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

共 22905 条记录，以下是4971-4980 订阅

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ShapeWalk: Compositional Shape Editing through Language-Guided Chains

ShapeWalk: Compositional Shape Editing through Language-Guid...

引用

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

作者： Slim, Habib Elhoseiny, Mohamed KAUST Thuwal Saudi Arabia

ISBN: (纸本)9798350353006

Editing 3D shapes through natural language instructions is a challenging task that requires the comprehension of both language semantics and fine-grained geometric details. To bridge this gap, we introduce ShapeWalk, a carefully designed synthetic dataset designed to advance the field of language-guided shape editing. The dataset consists of 158K unique shapes connected through 26K edit chains, with an average length of 14 chained shapes. Each consecutive pair of shapes is associated with precise language instructions describing the applied edits. We synthesize edit chains by reconstructing and interpolating shapes sampled from a realistic CAD-designed 3D dataset in the parameter space of the GeoCode shape program. We leverage rule-based methods and language models to generate accurate and realistic natural language prompts corresponding to each edit. To illustrate the practicality of our contribution, we train neural editor modules in the latent space of shape autoencoders, and demonstrate the ability of our dataset to enable a variety of language-guided shape edits. Finally, we introduce multi-step editing metrics to benchmark the capacity of our models to perform recursive shape edits. We hope that our work will enable further study of compositional language-guided shape editing, and finds application in 3D CAD design and interactive modeling.

关键词： 3D language editing 3D vision compositionality

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HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences

HumanGPS: Geodesic PreServing Feature for Dense Human Corres...

引用

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

作者： Tan, Feitong Tang, Danhang Dou, Mingsong Guo, Kaiwen Pandey, Rohit Keskin, Cem Du, Ruofei Sun, Deqing Bouaziz, Sofien Fanello, Sean Tan, Ping Zhang, Yinda Google Mountain View CA 94043 USA Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665445092

In this paper, we address the problem of building dense correspondences between human images under arbitrary camera viewpoints and body poses. Prior art either assumes small motion between frames or relies on local descriptors, which cannot handle large motion or visually ambiguous body parts, e.g., left vs. right hand. In contrast, we propose a deep learning framework that maps each pixel to a feature space, where the feature distances reflect the geodesic distances among pixels as if they were projected onto the surface of a 3D human scan. To this end, we introduce novel loss functions to push features apart according to their geodesic distances on the surface. Without any semantic annotation, the proposed embeddings automatically learn to differentiate visually similar parts and align different subjects into an unified feature space. Extensive experiments show that the learned embeddings can produce accurate correspondences between images with remarkable generalization capabilities on both intra and inter subjects.(1)

关键词： Deep learning Image segmentation computer vision Three-dimensional displays Shape Semantics Buildings

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HSA-RNN: Hierarchical Structure-Adaptive RNN for Video Summarization 31

HSA-RNN: Hierarchical Structure-Adaptive RNN for Video Summa...

引用

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

作者： Zhao, Bin Li, Xuelong Lu, Xiaoqiang Northwestern Polytech Univ Sch Comp Sci Xian Shaanxi Peoples R China Northwestern Polytech Univ Ctr OPT IMagery Anal & Learning OPTIMAL Xian Shaanxi Peoples R China Chinese Acad Sci Xian Inst Opt & Precis Mech Xian Shaanxi Peoples R China

ISBN: (纸本)9781538664209

Although video summarization has achieved great success in recent years, few approaches have realized the influence of video structure on the summarization results. As we know, the video data follow ahierarchical structure, i.e., a video is composed of shots, and a shot is composed of several frames. Generally, shots provide the activity-level information for people to understand the video content. While few existing summarization approaches pay attention to the shot segmentation procedure. They generate shots by some trivial strategies, such as fixed length segmentation, which may destroy the underlying hierarchical structure of video data and further reduce the quality of generated summaries. To address this problem, we propose a structure-adaptive video summarization approach that integrates shot segmentation and video summarization into a Hierarchical Structure-Adaptive RNN, denoted as HSA-RNN. We evaluate the proposed approach on four popular datasets, i.e., SumMe, TVsum, CoSum and VTW. The experimental results have demonstrated the effectiveness of HSA-RNN in the video summarization task.

关键词： Feature extraction Motion segmentation Streaming media Visualization Tools Machine learning computer vision

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CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification

CAD: Co-Adapting Discriminative Features for Improved Few-Sh...

引用

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

作者： Chikontwe, Philip Kim, Soopil Park, Sang Hyun DGIST Dept Robot & Mechatron Daegu South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Few-shot classification is a challenging problem that aims to learn a model that can adapt to unseen classes given a few labeled samples. Recent approaches pre-train a feature extractor, and then fine-tune for episodic meta-learning. Other methods leverage spatial features to learn pixel-level correspondence while jointly training a classifier. However, results using such approaches show marginal improvements. In this paper, inspired by the transformer style self-attention mechanism, we propose a strategy to cross-attend and re-weight discriminative features for few-shot classification. Given a base representation of support and query images after global pooling, we introduce a single shared module that projects features and cross-attends in two aspects: (i) query to support, and (ii) support to query. The module computes attention scores between features to produce an attention pooled representation of features in the same class that is later added to the original representation followed by a projection head. This effectively re-weights features in both aspects (i & ii) to produce features that better facilitate improved metric-based meta-learning. Extensive experiments on public benchmarks show our approach outperforms state-of-the-art methods by 3%similar to 5%.

关键词： Training Solid modeling computer vision Head Transfer learning Prototypes Feature extraction

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Iterative Residual CNNs for Burst Photography Applications 32

Iterative Residual CNNs for Burst Photography Applications

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32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kokkinos, Filippos Lefkimmiatis, Stamatios Skolkovo Inst Sci & Technol Skoltech Moscow Russia

ISBN: (纸本)9781728132938

Modern inexpensive imaging sensors suffer from inherent hardware constraints which often result in captured images of poor quality. Among the most common ways to deal with such limitations is to rely on burst photography, which nowadays acts as the backbone of all modern smartphone imaging applications. In this work, we focus on the fact that every frame of a burst sequence can be accurately described by a forward (physical) model. This, in turn, allows us to restore a single image of higher quality from a sequence of low-quality images as the solution of an optimization problem. Inspired by an extension of the gradient descent method that can handle non-smooth functions, namely the proximal gradient descent, and modern deep learning techniques, we propose a convolutional iterative network with a transparent architecture. Our network uses a burst of low-quality image frames and is able to produce an output of higher image quality recovering fine details which are not distinguishable in any of the original burst frames. We focus both on the burst photography pipeline as a whole, i.e., burst demosaicking and denoising, as well as on the traditional Gaussian denoising task. The developed method demonstrates consistent state-of-the art performance across the two tasks and as opposed to other recent deep learning approaches does not have any inherent restrictions either to the number of frames or their ordering.

关键词： Computational Photography Deep Learning Low-level vision Optimization Methods

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Sparse and Complete Latent Organization for Geospatial Semantic Segmentation

Sparse and Complete Latent Organization for Geospatial Seman...

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

作者： Yang, Fengyu Ma, Chenyang Univ Michigan Ann Arbor MI 48109 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Geospatial semantic segmentation on remote sensing images suffers from large intra-class variance in both foreground and background classes. First, foreground objects are tiny in the remote sensing images and are represented by only a few pixels, which leads to large foreground intraclass variance and undermines the discrimination between foreground classes (issue firstly considered in this work). Second, background class contains complex context, which results in false alarms due to large background intra-class variance. To alleviate these two issues, we construct a sparse and complete latent structure via prototypes. In particular, to enhance the sparsity of the latent space, we design a prototypical contrastive learning to have prototypes of the same category clustering together and prototypes of different categories to be far away from each other. Also, we strengthen the completeness of the latent space by modeling all foreground categories and hardest (nearest) background objects. We further design a patch shuffle augmentation for remote sensing images with complicated contexts. Our augmentation encourages the semantic information of an object to he correlated only to the limited context within the patch that is specific to its category, which further reduces large intra-class variance. We conduct extensive evaluations on a large scale remote sensing dataset, showing our approach significantly outperforms state-of-the-art methods by a large margin.

关键词： Image segmentation computer vision Computational modeling Semantics Prototypes Organizations Geospatial analysis

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MetaFusion: Infrared and Visible Image Fusion via Meta-Feature Embedding from Object Detection

MetaFusion: Infrared and Visible Image Fusion via Meta-Featu...

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

作者： Zhao, Wenda Xie, Shigeng Zhao, Fan He, You Lu, Huchuan Dalian Univ Technol Dalian Peoples R China Liaoning Normal Univ Dalian Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Fusing infrared and visible images can provide more texture details for subsequent object detection task. Conversely, detection task furnishes object semantic information to improve the infrared and visible image fusion. Thus, a joint fusion and detection learning to use their mutual promotion is attracting more attention. However, the feature gap between these two different-level tasks hinders the progress. Addressing this issue, this paper proposes an infrared and visible image fusion via meta-feature embedding from object detection. The core idea is that meta-feature embedding model is designed to generate object semantic features according to fusion network ability, and thus the semantic features are naturally compatible with fusion features. It is optimized by simulating a meta learning. Moreover, we further implement a mutual promotion learning between fusion and detection tasks to improve their performances. Comprehensive experiments on three public datasets demonstrate the effectiveness of our method. Code and model are available at: https://***/wdzhao123/MetaFusion.

关键词： Low-level vision

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Interactive Self-Training with Mean Teachers for Semi-supervised Object Detection

Interactive Self-Training with Mean Teachers for Semi-superv...

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

作者： Yang, Qize Wei, Xihan Wang, Biao Hua, Xian-Sheng Zhang, Lei Alibaba Grp Hangzhou Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9781665445092

The goal of semi-supervised object detection is to learn a detection model using only a few labeled data and large amounts of unlabeled data, thereby reducing the cost of data labeling. Although a few studies have proposed various self-training-based methods or consistency regularization-based methods, they ignore the discrepancies among the detection results in the same image that occur during different training iterations. Additionally, the predicted detection results vary among different detection models. In this paper, we propose an interactive form of self-training using mean teachers for semi-supervised object detection. Specifically, to alleviate the instability among the detection results in different iterations, we propose using nonmaximum suppression to fuse the detection results from different iterations. Simultaneously, we use multiple detection heads that predict pseudo labels for each other to provide complementary information. Furthermore, to avoid different detection heads collapsing to each other, we use a mean teacher model instead of the original detection model to predict the pseudo labels. Thus, the object detection model can be trained on both labeled and unlabeled data. Extensive experimental results verify the effectiveness of our proposed method.

关键词： Training computer vision Head Costs Fuses Object detection Predictive models

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PISE: Person Image Synthesis and Editing with Decoupled GAN

PISE: Person Image Synthesis and Editing with Decoupled GAN

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

作者： Zhang, Jinsong Li, Kun Lai, Yu-Kun Yang, Jingyu Tianjin Univ Beijing Peoples R China Cardiff Univ Cardiff Wales

ISBN: (纸本)9781665445092

Person image synthesis, e.g., pose transfer, is a challenging problem due to large variation and occlusion. Existing methods have difficulties predicting reasonable invisible regions and fail to decouple the shape and style of clothing, which limits their applications on person image editing. In this paper, we propose PISE, a novel two-stage generative model for Person Image Synthesis and Editing, which is able to generate realistic person images with desired poses, textures, or semantic layouts. For human pose transfer, we first synthesize a human parsing map aligned with the target pose to represent the shape of clothing by a parsing generator, and then generate the final image by an image generator. To decouple the shape and style of clothing, we propose joint global and local per-region encoding and normalization to predict the reasonable style of clothing for invisible regions. We also propose spatial-aware normalization to retain the spatial context relationship in the source image. The results of qualitative and quantitative experiments demonstrate the superiority of our model on human pose transfer. Besides, the results of texture transfer and region editing show that our model can be applied to person image editing. The code is available for research purposes at https://***/Zhangjinso/PISE.

关键词： computer vision Image coding Shape Image synthesis Clothing Semantics Layout

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Recurrent Scene Parsing with Perspective Understanding in the Loop 31

Recurrent Scene Parsing with Perspective Understanding in th...

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

作者： Kong, Shu Fowlkes, Charless Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA

ISBN: (纸本)9781538664209

Objects may appear at arbitrary scales in perspective images of a scene, posing a challenge for recognition systems that process images at a fixed resolution. We propose a depth-aware gating module that adaptively selects the pooling field size in a convolutional network architecture according to the object scale (inversely proportional to the depth) so that small details are preserved for distant objects while larger receptive fields are used for those nearby. The depth gating signal is provided by stereo disparity or estimated directly from monocular input. We integrate this depth-aware gating into a recurrent convolutional neural network to perform semantic segmentation. Our recurrent module iteratively refines the segmentation results, leveraging the depth and semantic predictions from the previous iterations. Through extensive experiments on four popular large-scale datasets, we demonstrate this approach achieves competitive semantic segmentation performance with a model which is substantially more compact. We carry out extensive analysis of this architecture including variants that operate on monocular RGB but use depth as side-information during training, unsupervised gating as a generic attentional mechanism, and multi-resolution gating. We find that gated pooling for joint semantic segmentation and depth yields state-of-the-art results for quantitative monocular depth estimation.

关键词： Semantics Image segmentation computer architecture Training Task analysis Computational modeling Convolution

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