone, David Paulhamus, Bart L. Johns Hopkins Univ Intelligent Syst Ctr Appl Phys Lab Laurel MD 20723 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The goal of meta-learning is to generalize to new tasks and goals as quickly as possible. Ideally, we would like approaches that generalize to new goals and tasks on the first attempt. Requiring a policy to perform on a new task on the first attempt without even a single example trajectory is a zero-shot problem formulation. When tasks are identified by goal images, the tasks can be considered visually goal-directed. In this work, we explore the problem of visual goal-directed zero-shot meta-imitation learning. Inspired by several popular approaches to Meta-RL, we composed several core ideas related to task-embedding and planning by gradient descent to attempt to explore this problem. To evaluate these approaches, we adapted the Meta-world benchmark tasks to create 24 distinct visual goal-directed manipulation tasks. We found that 7 out of 24 tasks could be successfully completed on the first attempt by at least one of the approaches we tested. We demonstrated that goal-directed zero-shot approaches can translate to a physical robot with a demonstration based on Jenga block manipulation tasks using a Kinova Jaco robotic arm.

关键词： Visualization computer vision conferences Benchmark testing Manipulators Trajectory Planning

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Z-Domain Entropy Adaptable Flex for Semi-supervised Action recognition in the Dark

Z-Domain Entropy Adaptable Flex for Semi-supervised Action R...

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

作者： Chen, Zhi Fan, Zijun Li, Yongjie Gao, Huaien Lin, Shan Guangzhou Xi Ma Informat Technol Co 101 Waihuan Xi Rd Guangzhou 510006 Guangdong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The subtask of Human Action recognition (AR) in the dark is gaining a lot of traction nowadays, which takes a significant place in the field of computer vision. The implementation of its application includes self-driving at night, human-pose estimation, night surveillance, etc. Currently, solutions such as DLN for AR have emerged. However, due to the poor accuracy even when leveraging on large amounts of datasets and complex architectures, the development of AR in the dark has been slow to progress. In this paper, we propose a novel and straightforward method: Z-Domain Entropy Adaptable Flex. This constructs a neural network architecture R(2+1)D, including (i) a self-attention mechanism, which combines and extracts corresponding and complementary features from the dual pathways;(ii) Zero-DCE low light image enhancement, which improves enhanced quality;and (iii) FlexMatch method, which can generates the pseudo-labels flexibly. With the help of pseudo-labels from FlexMatch, our proposed Z-DEAF method facilitates the process of gaining desired classification boundaries. This works by repeating Expanding Entropy and Shrinking Entropy. It aims to solve the problem of unclear classification boundaries between the categories. Our model obtains superior performance in experiments, and achieves state-of-the-art results on ARID.

关键词： computer vision Flexible printed circuits Surveillance Neural networks Estimation computer architecture Feature extraction

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Using Pure Pollen Species When Training a CNN to Segment Pollen Mixtures

Using Pure Pollen Species When Training a CNN to Segment Pol...

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

作者： Yang, Nana Joos, Victor Jacquemart, A. L. Buyens, Christel De Vleeschouwer, C. UCLouvain ICTEAM Inst Ottignies Belgium UCLouvain ELI Inst Ottignies Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recognizing the types of pollen grains and estimating their proportion in pollen mixture samples collected in a specific geographical area is important for agricultural, medical, and ecosystem research. Our paper adopts a convolutional neural network for the automatic segmentation of pollen species in microscopy images, and proposes an original strategy to train such network at reasonable manual annotation cost. Our approach is founded on a large dataset composed of pure pollen images. It first (semi-)manually segments foreground, i.e. pollen grains, and background in a fraction of those images, and use the resulting annotated dataset to train a universal pollen segmentation CNN. In the second step, this model is used to automatically segment a large number of additional pure pollen images, so as to supervise the training of a pollen species segmentation model. Despite the fact that it has been trained from pure images only, the model is shown to provide accurate segmentation of species in pollen mixtures. Our experiments also demonstrate that dedicating a model to the segmentation of a subset of the available pure pollen species makes it possible to train a bin pollen class, corresponding to pollen species that are not in the subset of species recognized by the model. This strategy is useful to cope with unexpected species in a mixture.

关键词： Training Image segmentation computer vision Costs Computational modeling Microscopy pattern recognition

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