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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4751-4760 订阅

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ROR: Rejection of outliers by rotations in stereo matching

ROR: Rejection of outliers by rotations in stereo matching

引用

ieee conference on computer vision and pattern recognition (CVPR 2000)

作者： Adam, A Rivlin, E Shimshoni, I Technion Israel Inst Technol Dept Math IL-32000 Haifa Israel

ISBN: (纸本)0769506623

We address the problem of rejecting false matches of points between two perspective views. Even the best algorithms for image matching make some mistakes and output some false matches. We present an algorithm for identification of the false matches between the views. The algorithm exploits the possibility of rotating one of the images to achieve some common behaviour of the correct matches. Those matches that deviate from this common behaviour turn out to be false matches. The statistical tool we use is the Mean Shift Mode Estimator. Our algorithm does not use in any way the image characteristics of the matched features. In particular it avoids problems that cause the false matches in the first place. The algorithm may be run as a post processing step on output from any point matching algorithm. Use of the algorithm may significantly improve the ratio of correct matches to incorrect matches. On real images our algorithm has improved the percentage of correct matches from an initial 20%-30% to a final 70%-80%. For robust estimation algorithms which are later employed, this is a very desirable quality since it reduces significantly their computational cost. We present the algorithm, identify the conditions under which it works, and present results of testing it on both synthetic anf real images.

关键词： pattern matching

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Region-tree based stereo using dynamic programming optimization

Region-tree based stereo using dynamic programming optimizat...

引用

2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Lei, Cheng Selzer, Jason Yang, Yee-Hong IEEE Department of Computing Science University of Alberta

ISBN: (纸本)0769525970

In this paper, we present a novel stereo algorithm that combines the strengths of region-based stereo and dynamic programming on a tree approaches. Instead of formulating an image as individual scan-lines or as a pixel tree, a new region tree structure, which is built as a minimum spanning tree on the adjacency-graph of an over-segmented image, is used for the global dynamic programming optimization. The resulting disparity maps do not contain any streaking problem as is common in scanline-based algorithms because of the tree structure. The performance evaluation using the Middlebury benchmark datasets shows that the performance of our algorithm is comparable in accuracy and efficiency with top ranking algorithms. © 2006 ieee.

关键词： Stereo vision

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Interact before Align: Leveraging Cross-Modal Knowledge for Domain Adaptive Action recognition

Interact before Align: Leveraging Cross-Modal Knowledge for ...

引用

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

作者： Yang, Lijin Huang, Yifei Sugano, Yusuke Sato, Yoichi Univ Tokyo Inst Ind Sci Tokyo Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Unsupervised domain adaptive video action recognition aims to recognize actions of a target domain using a model trained with only out-of-domain (source) annotations. The inherent complexity of videos makes this task challenging but also provides ground for leveraging multi-modal inputs (e.g., RGB, Flow, Audio). Most previous works utilize the multi-modal information by either aligning each modality individually or learning representation via cross-modal self-supervision. Different from previous works, we find that the cross-domain alignment can be more effectively done by using cross-modal interaction first. Cross-modal knowledge interaction allows other modalities to supplement missing transferable information because of the cross-modal complementarity. Also, the most transferable aspects of data can be highlighted using cross-modal consensus. In this work, we present a novel model that jointly considers these two characteristics for domain adaptive action recognition. We achieve this by implementing two modules, where the first module exchanges complementary transferable information across modalities through the semantic space, and the second module finds the most transferable spatial region based on the consensus of all modalities. Extensive experiments validate that our proposed method can significantly outperform state-of-the-art methods on multiple benchmark datasets, including the complex fine-grained dataset EPIC-Kitchens-100.

关键词： Adaptation models computer vision Target recognition Annotations Semantics Benchmark testing Data models

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Pseudo Facial Generation with Extreme Poses for Face recognition

Pseudo Facial Generation with Extreme Poses for Face Recogni...

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

作者： Wang, Guoli Ma, Jiaqi Zhang, Qian Lu, Jiwen Zhou, Jie Tsinghua Univ Beijing Peoples R China Wuhan Univ Wuhan Peoples R China Horizon Robot Beijing Peoples R China

ISBN: (纸本)9781665445092

Face recognition has achieved a great success in recent years, it is still challenging to recognize those facial images with extreme poses. Traditional methods consider it as a domain gap problem. Many of them settle it by generating fake frontal faces from extreme ones, whereas they are tough to maintain the identity information with high computational consumption and uncontrolled disturbances. Our experimental analysis shows a dramatic precision drop with extreme poses. Meanwhile, those extreme poses just exist minor visual differences after small rotations. Derived from this insight, we attempt to relieve such a huge precision drop by making minor changes to the input images without modifying existing discriminators. A novel lightweight pseudo facial generation is proposed to relieve the problem of extreme poses without generating any frontal facial image. It can depict the facial contour information and make appropriate modifications to preserve the critical identity information. Specifically, the proposed method reconstructs pseudo profile faces by minimizing the pixel-wise differences with original profile faces and maintaining the identity consistent information from their corresponding frontal faces simultaneously. The proposed framework can improve existing discriminators and obtain a great promotion on several benchmark datasets.

关键词： Visualization computer vision Image recognition Face recognition Computational modeling Benchmark testing Generators

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Integrating Language Guidance into vision-based Deep Metric Learning

Integrating Language Guidance into Vision-based Deep Metric ...

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

作者： Roth, Karsten Vinyals, Oriol Akata, Zeynep Univ Tubingen Tubingen Germany DeepMind London England MPI Intelligent Syst Stuttgart Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Deep Metric Learning (DML) proposes to learn metric spaces which encode semantic similarities as embedding space distances. These spaces should be transferable to classes beyond those seen during training. Commonly, DML methods task networks to solve contrastive ranking tasks defined over binary class assignments. However, such approaches ignore higher-level semantic relations between the actual classes. This causes learned embedding spaces to encode incomplete semantic context and misrepresent the semantic relation between classes, impacting the generalizability of the learned metric space. To tackle this issue, we propose a language guidance objective for visual similarity learning. Leveraging language embeddings of expert- and pseudo-classnames, we contextualize and realign visual representation spaces corresponding to meaningful language semantics for better semantic consistency. Extensive experiments and ablations provide a strong motivation for our proposed approach and show language guidance offering significant, model-agnostic improvements for DML, achieving competitive and state-ofthe-art results on all benchmarks.

关键词： Training Learning systems Visualization Semantics Benchmark testing Extraterrestrial measurements pattern recognition

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GeoNeRF: Generalizing NeRF with Geometry Priors

GeoNeRF: Generalizing NeRF with Geometry Priors

引用

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

作者： Johari, Mohammad Mahdi Lepoittevin, Yann Fleuret, Francois Ecole Polytech Fed Lausanne Idiap Res Inst Lausanne Switzerland Ams OSRAM Premstatten Austria Univ Geneva EPFL Geneva Switzerland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present GeoNeRF, a generalizable photorealistic novel view synthesis method based on neural radiance fields. Our approach consists of two main stages: a geometry reasoner and a renderer. To render a novel view, the geometry reasoner first constructs cascaded cost volumes for each nearby source view. Then, using a Transformer-based attention mechanism and the cascaded cost volumes, the renderer infers geometry and appearance, and renders detailed images via classical volume rendering techniques. This architecture, in particular, allows sophisticated occlusion reasoning, gathering information from consistent source views. Moreover, our method can easily be fine-tuned on a single scene, and renders competitive results with per-scene optimized neural rendering methods with a fraction of computational cost. Experiments show that GeoNeRF outperforms state-of-the-art generalizable neural rendering models on various synthetic and real datasets. Lastly, with a slight modification to the geometry reasoner, we also propose an alternative model that adapts to RGBD images. This model directly exploits the depth information often available thanks to depth sensors.

关键词： Geometry Image sensors Adaptation models Costs Three-dimensional displays Computational modeling computer architecture

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Wide Compression: Tensor Ring Nets 31

Wide Compression: Tensor Ring Nets

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

作者： Wang, Wenqi Sun, Yifan Eriksson, Brian Wang, Wenlin Aggarwal, Vaneet Purdue Univ W Lafayette IN 47907 USA Technicolor Res Los Altos CA USA Adobe Los Altos CA USA Duke Univ Durham NC 27706 USA

ISBN: (纸本)9781538664209

Deep neural networks have demonstrated state-of-the-art performance in a variety of real-world applications. In order to obtain performance gains, these networks have grown larger and deeper, containing millions or even billions of parameters and over a thousand layers. The tradeoff is that these large architectures require an enormous amount of memory, storage, and computation, thus limiting their usability. Inspired by the recent tensor ring factorization, we introduce Tensor Ring Networks (TR-Nets), which significantly compress both the fully connected layers and the convolutional layers of deep neural networks. Our results show that our TR-Nets approach is able to compress LeNet-5 by 11x without losing accuracy, and can compress the state-of-the-art Wide ResNet by 243x with only 2.3% degradation in Cifar10 image classification. Overall, this compression scheme shows promise in scientific computing and deep learning, especially for emerging resource-constrained devices such as smartphones, wearables, and IoT devices.

关键词： Neural networks Image coding Shape Merging computer architecture

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Using eye region biometrics to reveal affective and cognitive states

Using eye region biometrics to reveal affective and cognitiv...

引用

2004 ieee computer society conference on computer vision and pattern recognition Workshops, CVPRW 2004

作者： Heishman, Ric Duric, Zoran Wechsler, Harry George Mason University Department of Computer Science FairfaxVA22030 United States

Various facial region biometrics have been used extensively in the areas of recognition and authentication. However, some regions of the face provide more information than is currently being fully utilized in these specific capacities. Biometrics associated exclusively with the eye region hold a key to identifying and classifying particular affective and cognitive states. This paper focuses on 1) methods for identifying and deriving the appropriate biometric data inherent to the eye region that is most useful in specific HCI scenarios and, 2) outlining a framework for classification of these biometric data into affective and cognitive states relative to a particular HCI context. © 2004 ieee.

关键词： Biometrics

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Generalized Zero-Shot Learning via Synthesized Examples 31

Generalized Zero-Shot Learning via Synthesized Examples

引用

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

作者： Verma, Vinay Kumar Arora, Gundeep Mishra, Ashish Rai, Piyush Indian Inst Technol Kanpur Kanpur Uttar Pradesh India Indian Inst Technol Madras Chennai India

ISBN: (纸本)9781538664209

We present a generative framework for generalized zero shot learning where the training and test classes are not necessarily disjoint. Built upon a variational autoencoder based architecture, consisting of a probabilistic encoder and a probabilistic conditional decoder, our model can generate novel exemplars from seen/unseen classes, given their respective class attributes. These exemplars can subsequently be used to train any off-the-shelf classification model. One of the key aspects of our encoder-decoder architecture is a feedback-driven mechanism in which a discriminator (a multivariate regressor) learns to map the generated exemplars to the corresponding class attribute vectors, leading to an improved generator. Our model's ability to generate and leverage examples from unseen classes to train the classification model naturally helps to mitigate the bias towards predicting seen classes in generalized zero shot learning settings. Through a comprehensive set of experiments, we show that our model outperforms several state-of-the-art methods, on several benchmark datasets, for both standard as well as generalized zero-shot learning.

关键词： Generators Training Predictive models computer architecture Probabilistic logic Decoding Image reconstruction

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Dynamic Class Queue for Large Scale Face recognition In the Wild

Dynamic Class Queue for Large Scale Face Recognition In the ...

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

作者： Li, Bi Xi, Teng Zhang, Gang Feng, Haocheng Han, Junyu Liu, Jingtuo Ding, Errui Liu, Wenyu Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

Learning discriminative representation using large-scale face datasets in the wild is crucial for real-world applications, yet it remains challenging. The difficulties lie in many aspects and this work focus on computing resource constraint and long-tailed class distribution. Recently, classification-based representation learning with deep neural networks and well-designed losses have demonstrated good recognition performance. However, the computing and memory cost linearly scales up to the number of identities (classes) in the training set, and the learning process suffers from unbalanced classes. In this work, we propose a dynamic class queue (DCQ) to tackle these two problems. Specifically, for each iteration during training, a subset of classes for recognition are dynamically selected and their class weights are dynamically generated on-the-fly which are stored in a queue. Since only a subset of classes is selected for each iteration, the computing requirement is reduced. By using a single server without model parallel, we empirically verify in large-scale datasets that 10% of classes are sufficient to achieve similar performance as using all classes. Moreover, the class weights are dynamically generated in a few-shot manner and therefore suitable for tail classes with only a few instances. We show clear improvement over a strong baseline in the largest public dataset Megaface Challenge2 (MF2) which has 672K identities and over 88% of them have less than 10 instances. Code is available at https ://***/bilylee/DCQ

关键词： Training Deep learning computer vision Costs Codes Face recognition Computational modeling

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