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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22906 条记录，以下是4801-4810 订阅

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Range image segmentation by surface extraction using an improved robust estimator

Range image segmentation by surface extraction using an impr...

引用

2003 ieee computer society conference on computer vision and pattern recognition, CVPR 2003

作者： Gotardo, Paulo F.U. Bellon, Olga R.P. Silva, Luciano Departamento de Informatica Universidade Federal do Parana Parana Brazil CPGEI Ctro. Fed. de Educ. Technol. Parana Parana Brazil

This paper presents a novel range image segmentation algorithm based on planar surface extraction. The algorithm was applied to common range image databases and was favorably compared against seven other segmentation algorithms using a popular evaluation framework. The experimental results show that, as compared to the other methods, our algorithm presents a good performance in preserving small regions and edge locations when processing noisy images. Our main contribution is an improved robust estimator, derived from the RANSAC and MSAC estimators, whose optimization process is accelerated by a genetic algorithm with a new set of parameters and operations designed to avoid premature convergence.

关键词： computer vision

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NTIRE 2021 Challenge for Defocus Deblurring Using Dual-pixel Images: Methods and Results

NTIRE 2021 Challenge for Defocus Deblurring Using Dual-pixel...

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

作者： Abuolaim, Abdullah Timofte, Radu Brown, Michael S. Zhang, Dafeng Wang, Xiaobing Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Shao, Ling Liu, Shuai Lei, Lei Feng, Chaoyu Xiong, Zhiwei Xiao, Zeyu Xu, Ruikang Zhu, Yunan Liu, Dong Vo, Tu Miao, Si Shah, Nisarg A. Liang, Pengwei Zhong, Zhiwei Hu, Xingyu Chen, Yiqun Li, Chenghua Bai, Xiaoying Zhang, Chi Yao, Yiheng Gang, Ruipeng Nathan, Sabari Ragavendran, Thangavelu Srinija, Venkatakrishnan Srivatsav, Venkatakrishnan York Univ N York ON Canada Swiss Fed Inst Technol Zurich Switzerland Samsung Res China Beijing SRC B Beijing Peoples R China Incept Inst Artificial Intelligence IIAI Abu Dhabi U Arab Emirates North China Univ Technol Beijing Peoples R China Xiaomi Beijing Peoples R China Univ Sci & Technol China Hefei Anhui Peoples R China Bridge AI Inc Seongnam South Korea Chinese Acad Sci Shanghai Adv Res Inst Shanghai Peoples R China Indian Inst Technol Jodhpur Karwar Rajasthan India Harbin Inst Technol Harbin Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Couger Inc Meerut Uttar Pradesh India Jeppair Coll Engn Chennai Tamil Nadu India Sri Sai Ram Inst Technol Chennai Tamil Nadu India Prince Shri Venkateshwara Padmavathy Engn Coll Chennai Tamil Nadu India

ISBN: (纸本)9781665448994

This paper provides a review of the NTIRE 2021 challenge targeting defocus deblurring using dual-pixel (DP) data. The goal of this single-track challenge was to reduce spatially varying defocus blur present in images captured with a shallow depth of field. The images used in this challenge were obtained using a DP sensor that provided a pair of DP views per captured image. Submitted solutions were evaluated using conventional signal processing metrics, namely peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Out of 185 registered participants, nine teams provided methods and competed in the final stage. The paper describes the methods proposed by the participating teams and their results. The winning teams represent the state-of-the-art in terms of defocus deblurring using DP images.

关键词： computer vision PSNR Atmospheric measurements conferences Signal processing Particle measurements pattern recognition

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DiffusionCLIP Text-Guided Diffusion Models for Robust Image Manipulation

DiffusionCLIP Text-Guided Diffusion Models for Robust Image ...

引用

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

作者： Kim, Gwanghyun Kwon, Taesung Ye, Jong Chul Korea Adv Inst Sci & Technol KAIST Dept Bio & Brain Engn Daejeon South Korea Korea Adv Inst Sci & Technol KAIST Kim Jaechul Grad Sch AI Daejeon South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, GAN inversion methods combined with Contrastive Language-Image Pretraining (CLIP) enables zeroshot image manipulation guided by text prompts. However, their applications to diverse real images are still difficult due to the limited GAN inversion capability. Specifically, these approaches often have difficulties in reconstructing images with novel poses, views, and highly variable contents compared to the training data, altering object identity, or producing unwanted image artifacts. To mitigate these problems and enable faithful manipulation of real images, we propose a novel method, dubbed DiffusionCLIP, that performs textdriven image manipulation using diffusion models. Based on full inversion capability and high-quality image generation power of recent diffusion models, our method performs zeroshot image manipulation successfully even between unseen domains and takes another step towards general application by manipulating images from a widely varying ImageNet dataset. Furthermore, we propose a novel noise combination method that allows straightforward multi-attribute manipulation. Extensive experiments and human evaluation confirmed robust and superior manipulation performance of our methods compared to the existing baselines.

关键词： computer vision Codes Image synthesis Machine vision Computational modeling Training data pattern recognition

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Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation 31

Structured Attention Guided Convolutional Neural Fields for ...

引用

31st ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Xu, Dan Wang, Wei Tang, Hao Liu, Hong Sebe, Nicu Ricci, Elisa Univ Trento Multimedia & Human Understanding Grp Trento Italy Peking Univ Shenzhen Grad Sch Key Lab Machine Percept Beijing Peoples R China Fdn Bruno Kessler Technol Vis Grp Trento Italy

ISBN: (纸本)9781538664209

Recent works have shown the benefit of integrating Conditional Random Fields (CRFs) models into deep architectures for improving pixel-level prediction tasks. Following this line of research, in this paper we introduce a novel approach for monocular depth estimation. Similarly to previous works, our method employs a continuous CRF to fuse multi-scale information derived from different layers of a front-end Convolutional Neural Network (CNN). Differently from past works, our approach benefits from a structured attention model which automatically regulates the amount of information transferred between corresponding features at different scales. Importantly, the proposed attention model is seamlessly integrated into the CRF allowing end-to-end training of the entire architecture. Our extensive experimental evaluation demonstrates the effectiveness of the proposed method which is competitive with previous methods on the KITH benchmark and outperforms the state of the art on the NYU Depth V2 dataset.

关键词： Estimation Predictive models Task analysis computer architecture Semantics Computational modeling Fuses

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R-FCN-3000 at 30fps: Decoupling Detection and Classification 31

R-FCN-3000 at 30fps: Decoupling Detection and Classification

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31st ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Singh, Bharat Li, Hengduo Sharma, Abhishek Davis, Larry S. Univ Maryland College Pk MD 20742 USA Fudan Univ Shanghai Peoples R China Gobasco AI Labs New Delhi India

ISBN: (纸本)9781538664209

We propose a modular approach towards large-scale real-time object detection by decoupling objectness detection and classification. We exploit the fact that many object classes are visually similar and share parts. Thus, a universal objectness detector can be learned for class-agnostic object detection followed by fine-grained classification using a (non)linear classifier. Our approach is a modification of the R-FCN architecture to learn shared filters for performing localization across different object classes. We trained a detector for 3000 object classes, called R-FCN-3000, that obtains an mAP of 34.9% on the ImageNet detection dataset. It outperforms YOLO-9000 by 18% while processing 30 images per second. We also show that the objectness learned by R-FCN-3000 generalizes to novel classes and the performance increases with the number of training object classes - supporting the hypothesis that it is possible to learn a universal objectness detector. Code will be made available.

关键词： Detectors Object detection Training Proposals Convolution computer architecture Dogs

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RIM-Net: Recursive Implicit Fields for Unsupervised Learning of Hierarchical Shape Structures

RIM-Net: Recursive Implicit Fields for Unsupervised Learning...

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

作者： Niu, Chengjie Li, Manyi Xu, Kai Zhang, Hao Natl Univ Def Technol Changsha Peoples R China Shandong Univ Jinan Peoples R China Simon Fraser Univ Burnaby BC Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We introduce RIM-Net, a neural network which learns recursive implicit fields for unsupervised inference of hierarchical shape structures. Our network recursively decomposes an input 3D shape into two parts, resulting in a binary tree hierarchy. Each level of the tree corresponds to an assembly of shape parts, represented as implicit functions, to reconstruct the input shape. At each node of the tree, simultaneous feature decoding and shape decomposition are carried out by their respective feature and part decoders, with weight sharing across the same hierarchy level. As an implicit field decoder, the part decoder is designed to decompose a sub-shape, via a two-way branched reconstruction, where each branch predicts a set of parameters defining a Gaussian to serve as a local point distribution for shape reconstruction. With reconstruction losses accounted for at each hierarchy level and a decomposition loss at each node, our network training does not require any ground-truth segmentations, let alone hierarchies. Through extensive experiments and comparisons to state-of-the-art alternatives, we demonstrate the quality, consistency, and interpretability of hierarchical structural inference by RIM-Net.

关键词： Training computer vision Three-dimensional displays Shape Neural networks Binary trees Decoding

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OBJECTFOLDER 2.0: A Multisensory Object Dataset for Sim2Real Transfer

OBJECTFOLDER 2.0: A Multisensory Object Dataset for Sim2Real...

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

作者： Gao, Ruohan Si, Zilin Chang, Yen-Yu Clarke, Samuel Bohg, Jeannette Li Fei-Fei Yuan, Wenzhen Wu, Jiajun Stanford Univ Stanford CA 94305 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Objects play a crucial role in our everyday activities. Though multisensory object-centric learning has shown great potential lately, the modeling of objects in prior work is rather unrealistic. OBJECTFOLDER 1.0 is a recent dataset that introduces 100 virtualized objects with visual, acoustic, and tactile sensory data. However;the dataset is small in scale and the multisensory data is of limited quality, hampering generalization to real-world scenarios. We present OBJECTFOLDER 2.0, a large-scale, multisensory dataset of common household objects in the form of implicit neural representations that significantly enhances OBJECTFOLDER 1.0 in three aspects. First, our dataset is 10 times larger in the amount of objects and orders of magnitude faster in rendering time. Second, we significantly improve the multisensory rendering quality for all three modalities. Third, we show that models learned from virtual objects in our dataset successfully transfer to their real-world counterparts in three challenging tasks: object scale estimation, contact localization, and shape reconstruction. OBJECTFOLDER 2.0 offers a new path and testbed for multisensory learning in computer vision and robotics. The dataset is available at https: //***/rhgao/ObjectFolder.

关键词： Location awareness computer vision Visualization Shape Computational modeling Estimation Rendering (computer graphics)

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Image Super-Resolution via Dual-State Recurrent Networks 31

Image Super-Resolution via Dual-State Recurrent Networks

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31st ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Han, Wei Chang, Shiyu Liu, Ding Yu, Mo Witbrock, Michael Huang, Thomas S. Univ Illinois Champaign IL 61820 USA IBM Res Yorktown Hts NY USA

ISBN: (纸本)9781538664209

Advances in image super-resolution (SR) have recently benefited significantly from rapid developments in deep neural networks. Inspired by these recent discoveries, we note that many state-of-the-art deep SR architectures can be reformulated as a single-state recurrent neural network (RNN) with finite unfoldings. In this paper, we explore new structures for SR based on this compact RNN view, leading us to a dual-state design, the Dual-State Recurrent Network (DSRN). Compared to its single-state counterparts that operate at a fixed spatial resolution, DSRN exploits both low-resolution (LR) and high-resolution (HR) signals jointly. Recurrent signals are exchanged between these states in both directions (both LR to HR and HR to LR) via delayed feedback. Extensive quantitative and qualitative evaluations on benchmark datasets and on a recent challenge demonstrate that the proposed DSRN performs favorably against state-of-the-art algorithms in terms of both memory consumption and predictive accuracy. The code for our method is publicly available.

关键词： Spatial resolution Signal resolution Recurrent neural networks Computational modeling computer architecture

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Rainbow Memory: Continual Learning with a Memory of Diverse Samples

Rainbow Memory: Continual Learning with a Memory of Diverse ...

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

作者： Bang, Jihwan Kim, Heesu Yoo, YoungJoon Ha, Jung-Woo Choi, Jonghyun Search Solut Inc Thousand Oaks CA USA NAVER CLOVA Seongnam South Korea NAVER AI Lab Seongnam South Korea GIST Gwangju South Korea

ISBN: (纸本)9781665445092

Continual learning is a realistic learning scenario for AI models. Prevalent scenario of continual learning, however;assumes disjoint sets of classes as tasks and is less realistic rather artificial. Instead, we focus on 'blurry' task boundary;where tasks shares classes and is more realistic and practical. To address such task, we argue the importance of diversity of samples in an episodic memory. To enhance the sample diversity in the memory, we propose a novel memory management strategy based on persample classification uncertainty and data augmentation, named Rainbow Memory (RM). With extensive empirical validations on MNIST CIFAR10, CIFAR100, and ImageNet datasets, we show that the proposed method significantly improves the accuracy in blurry continual learning setups, outperforming state of the arts by large margins despite its simplicity.

关键词： Training computer vision Uncertainty Codes Memory management Learning (artificial intelligence) Sampling methods

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Meta-Personalizing vision-Language Models to Find Named Instances in Video

Meta-Personalizing Vision-Language Models to Find Named Inst...

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

作者： Yeh, Chun-Hsiao Russell, Bryan Sivic, Josef Heilbron, Fabian Caba Jenni, Simon Univ Calif Berkeley Berkeley CA 94720 USA Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic Adobe Res San Francisco CA 94107 USA

ISBN: (纸本)9798350301298

Large-scale vision-language models (VLM) have shown impressive results for language-guided search applications. While these models allow category-level queries, they currently struggle with personalized searches for moments in a video where a specific object instance such as "My dog Biscuit" appears. We present the following three contributions to address this problem. First, we describe a method to meta-personalize a pre-trained VLM, i.e., learning how to learn to personalize a VLM at test time to search in video. Our method extends the VLM's token vocabulary by learning novel word embeddings specific to each instance. To capture only instance-specific features, we represent each instance embedding as a combination of shared and learned global category features. Second, we propose to learn such personalization without explicit human supervision. Our approach automatically identifies moments of named visual instances in video using transcripts and vision-language similarity in the VLM's embedding space. Finally, we introduce This-Is-My, a personal video instance retrieval benchmark. We evaluate our approach on This-Is-My and Deep-Fashion2 and show that we obtain a 15% relative improvement over the state of the art on the latter dataset.

关键词： language reasoning vision

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