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

共 31021 条记录，以下是4191-4200 订阅

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Unsupervised Hyperbolic Representation Learning via Message Passing Auto-Encoders

Unsupervised Hyperbolic Representation Learning via Message ...

引用

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

作者： Park, Jiwoong Cho, Junho Chang, Hyung Jin Choi, Jin Young Seoul Natl Univ Dept ECE ASRI Seoul South Korea Univ Birmingham Sch Comp Sci Birmingham W Midlands England

ISBN: (纸本)9781665445092

Most of the existing literature regarding hyperbolic embedding concentrate upon supervised learning, whereas the use of unsupervised hyperbolic embedding is less well explored. In this paper, we analyze how unsupervised tasks can benefit from learned representations in hyperbolic space. To explore how well the hierarchical structure of unlabeled data can be represented in hyperbolic spaces, we design a novel hyperbolic message passing auto-encoder whose overall auto-encoding is performed in hyperbolic space. The proposed model conducts auto-encoding the networks via fully utilizing hyperbolic geometry in message passing. Through extensive quantitative and qualitative analyses, we validate the properties and benefits of the unsupervised hyperbolic representations.

关键词： Geometry Visualization computer vision Codes Message passing Supervised learning Space exploration

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Fair Attribute Classification through Latent Space De-biasing

Fair Attribute Classification through Latent Space De-biasin...

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

作者： Ramaswamy, Vikram V. Kim, Sunnie S. Y. Russakovsky, Olga Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781665445092

Fairness in visual recognition is becoming a prominent and critical topic of discussion as recognition systems are deployed at scale in the real world. Models trained from data in which target labels are correlated with protected attributes (e.g., gender, race) are known to learn and exploit those correlations. In this work, we introduce a method for training accurate target classifiers while mitigating biases that stem from these correlations. We use GANs to generate realistic-looking images, and perturb these images in the underlying latent space to generate training data that is balanced for each protected attribute. We augment the original dataset with this generated data, and empirically demonstrate that target classifiers trained on the augmented dataset exhibit a number of both quantitative and qualitative benefits. We conduct a thorough evaluation across multiple target labels and protected attributes in the CelebA dataset, and provide an in-depth analysis and comparison to existing literature in the space. Code can be found at https://***/princetonvisualai/gan-debiasing.

关键词： Training Measurement Visualization computer vision Correlation Codes Training data

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StyLitGAN: Image-Based Relighting via Latent Control

StyLitGAN: Image-Based Relighting via Latent Control

引用

conference on computer vision and pattern recognition (CVPR)

作者： Anand Bhattad James Soole D.A. Forsyth University of Illinois Urbana-Champaign

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We describe a novel method, StyLitGAN, for relighting and resurfacing images in the absence of labeled data. StyL-itGAN generates images with realistic lighting effects, including cast shadows, soft shadows, inter-reflections, and glossy effects, without the need for paired or CGI data. StyLit-GAN uses an intrinsic image method to decompose an image, followed by a search of the latent space of a pretrained Style-GAN to identify a set of directions. By prompting the model to fix one component (e.g., albedo) and vary another (e.g., shading), we generate relighted images by adding the identi-fied directions to the latent style codes. Quantitative metrics of change in albedo and lighting diversity allow us to choose effective directions using a forward selection process. Qual-itative evaluation confirms the effectiveness of our method.

关键词： Measurement computer vision Codes Lighting pattern recognition

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CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation

CyberDemo: Augmenting Simulated Human Demonstration for Real...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jun Wang Yuzhe Qin Kaiming Kuang Yigit Korkmaz Akhilan Gurumoorthy Hao Su Xiaolong Wang UC San Diego University of Southern California

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce CyberDemo, a novel approach to robotic imitation learning that leverages simulated human demonstrations for real-world tasks. By incorporating extensive data augmentation in a simulated environment, CyberDemo outperforms traditional in-domain real-world demonstrations when transferred to the real world, handling diverse physical and visual conditions. Regardless of its affordability and convenience in data collection, CyberDemo outperforms baseline methods in terms of success rates across various tasks and exhibits generalizability with previously unseen objects. For example, it can rotate novel tetra-valve and penta-valve, despite human demonstrations only involving tri-valves. Our research demonstrates the significant potential of simulated human demonstrations for realworld dexterous manipulation tasks. More details can be found at https://***/

关键词： Visualization computer vision Imitation learning Data collection Data augmentation pattern recognition

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Unsupervised Hyperbolic Metric Learning

Unsupervised Hyperbolic Metric Learning

引用

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

作者： Yan, Jiexi Luo, Lei Deng, Cheng Huang, Heng Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Univ Pittsburgh Dept Elect & Comp Engn Pittsburgh PA 15260 USA JD Finance Amer Corp Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Learning feature embedding directly from images without any human supervision is a very challenging and essential task in the field of computer vision and machine learning. Following the paradigm in supervised manner, most existing unsupervised metric learning approaches mainly focus on binary similarity in Euclidean space. However, these methods cannot achieve promising performance in many practical applications, where the manual information is lacking and data exhibits non-Euclidean latent anatomy. To address this limitation, we propose an Unsupervised Hyperbolic Metric Learning method with Hierarchical Similarity. It considers the natural hierarchies of data by taking advantage of Hyperbolic metric learning and hierarchical clustering, which can effectively excavate richer similarity information beyond binary in modeling. More importantly, we design a new loss function to capture the hierarchical similarity among samples to enhance the stability of the proposed method. Extensive experimental results on benchmark datasets demonstrate that our method achieves state-of-the-art performance compared with current unsupervised deep metric learning approaches.

关键词： Measurement Learning systems computer vision Manuals Benchmark testing Stability analysis pattern recognition

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Fusing Forces: Deep-Human-Guided Refinement of Segmentation Masks 27th

Fusing Forces: Deep-Human-Guided Refinement of Segmentation ...

引用

27th International conference on pattern recognition, ICPR 2024

作者： Sterzinger, Rafael Stippel, Christian Sablatnig, Robert Computer Vision Lab TU Wien Vienna Austria

ISBN: (纸本)9783031781971

Etruscan mirrors constitute a significant category in Etruscan art, characterized by elaborate figurative illustrations featured on their backside. A laborious and costly aspect of their analysis and documentation is the task of manually tracing these illustrations. In previous work, a methodology has been proposed to automate this process, involving photometric-stereo scanning in combination with deep neural networks. While achieving quantitative performance akin to an expert annotator, some results still lack qualitative precision and, thus, require annotators for inspection and potential correction, maintaining resource intensity. In response, we propose a deep neural network trained to interactively refine existing annotations based on human guidance. Our human-in-the-loop approach streamlines annotation, achieving equal quality with up to 75% less manual input required. Moreover, during the refinement process, the relative improvement of our methodology over pure manual labeling reaches peak values of up to 26%, attaining drastically better quality quicker. By being tailored to the complex task of segmenting intricate lines, specifically distinguishing it from previous methods, our approach offers drastic improvements in efficacy, transferable to a broad spectrum of applications beyond Etruscan mirrors. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mirrors

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Beyond Short Clips: End-to-End Video-Level Learning with Collaborative Memories

Beyond Short Clips: End-to-End Video-Level Learning with Col...

引用

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

作者： Yang, Xitong Fan, Haoqi Torresani, Lorenzo Davis, Larry Wang, Heng Univ Maryland College Pk MD 20742 USA Facebook AI Menlo Pk CA USA

ISBN: (纸本)9781665445092

The standard way of training video models entails sampling at each iteration a single clip from a video and optimizing the clip prediction with respect to the video-level label. We argue that a single clip may not have enough temporal coverage to exhibit the label to recognize, since video datasets are often weakly labeled with categorical information but without dense temporal annotations. Furthermore, optimizing the model over brief clips impedes its ability to learn long-term temporal dependencies. To overcome these limitations, we introduce a collaborative memory mechanism that encodes information across multiple sampled clips of a video at each training iteration. This enables the learning of long-range dependencies beyond a single clip. We explore different design choices for the collaborative memory to ease the optimization difficulties. Our proposed framework is end-to-end trainable and significantly improves the accuracy of video classification at a negligible computational overhead. Through extensive experiments, we demonstrate that our framework generalizes to different video architectures and tasks, outperforming the state of the art on both action recognition (e.g., Kinetics-400 & 700, Charades, Something-Something-VI) and action detection (e.g., AVA v2.1 & v2.2).

关键词： Training computer vision Collaboration computer architecture Predictive models Fasteners pattern recognition

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Towards Real-World Blind Face Restoration with Generative Facial Prior

Towards Real-World Blind Face Restoration with Generative Fa...

引用

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

作者： Wang, Xintao Li, Yu Zhang, Honglun Shan, Ying Tencent PCG Appl Res Ctr ARC Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets.

关键词： Geometry computer vision Art Limiting Image color analysis Face recognition Transforms

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Neural Splines: Fitting 3D Surfaces with Infinitely-Wide Neural Networks

Neural Splines: Fitting 3D Surfaces with Infinitely-Wide Neu...

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

作者： Williams, Francis Trager, Matthew Bruna, Joan Zorin, Denis NYU New York NY 10003 USA Amazon Seattle WA USA

ISBN: (纸本)9781665445092

We present Neural Splines, a technique for 3D surface reconstruction that is based on random feature kernels arising from infinitely-wide shallow ReLU networks. Our method achieves state-of-the-art results, outperforming recent neural network-based techniques and widely used Poisson Surface Reconstruction (which, as we demonstrate, can also be viewed as a type of kernel method). Because our approach is based on a simple kernel formulation, it is easy to analyze and can be accelerated by general techniques designed for kernel-based learning. We provide explicit analytical expressions for our kernel and argue that our formulation can be seen as a generalization of cubic spline interpolation to higher dimensions. In particular, the RKHS norm associated with Neural Splines biases toward smooth interpolants.

关键词： Surface reconstruction Interpolation computer vision Three-dimensional displays Neural networks Fitting Surface fitting

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DeepObjStyle: Deep Object-based Photo Style Transfer

DeepObjStyle: Deep Object-based Photo Style Transfer

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

作者： Mastan, Indra Deep Raman, Shanmuganathan Indian Inst Technol Gandhinagar Gandhinagar Gujarat India

ISBN: (纸本)9781665448994

One of the major challenges of style transfer is the appropriate image features supervision between the output image and the input images (style and content). An efficient strategy would be to define an object map between the objects of the style and the content images. However, such a mapping is not well established when there are semantic objects of different types and numbers in the style and the content images. It also leads to content mismatch in the style transfer output, which could reduce the visual quality of the results. We propose an object-based style transfer approach, called DeepObjStyle, for the style supervision in the training data-independent framework. DeepObjStyle preserves the semantics of the objects and achieves better style transfer in the challenging scenario when the style and the content images have a mismatch of image features. We also perform style transfer of images containing a word cloud to demonstrate that DeepObjStyle enables an appropriate image features supervision. We validate the results using quantitative comparisons and user studies.

关键词： Training Visualization computer vision conferences Semantics Tag clouds Quality assessment

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