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

共 22908 条记录，以下是4401-4410 订阅

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Siamese Natural Language Tracker: Tracking by Natural Language Descriptions with Siamese Trackers

Siamese Natural Language Tracker: Tracking by Natural Langua...

引用

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

作者： Feng, Qi Ablavsky, Vitaly Bai, Qinxun Sclaroff, Stan Boston Univ Boston MA 02215 USA Univ Washington Seattle WA 98195 USA Horizon Robot Beijing Peoples R China

ISBN: (纸本)9781665445092

We propose a novel Siamese Natural Language Tracker (SNLT), which brings the advancements in visual tracking to the tracking by natural language (NL) descriptions task. The proposed SNLT is applicable to a wide range of Siamese trackers, providing a new class of baselines for the tracking by NL task and promising future improvements from the advancements of Siamese trackers. The carefully designed architecture of the Siamese Natural Language Region Proposal Network (SNL-RPN), together with the Dynamic Aggregation of vision and language modalities, is introduced to perform the tracking by NL task. Empirical results over tracking benchmarks with NL annotations show that the proposed SNLT improves Siamese trackers by 3 to 7 percentage points with a slight tradeoff of speed. The proposed SNLT outperforms all NL trackers to-date and is competitive among state-of-the-art real-time trackers on LaSOT benchmarks while running at 50 frames per second on a single GPU. Code for this work is available at https: // github. com/ fredfung007/ snlt.

关键词： Visualization computer vision Natural languages Graphics processing units computer architecture Benchmark testing Real-time systems

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NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections

NeRF in the Wild: Neural Radiance Fields for Unconstrained P...

引用

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

作者： Martin-Brualla, Ricardo Radwan, Noha Sajjadi, Mehdi S. M. Barron, Jonathan T. Dosovitskiy, Alexey Duckworth, Daniel Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

We present a learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs. We build on Neural Radiance Fields (NeRF), which uses the weights of a multi-layer perceptron to model the density and color of a scene as a function of 3D coordinates. While NeRF works well on images of static subjects captured under controlled settings, it is incapable of modeling many ubiquitous, real-world phenomena in uncontrolled images, such as variable illumination or transient occluders. We introduce a series of extensions to NeRF to address these issues, thereby enabling accurate reconstructions from unstructured image collections taken from the internet. We apply our system, dubbed NeRF-W, to internet photo collections of famous landmarks, and demonstrate temporally consistent novel view renderings that are significantly closer to photorealism than the prior state of the art.

关键词： Learning systems Solid modeling Photorealism Three-dimensional displays Image color analysis Lighting Rendering (computer graphics)

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Sparse Multi-Path Corrections in Fringe Projection Profilometry

Sparse Multi-Path Corrections in Fringe Projection Profilome...

引用

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

作者： Zhang, Yu Lau, Daniel Wipf, David Nanjing Univ Nanjing Peoples R China Univ Kentucky Lexington KY 40506 USA Amazon Seattle WA USA

ISBN: (纸本)9781665445092

Three-dimensional scanning by means of structured light illumination is an active imaging technique involving projecting and capturing a series of striped patterns and then using the observed warping of stripes to reconstruct the target object's surface through triangulating each pixel in the camera to a unique projector coordinate corresponding to a particular feature in the projected patterns. The undesirable phenomenon of multi-path occurs when a camera pixel simultaneously sees features from multiple projector coordinates. Bimodal multi-path is a particularly common situation found along step edges, where the camera pixel sees both a foreground and background surface. Generalized from bimodal multi-path, this paper examines the phenomenon of sparse or N-modal multi-path as a more general case, where the camera pixel sees no fewer than two reflective surfaces, resulting in decoding errors. Using fringe projection profilometry, our proposed solution is to treat each camera pixel as an underdetermined linear system of equations and to find the sparsest (least number of paths) solution by taking an application-specific Bayesian learning approach. We validate this algorithm with both simulations and a number of challenging real-world scenarios, demonstrating that it outperforms state-of-the-art techniques.

关键词： Linear systems Surface reconstruction computer vision Lighting Cameras Mathematical models pattern recognition

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Towards Bridging Event Captioner and Sentence Localizer for Weakly Supervised Dense Event Captioning

Towards Bridging Event Captioner and Sentence Localizer for ...

引用

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

作者： Chen, Shaoxiang Jiang, Yu-Gang Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China

ISBN: (纸本)9781665445092

Dense Event Captioning (DEC) aims to jointly localize and describe multiple events of interest in untrimmed videos, which is an advancement of the conventional video captioning task (generating a single sentence description for a trimmed video). Weakly Supervised Dense Event Captioning (WS-DEC) goes one step further by not relying on human-annotated temporal event boundaries. However, there are few methods trying to tackle this task, and how to connect localization and description remains an open problem. In this paper, we demonstrate that under weak supervision, the event captioning module and localization module should be more closely bridged in order to improve description performance. Different from previous approaches, in our method, the event captioner generates a sentence from a video segment and feeds it to the sentence localizer to reconstruct the segment, and the localizer produces word importance weights as a guidance for the captioner to improve event description. To further bridge the sentence localizer and event captioner, a concept learner is adopted as the basis of the sentence localizer, which can be utilized to construct an induced set of concept features to enhance video features and improve the event captioner. Finally, our proposed method outperforms state-of-the-art WS-DEC methods on the ActivityNet Captions dataset.

关键词： Location awareness Bridges computer vision Training data pattern recognition Feeds Task analysis

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SurFree: a fast surrogate-free black-box attack

SurFree: a fast surrogate-free black-box attack

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

作者： Maho, Thibault Furon, Teddy Le Merrer, Erwan Univ Rennes CNRS INRIA IRISA Rennes France

ISBN: (纸本)9781665445092

Machine learning classifiers are critically prone to evasion attacks. Adversarial examples are slightly modified inputs that are then misclassified, while remaining perceptively close to their originals. Last couple of years have witnessed a striking decrease in the amount of queries a black box attack submits to the target classifier, in order to forge adversarials. This particularly concerns the black box score-based setup, where the attacker has access to top predicted probabilites: the amount of queries went from to millions of to less than a thousand. This paper presents SurFree, a geometrical approach that achieves a drastic reduction in the amount of queries in the hardest setup: black box decision-based attacks (only the top-1 label is available). We first highlight that the most recent attacks in that setup, HSJA [3], QEBA [14] and GeoDA [23] all perform costly gradient surrogate estimations. SurFree proposes to bypass these, by instead focusing on careful trials along diverse directions, guided by precise indications of geometrical properties of the classifier decision boundaries. We motivate this geometric approach before performing a head-to-head comparison with previous attacks with the amount of queries as a first class citizen. We exhibit a faster distortion decay under low query amounts (few hundreds to a thousand), while remaining competitive at higher query budgets.(1)

关键词： computer vision Estimation Focusing Machine learning Distortion pattern recognition Convergence

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Blind Deblurring for Saturated Images

Blind Deblurring for Saturated Images

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

作者： Chen, Liang Zhang, Jiawei Lin, Songnan Fang, Faming Ren, Jimmy S. East China Normal Univ Proc Sch Comp Sci & Technol Shanghai Key Lab Multidimens Informat Shanghai Peoples R China SenseTime Res Shanghai Peoples R China Shanghai Jiao Tong Univ Qing Yuan Res Inst Shanghai Peoples R China

ISBN: (纸本)9781665445092

Blind deblurring has received considerable attention in recent years. However, state-of-the-art methods often fail to process saturated blurry images. The main reason is that pixels around saturated regions are not conforming to the commonly used linear blur model. Pioneer arts suggest excluding these pixels during the deblurring process, which sometimes simultaneously removes the informative edges around saturated regions and results in insufficient information for kernel estimation when large saturated regions exist. To address this problem, we introduce a new blur model to fit both saturated and unsaturated pixels, and all informative pixels can be considered during the deblurring process. Based on our model, we develop an effective maximum a posterior (MAP)-based optimization framework. Quantitative and qualitative evaluations on benchmark datasets and challenging real-world examples show that the proposed method performs favorably against existing methods.

关键词： computer vision Art Image edge detection Estimation Benchmark testing pattern recognition Image restoration

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Mask Guided Matting via Progressive Refinement Network

Mask Guided Matting via Progressive Refinement Network

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

作者： Yu, Qihang Zhang, Jianming Zhang, He Wang, Yilin Lin, Zhe Xu, Ning Bai, Yutong Yuille, Alan Johns Hopkins Univ Baltimore MD 21218 USA Adobe San Jose CA USA

ISBN: (纸本)9781665445092

We propose Mask Guided (MG) Matting, a robust matting framework that takes a general coarse mask as guidance. MG Matting leverages a network (PRN) design which encourages the matting model to provide self-guidance to progressively refine the uncertain regions through the decoding process. A series of guidance mask perturbation operations are also introduced in the training to further enhance its robustness to external guidance. We show that PRN can generalize to unseen types of guidance masks such as trimap and low-quality alpha matte, making it suitable for various application pipelines. In addition, we revisit the foreground color prediction problem for matting and propose a surprisingly simple improvement to address the dataset issue. Evaluation on real and synthetic benchmarks shows that MG Matting achieves state-of-the-art performance using various types of guidance inputs.

关键词： Training Image segmentation Image color analysis Computational modeling Perturbation methods Pipelines Rendering (computer graphics)

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Rich Context Aggregation with Reflection Prior for Glass Surface Detection

Rich Context Aggregation with Reflection Prior for Glass Sur...

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

作者： Lin, Jiaying He, Zebang Lau, Rynson W. H. City Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Glass surfaces appear everywhere. Their existence can however pose a serious problem to computer vision tasks. Recently, a method is proposed to detect glass surfaces by learning multi-scale contextual information. However, as it is only based on a general context integration operation and does not consider any specific glass surface properties, it gets confused when the images contain objects that are similar to glass surfaces and degenerates in challenging scenes with insufficient contexts. We observe that humans often rely on identifying reflections in order to sense the existence of glass and on locating the boundary in order to determine the extent of the glass. Hence, we propose a model for glass surface detection, which consists of two novel modules: (1) a rich context aggregation module (RCAM) to extract multi-scale boundary features from rich context features for locating glass surface boundaries of different sizes and shapes, and (2) a reflection-based refinement module (RRM) to detect reflection and then incorporate it so as to differentiate glass regions from non-glass regions. In addition, we also propose a challenging dataset consisting of 4,012 glass images with annotations for glass surface detection. Our experiments demonstrate that the proposed model outperforms state-of-the-art methods from relevant fields.

关键词： computer vision Shape Annotations Computational modeling Glass Feature extraction Reflection

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DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

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

作者： Rozumnyi, Denys Oswald, Martin R. Ferrari, Vittorio Matas, Jiri Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Google Res Mountain View CA 94043 USA Microsoft Mixed Real & AI Zurich Lab Zurich Switzerland Czech Tech Univ Visual Recognit Grp Prague Czech Republic

ISBN: (纸本)9781665445092

Objects moving at high speed appear significantly blurred when captured with cameras. The blurry appearance is especially ambiguous when the object has complex shape or texture. In such cases, classical methods, or even humans, are unable to recover the object's appearance and motion. We propose a method that, given a single image with its estimated background, outputs the object's appearance and position in a series of sub frames as if captured by a high-speed camera (i.e. temporal super-resolution). The proposed generative model embeds an image of the blurred object into a latent space representation, disentangles the background, and renders the sharp appearance. Inspired by the image formation model, we design novel self-supervised loss function terms that boost performance and show good generalization capabilities. The proposed DeFMO method is trained on a complex synthetic dataset, yet it performs well on real-world data from several datasets. DeFMO outperforms the state of the art and generates high-quality temporal super-resolution frames.

关键词： Training computer vision Shape Computational modeling Superresolution Cameras pattern recognition

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CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning

CReST: A Class-Rebalancing Self-Training Framework for Imbal...

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

作者： Wei, Chen Sohn, Kihyuk Mellina, Clayton Yuille, Alan Yang, Fan Johns Hopkins Univ Baltimore MD 21218 USA Google Cloud AI Mountain View CA USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Semi-supervised learning on class-imbalanced data, although a realistic problem, has been under studied. While existing semi-supervised learning (SSL) methods are known to perform poorly on minority classes, we find that they still generate high precision pseudo-labels on minority classes. By exploiting this property, in this work, we propose Class-Rebalancing Self-Training (CReST), a simple yet effective framework to improve existing SSL methods on class-imbalanced data. CReST iteratively retrains a baseline SSL model with a labeled set expanded by adding pseudo labeled samples from an unlabeled set, where pseudo labeled samples from minority classes are selected more frequently according to an estimated class distribution. We also propose a progressive distribution alignment to adaptively adjust the rebalancing strength dubbed CReST+. We show that CReST and CReST+ improve state-of-the-art SSL algorithms on various class-imbalanced datasets and consistently outperform other popular rebalancing methods. Code has been made available at ht-sps//***/google-research/crest.

关键词： computer vision Adaptation models Codes Semisupervised learning pattern recognition

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