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

共 30983 条记录，以下是4721-4730 订阅

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Learning Feature Aggregation for Deep 3D Morphable Models

Learning Feature Aggregation for Deep 3D Morphable Models

引用

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

作者： Chen, Zhixiang Kim, Tae-Kyun Imperial Coll London London England Korea Adv Inst Sci & Technol Seoul South Korea

ISBN: (纸本)9781665445092

3D morphable models are widely used for the shape representation of an object class in computer vision and graphics applications. In this work, we focus on deep 3D morphable models that directly apply deep learning on 3D mesh data with a hierarchical structure to capture information at multiple scales. While great efforts have been made to design the convolution operator, how to best aggregate vertex features across hierarchical levels deserves further attention. In contrast to resorting to mesh decimation, we propose an attention based module to learn mapping matrices for better feature aggregation across hierarchical levels. Specifically, the mapping matrices are generated by a compatibility function of the keys and queries. The keys and queries are trainable variables, learned by optimizing the target objective, and shared by all data samples of the same object class. Our proposed module can be used as a train-only drop-in replacement for the feature aggregation in existing architectures for both downsampling and upsampling. Our experiments show that through the end-to-end training of the mapping matrices, we achieve state-of-the-art results on a variety of 3D shape datasets in comparison to existing morphable models.

关键词： Training Deep learning Solid modeling computer vision Three-dimensional displays Shape Convolution

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Pulsar: Efficient Sphere-based Neural Rendering

Pulsar: Efficient Sphere-based Neural Rendering

引用

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

作者： Lassner, Christoph Zollhofer, Michael Facebook Real Labs Redmond WA 98052 USA

ISBN: (纸本)9781665445092

We propose Pulsar, an efficient sphere-based differentiable rendering module that is orders of magnitude faster than competing techniques, modular, and easy-to-use due to its tight integration with PyTorch. Differentiable rendering is the foundation for modern neural rendering approaches, since it enables end-to-end training of 3D scene representations from image observations. However, gradient-based optimization of neural mesh, voxel, or function representations suffers from multiple challenges, i.e., topological inconsistencies, high memory footprints, or slow rendering speeds. To alleviate these problems, Pulsar employs: 1) a sphere-based scene representation, 2) a modular, efficient differentiable projection operation, and 3) (optional) neural shading. Pulsar executes orders of magnitude faster than existing techniques and allows real-time rendering and optimization of representations with millions of spheres. Using spheres for the scene representation, unprecedented speed is obtained while avoiding topology problems. Pulsar is fully differentiable and thus enables a plethora of applications, ranging from 3D reconstruction to neural rendering.

关键词： Training Three-dimensional displays Scalability Rendering (computer graphics) Real-time systems Distance measurement Topology

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Communication Efficient SGD via Gradient Sampling with Bayes Prior

Communication Efficient SGD via Gradient Sampling with Bayes...

引用

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

作者： Song, Liuyihan Zhao, Kang Pan, Pan Liu, Yu Zhang, Yingya Xu, Yinghui Jin, Rong Alibaba Grp Machine Intelligence Technol Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

Gradient compression has been widely adopted in data-parallel distributed training of deep neural networks to reduce communication overhead. Some literatures have demonstrated that large gradients are more important than small ones because they contain more information, such as Top-k compressor. Other mainstream methods, like random-k compressor and gradient quantization, usually treat all gradients equally. Different from all of them, we regard large and small gradients selection as the exploitation and exploration of gradient information, respectively. And we find taking both of them into consideration is the key to boost the final accuracy. So, we propose a novel gradient compressor: Gradient Sampling with Bayes Prior in this paper. Specifically, we sample important/large gradients based on the global gradient distribution, which is periodically updated across multiple workers. Then we introduce Bayes Prior into distribution model to further explore the gradients. We prove the convergence of our method for smooth non-convex problems in the distributed system. Compared with methods that running after high compression ratio at the expense of accuracy, we pursue no loss of accuracy and the actual acceleration benefit in practice. Experimental comparisons on a variety of computer vision tasks (e.g. image classification and object detection) and backbones (ResNet, MobileNetV2, InceptionV3 and AlexNet) show that our approach outperforms the state-of-the-art techniques in terms of both speed and accuracy, with the limitation of 100x compression ratio.

关键词： Training Degradation computer vision Image coding Quantization (signal) Object detection pattern recognition

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Reconstructing 3D Human Pose by Watching Humans in the Mirror

Reconstructing 3D Human Pose by Watching Humans in the Mirro...

引用

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

作者： Fang, Qi Shuai, Qing Dong, Junting Bao, Hujun Zhou, Xiaowei Zhejiang Univ State Key Lab CAD&CG Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

In this paper, we introduce the new task of reconstructing 3D human pose from a single image in which we can see the person and the person's image through a mirror. Compared to general scenarios of 3D pose estimation from a single view, the mirror reflection provides an additional view for resolving the depth ambiguity. We develop an optimization-based approach that exploits mirror symmetry constraints for accurate 3D pose reconstruction. We also provide a method to estimate the surface normal of the mirror from vanishing points in the single image. To validate the proposed approach, we collect a large-scale dataset named Mirrored-Human, which covers a large variety of human subjects, poses and backgrounds. The experiments demonstrate that, when trained on Mirrored-Human with our reconstructed 3D poses as pseudo ground-truth, the accuracy and generalizability of existing single-view 3D pose estimators can be largely improved. The code and dataset are available at https://***/Mirrored-Human/.

关键词： Training Surface reconstruction Three-dimensional displays Shape Pose estimation Reflection pattern recognition

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pix2gestalt: Amodal Segmentation by Synthesizing Wholes

pix2gestalt: Amodal Segmentation by Synthesizing Wholes

引用

conference on computer vision and pattern recognition (CVPR)

作者： Ege Ozguroglu Ruoshi Liu Dídac Surís Dian Chen Achal Dave Pavel Tokmakov Carl Vondrick Columbia University Toyota Research Institute

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce pix2gestalt, a framework for zero-shot amodal segmentation, which learns to estimate the shape and appearance of whole objects that are only partially visible behind occlusions. By capitalizing on large-scale diffusion models and transferring their representations to this task, we learn a conditional diffusion model for reconstructing whole objects in challenging zero-shot cases, including examples that break natural and physical priors, such as art. As training data, we use a synthetically curated dataset containing occluded objects paired with their whole counterparts. Experiments show that our approach outperforms supervised baselines on established benchmarks. Our model can furthermore be used to significantly improve the performance of existing object recognition and 3D reconstruction methods in the presence of occlusions.

关键词： computer vision Solid modeling Three-dimensional displays Art Shape Training data Benchmark testing

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Open World Compositional Zero-Shot Learning

Open World Compositional Zero-Shot Learning

引用

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

作者： Mancini, Massimiliano Naeem, Muhammad Ferjad Xian, Yongqin Akata, Zeynep Univ Tubingen Tubingen Germany Tech Univ Munich Munich Germany MPI Informat Saarbrucken Germany MPI Intelligent Syst Saarbrucken Germany

ISBN: (纸本)9781665445092

Compositional Zero-Shot learning (CZSL) requires to recognize state-object compositions unseen during training. In this work, instead of assuming prior knowledge about the unseen compositions, we operate in the open world setting, where the search space includes a large number of unseen compositions some of which might be unfeasible. In this setting, we start from the cosine similarity between visual features and compositional embeddings. After estimating the feasibility score of each composition, we use these scores to either directly mask the output space or as a margin for the cosine similarity between visual features and compositional embeddings during training. Our experiments on two standard CZSL benchmarks show that all the methods suffer severe performance degradation when applied in the open world setting. While our simple CZSL model achieves state-of-the-art performances in the closed world scenario, our feasibility scores boost the performance of our approach in the open world setting, clearly outperforming the previous state of the art. Code is available at:https://***/ExplainableML/czsl.

关键词： Training Degradation Visualization computer vision Computational modeling Knowledge based systems Benchmark testing

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SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences

SceneGraphFusion: Incremental 3D Scene Graph Prediction from...

引用

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

作者： Wu, Shun-Cheng Wald, Johanna Tateno, Keisuke Navab, Nassir Tombari, Federico Tech Univ Munich Munich Germany Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks. This work proposes a method to incrementally build up semantic scene graphs from a 3D environment given a sequence of RGB-D frames. To this end, we aggregate PointNet features from primitive scene components by means of a graph neural network. We also propose a novel attention mechanism well suited for partial and missing graph data present in such an incremental reconstruction scenario. Although our proposed method is designed to run on submaps of the scene, we show it also transfers to entire 3D scenes. Experiments show that our approach outperforms 3D scene graph prediction methods by a large margin and its accuracy is on par with other 3D semantic and panoptic segmentation methods while running at 35Hz.

关键词： computer vision Three-dimensional displays Simultaneous localization and mapping Fuses Semantics Prediction methods Graph neural networks

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Long-term Head Pose Forecasting Conditioned on the Gaze-guiding Prior

Long-term Head Pose Forecasting Conditioned on the Gaze-guid...

引用

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

作者： Mo, Shentong Xin, Miao Carnegie Mellon Univ Pittsburgh PA 15213 USA Chinese Acad Sci Inst Automat CASIA Beijing Peoples R China

ISBN: (纸本)9781665448994

Forecasting head pose future states is a novel task in computer vision. Since future may have many possibilities, and the logical results are much more important than the impractical ones, the forecasting results for most of the scenarios should be not only diverse but also logically realistic. These requirements pose a real challenge to the current methods, which motivates us to seek for better head pose representation and methods to restrict the forecasting reasonably. In this paper, we adopt a spatial-temporal graph to model the interdependencies between the distribution of landmarks and head pose angles. Furthermore, we propose the conditional spatial-temporal variational graph autoencoder (CST-VGAE), a deep conditional generative model for learning restricted one-to-many mappings conditioned on the spatial-temporal graph input. Specifically, we improve the proposed CST-VGAE for the long-term head pose forecasting task in terms of several aspects. First, we introduce a gaze-guiding prior based on the physiology. Then we apply a temporal self-attention and self-supervised learning mechanism to learn the long-range dependencies on the gaze prior. To better model head poses structurally, we introduce a Gaussian Mixture Model (GMM), instead of a fixed Gaussian in the encoded latent space. Experiments demonstrate the effectiveness of the proposed method for the long-term head pose forecasting task. We achieve superior forecasting performance on the benchmark datasets compared to the existing methods.

关键词： Learning systems computer vision conferences Predictive models Benchmark testing Physiology pattern recognition

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AutoInt: Automatic Integration for Fast Neural Volume Rendering

AutoInt: Automatic Integration for Fast Neural Volume Render...

引用

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

作者： Lindell, David B. Martel, Julien N. P. Wetzstein, Gordon Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665445092

Numerical integration is a foundational technique in scientific computing and is at the core of many computer vision applications. Among these applications, neural volume rendering has recently been proposed as a new paradigm for view synthesis, achieving photorealistic image quality. However, a fundamental obstacle to making these methods practical is the extreme computational and memory requirements caused by the required volume integrations along the rendered rays during training and inference. Millions of rays, each requiring hundreds of forward passes through a neural network are needed to approximate those integrations with Monte Carlo sampling. Here, we propose automatic integration, a new framework for learning efficient, closed-form solutions to integrals using coordinate-based neural networks. For training, we instantiate the computational graph corresponding to the derivative of the coordinate-based network. The graph is fitted to the signal to integrate. After optimization, we reassemble the graph to obtain a network that represents the antiderivative. By the fundamental theorem of calculus, this enables the calculation of any definite integral in two evaluations of the network. Applying this approach to neural rendering, we improve a tradeoff between rendering speed and image quality: improving render times by greater than 10x with a tradeoff of reduced image quality.

关键词： Training Image quality computer vision Monte Carlo methods Scientific computing Neural networks Memory management

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Graph-based High-Order Relation Discovery for Fine-grained recognition

Graph-based High-Order Relation Discovery for Fine-grained R...

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

作者： Zhao, Yifan Yan, Ke Huang, Feiyue Li, Jia Beihang Univ State Key Lab Virtual Real Technol & Syst SCSE Beijing Peoples R China Tencent Youtu Lab Shanghai Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Fine-grained object recognition aims to learn effective features that can identify the subtle differences between visually similar objects. Most of the existing works tend to amplify discriminative part regions with attention mechanisms. Besides its unstable performance under complex backgrounds, the intrinsic interrelationship between different semantic features is less explored. Toward this end, we propose an effective graph-based relation discovery approach to build a contextual understanding of high-order relationships. In our approach, a high-dimensional feature bank is first formed and jointly regularized with semantic- and positional-aware high-order constraints, endowing rich attributes to feature representations. Second, to overcome the high-dimension curse, we propose a graph-based semantic grouping strategy to embed this high-order tensor bank into a low-dimensional space. Meanwhile, a group-wise learning strategy is proposed to regularize the features focusing on the cluster embedding center. With the collaborative learning of three modules, our module is able to grasp the stronger contextual details of fine-grained objects. Experimental evidence demonstrates our approach achieves new state-of-the-art on 4 widely-used fine-grained object recognition benchmarks.

关键词： Training Manifolds computer vision Tensors Semantics Focusing Collaborative work

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