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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4741-4750 订阅

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DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates

DeepMetaHandles: Learning Deformation Meta-Handles of 3D Mes...

引用

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

作者： Liu, Minghua Sung, Minhyuk Mech, Radomir Su, Hao Univ Calif San Diego San Diego CA 92103 USA Korea Adv Inst Sci & Technol Daejeon South Korea Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

We propose DeepMetaHandles, a 3D conditional generative model based on mesh deformation. Given a collection of 3D meshes of a category and their deformation handles (control points), our method learns a set of metahandles for each shape, which are represented as combinations of the given handles. The disentangled meta-handles factorize all the plausible deformations of the shape, while each of them corresponds to an intuitive deformation. A new deformation can then be generated by sampling the coefficients of the meta-handles in a specific range. We employ biharmonic coordinates as the deformation function, which can smoothly propagate the control points' translations to the entire mesh. To avoid learning zero deformation as meta-handles, we incorporate a target-fitting module which deforms the input mesh to match a random target. To enhance deformations' plausibility, we employ a soft-rasterizer-based discriminator that projects the meshes to a 2D space. Our experiments demonstrate the superiority of the generated deformations as well as the interpretability and consistency of the learned meta-handles.

关键词： Deformable models Training Solid modeling computer vision Three-dimensional displays Shape Impedance matching

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HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms

HistoGAN: Controlling Colors of GAN-Generated and Real Image...

引用

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

作者： Afifi, Mahmoud Brubaker, Marcus A. Brown, Michael S. York Univ N York ON Canada

ISBN: (纸本)9781665445092

While generative adversarial networks (GANs) can successfully produce high-quality images, they can be challenging to control. Simplifying GAN-based image generation is critical for their adoption in graphic design and artistic work. This goal has led to significant interest in methods that can intuitively control the appearance of images generated by GANs. In this paper, we present HistoGAN, a color histogram-based method for controlling GAN-generated images' colors. We focus on color histograms as they provide an intuitive way to describe image color while remaining decoupled from domain-specific semantics. Specifically, we introduce an effective modifi- cation of the recent StyleGAN architecture [31] to control the colors of GAN-generated images specified by a target color histogram feature. We then describe how to expand HistoGAN to recolor real images. For image recoloring, we jointly train an encoder network along with HistoGAN. The recoloring model, ReHistoGAN, is an unsupervised approach trained to encourage the network to keep the original image's content while changing the colors based on the given target histogram. We show that this histogram-based approach offers a better way to control GAN-generated and real images' colors while producing more compelling results compared to existing alternative strategies.

关键词： Graphics Histograms computer vision Image color analysis Image synthesis Semantics Generative adversarial networks

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Test of Time: Instilling Video-Language Models with a Sense of Time

Test of Time: Instilling Video-Language Models with a Sense ...

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

作者： Bagad, Piyush Tapaswi, Makarand Snoek, Cees G. M. Univ Amsterdam Amsterdam Netherlands IIIT Hyderabad Hyderabad India

ISBN: (纸本)9798350301298

Modelling and understanding time remains a challenge in contemporary video understanding models. With language emerging as a key driver towards powerful generalization, it is imperative for foundational video-language models to have a sense of time. In this paper, we consider a specific aspect of temporal understanding: consistency of time order as elicited by before/after relations. We establish that seven existing video-language models struggle to understand even such simple temporal relations. We then question whether it is feasible to equip these foundational models with temporal awareness without re-training them from scratch. Towards this, we propose a temporal adaptation recipe on top of one such model, VideoCLIP, based on post-pretraining on a small amount of video-text data. We conduct a zero-shot evaluation of the adapted models on six datasets for three downstream tasks which require varying degrees of time awareness. We observe encouraging performance gains especially when the task needs higher time awareness. Our work serves as a first step towards probing and instilling a sense of time in existing video-language models without the need for data and compute-intense training from scratch.

关键词： language reasoning vision

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Adversarial Feature Augmentation for Unsupervised Domain Adaptation 31

Adversarial Feature Augmentation for Unsupervised Domain Ada...

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

作者： Volpi, Riccardo Morerio, Pietro Savarese, Silvio Murino, Vittorio Ist Italiano Tecnol Pattern Anal & Comp Vis Genoa Italy Stanford Univ Stanford Vis & Learning Lab Stanford CA 94305 USA Univ Verona Comp Sci Dept Verona Italy

ISBN: (纸本)9781538664209

Recent works showed that Generative Adversarial Networks (GANs) can be successfully applied in unsupervised domain adaptation, where, given a labeled source dataset and an unlabeled target dataset, the goal is to train powerful classifiers for the target samples. In particular, it was shown that a GAN objective function can be used to learn target features indistinguishable from the source ones. In this work, we extend this framework by (i) forcing the learned feature extractor to be domain-invariant, and (ii) training it through data augmentation in the feature space, namely performing feature augmentation. While data augmentation in the image space is a well established technique in deep learning, feature augmentation has not yet received the same level of attention. We accomplish it by means of a feature generator trained by playing the GAN minimax game against source features. Results show that both enforcing domain-invariance and performing feature augmentation lead to superior or comparable performance to state-of-the-art results in several unsupervised domain adaptation benchmarks.

关键词： Feature extraction Gallium nitride Training Games Generative adversarial networks Generators Neural networks

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Visual inspection of machined parts

Visual inspection of machined parts

引用

1992 ieee computer society conference on computer vision and pattern recognition, CVPR 1992

作者： Modayur, B.R. Shapiro, L.G. Haralick, R.M. Department of Electrical Engineering FT -10 University of Washington SeattleWA98195 United States Department of Computer Science and Engineering FR-35 University of Washington SeattleWA98195 United States

ISBN: (纸本)0818628553

A CAD-model-based machine vision system for dimensional inspection of machine parts is described, with emphasis on the theory behind the system. The original contributions of this work are: (1) the use of precise definitions of geometric tolerances suitable for use in image processing, (2) the development of measurement algorithms corresponding directly to these definitions, (3) the derivation of the uncertainties in the measurement tasks, and (4) the use of this uncertainty information in the decision-making process. Initial experimental results have verified the uncertainty derivations statistically and proved that the error probabilities obtained by propagating uncertainties are lower than those obtainable without uncertainty propagation. © 1992 ieee.

关键词： Uncertainty analysis

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Scene Graph Generation with External Knowledge and Image Reconstruction 32

Scene Graph Generation with External Knowledge and Image Rec...

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

作者： Gu, Jiuxiang Zhao, Handong Lin, Zhe Li, Sheng Cai, Jianfei Ling, Mingyang Nanyang Technol Univ Interdisciplinary Grad Sch ROSE Lab Singapore Singapore Adobe Res San Jose CA USA Univ Georgia Athens GA 30602 USA Google Cloud AI San Jose CA USA

ISBN: (纸本)9781728132938

Scene graph generation has received growing attention with the advancements in image understanding tasks such as object detection, attributes and relationship prediction, etc. However, existing datasets are biased in terms of object and relationship labels, or often come with noisy and missing annotations, which makes the development of a reliable scene graph prediction model very challenging. In this paper, we propose a novel scene graph generation algorithm with external knowledge and image reconstruction loss to overcome these dataset issues. In particular, we extract commonsense knowledge from the external knowledge base to refine object and phrase features for improving generalizability in scene graph generation. To address the bias of noisy object annotations, we introduce an auxiliary image reconstruction path to regularize the scene graph generation network. Extensive experiments show that our framework can generate better scene graphs, achieving the state-of-the-art performance on two benchmark datasets: Visual Relationship Detection and Visual Genome datasets.

关键词： vision + Language Visual Reasoning

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Using Unknown Occluders to Recover Hidden Scenes 32

Using Unknown Occluders to Recover Hidden Scenes

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

作者： Yedidia, Adam B. Baradad, Manel Thrampoulidis, Christos Freeman, William T. Wornell, Gregory W. MIT Cambridge MA 02139 USA UC Santa Barbara Santa Barbara CA USA Google Res Mountain View CA USA

ISBN: (纸本)9781728132938

We consider the challenging problem of inferring a hidden moving scene from faint shadows cast on a diffuse surface. Recent work in passive non-line-of-sight (NLoS) imaging has shown that the presence of occluding objects in between the scene and the diffuse surface significantly improves the conditioning of the problem I I. However, that work assumes that the shape of the occluder is known a priori. In this paper, we relax this often impractical assumption, extending the range of applications for passive occluder-based NLoS imaging systems. We formulate the task of jointly recovering the unknown scene and unknown occluder as a blind deconvolution problem, for which we propose a simple but effective two-step algorithm. At the first step, the algorithm exploits motion in the scene in order to obtain an estimate of the occluder. In particular, it exploits the fact that motion in realistic scenes is typically sparse. The second step is more standard: using regularization, we deconvolve by the occluder estimate to solve for the hidden scene. We demonstrate the effectiveness of our method with simulations and experiments in a variety of settings.

关键词： Computational Photography Scene Analysis and Understanding vision Applications and Systems

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A-CNN: Annularly Convolutional Neural Networks on Point Clouds 32

A-CNN: Annularly Convolutional Neural Networks on Point Clou...

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

作者： Komarichev, Artem Zhong, Zichun Hua, Jing Wayne State Univ Dept Comp Sci Detroit MI 48202 USA

ISBN: (纸本)9781728132938

Analyzing the geometric and semantic properties of 3D point clouds through the deep networks is still challenging due to the irregularity and sparsity of samplings of their geometric structures. This paper presents a new method to define and compute convolution directly on 3D point clouds by the proposed annular convolution. This new convolution operator can better capture the local neighborhood geometry of each point by specifying the (regular and dilated) ring-shaped structures and directions in the computation. It can adapt to the geometric variability and scalability at the signal processing level. We apply it to the developed hierarchicalneural networks for object classification, part segmentation, and semantic segmentation in large-scale scenes. The extensive experiments and comparisons demonstrate that our approach outperforms the state-of-the-art methods on a variety of standard benchmark datasets (e.g., ModelNet10, ModelNet40, ShapeNetpart, S3DIS, and ScanNet).

关键词： Grouping and Shape Segmentation vision + Graphics

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NightLab: A Dual-level Architecture with Hardness Detection for Segmentation at Night

NightLab: A Dual-level Architecture with Hardness Detection ...

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

作者： Deng, Xueqing Wang, Peng Lian, Xiaochen Newsam, Shawn Univ Calif Merced EECS Merced CA 95343 USA ByteDance Inc Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The semantic segmentation of nighttime scenes is a challenging problem that is key to impactful applications like self-driving cars. Yet, it has received little attention compared to its daytime counterpart. In this paper, we propose NightLab, a novel nighttime segmentation framework that leverages multiple deep learning models imbued with night-aware features to yield State-of-The-Art (SoTA) performance on multiple night segmentation benchmarks. Notably, Night Lab contains models at two levels of granularity, i.e. image and regional, and each level is composed of light adaptation and segmentation modules. Given a nighttime image, the image level model provides an initial segmentation estimate while, in parallel, a hardness detection module identifies regions and their surrounding context that need further analysis. A regional level model focuses on these difficult regions to provide a significantly improved segmentation. All the models in NightLab are trained end-to-end using a set of proposed night-aware losses without handcrafted heuristics. Extensive experiments on the NightCity [44] and BDDIOOK [59] datasets show Night Lab achieves SoTA performance compared to concurrent methods. Code and dataset are available at https://***/xdeng7/NightLab.

关键词： Deep learning Image segmentation Adaptation models Visualization computer vision Image analysis Semantics

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Low-Resource vision Challenges for Foundation Models

Low-Resource Vision Challenges for Foundation Models

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

作者： Zhang, Yunhua Doughty, Hazel Snoek, Cees G. M. Univ Amsterdam Amsterdam Netherlands Leiden Univ Leiden Netherlands

ISBN: (纸本)9798350353006

Low-resource settings are well-established in natural language processing, where many languages lack sufficient data for deep learning at scale. However, low-resource problems are under-explored in computer vision. In this paper, we address this gap and explore the challenges of low-resource image tasks with vision foundation models. We first collect a benchmark of genuinely low-resource image data, covering historic maps, circuit diagrams, and mechanical drawings. These low-resource settings all share three challenges: data scarcity, fine-grained differences, and the distribution shift from natural images to the specialized domain of interest. While existing foundation models have shown impressive generalizability, we find they cannot transfer well to our low-resource tasks. To begin to tackle the challenges of low-resource vision, we introduce one simple baseline per challenge. Specifically, we i) enlarge the data space by generative models, ii) adopt the best sub-kernels to encode local regions for fine-grained difference discovery and iii) learn attention for specialized domains. Experiments on our three low-resource tasks demonstrate our proposals already provide a better baseline than transfer learning, data augmentation, and fine-grained methods. This highlights the unique characteristics and challenges of low-resource vision for foundation models that warrant further investigation. Project page: https://***/Low-Resource-vision/.

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