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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是331-340 订阅

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Fast Video Classification via Adaptive Cascading of Deep Models 30

Fast Video Classification via Adaptive Cascading of Deep Mod...

引用

30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Shen, Haichen Han, Seungyeop Philipose, Matthai Krishnamurthy, Arvind Univ Washington Seattle WA 98195 USA Rubrik Inc Palo Alto CA USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781538604571

Recent advances have enabled "oracle" classifiers that can classify across many classes and input distributions with high accuracy without retraining. However, these classifiers are relatively heavyweight, so that applying them to classify video is costly. We show that day-to-day video exhibits highly skewed class distributions over the short term, and that these distributions can be classified by much simpler models. We formulate the problem of detecting the short-term skews online and exploiting models based on it as a new sequential decision making problem dubbed the Online Bandit Problem, and present a new algorithm to solve it. When applied to recognizing faces in TV shows and movies, we realize end-toend classification speedups of 2.4-7.8x/2.6-11.2x (on GPU/CPU) relative to a state-of-the-art convolutional neural network, at competitive accuracy.

关键词： Face recognition Training Motion pictures Adaptation models Neural networks Cameras

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Superpixel-based Tracking-by-Segmentation using Markov Chains 30

Superpixel-based Tracking-by-Segmentation using Markov Chain...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yeo, Donghun Son, Jeany Han, Bohyung Han, Joon Hee POSTECH Dept Comp Sci & Engn Pohang South Korea

ISBN: (纸本)9781538604571

We propose a simple but effective tracking-by-segmentation algorithm using Absorbing Markov Chain (AMC) on superpixel segmentation, where target state is estimated by a combination of bottom-up and top-down approaches, and target segmentation is propagated to subsequent frames in a recursive manner. Our algorithm constructs a graph for AMC using the superpixels identified in two consecutive frames, where background superpixels in the previous frame correspond to absorbing vertices while all other superpixels create transient ones. the weight of each edge depends on the similarity of scores in the end superpixels, which are learned by support vector regression. Once graph construction is completed, target segmentation is estimated using the absorption time of each superpixel. the proposed tracking algorithm achieves substantially improved performance compared to the state-of-the-art segmentation-based tracking techniques in multiple challenging datasets.

关键词： Target tracking Transient analysis Absorption computer vision Markov processes Image segmentation Support vector machines

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Semi-Supervised Deep Learning for Monocular Depth Map Prediction 30

Semi-Supervised Deep Learning for Monocular Depth Map Predic...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kuznietsov, Yevhen Stuckle, Jorg Leibe, Bastian Rhein Westfal TH Aachen Visual Comp Inst Comp Vis Grp Aachen Germany

ISBN: (纸本)9781538604571

Supervised deep learning often suffers from the lack of sufficient training data. Specifically in the context of monocular depth map prediction, it is barely possible to determine dense ground truth depth images in realistic dynamic outdoor environments. When using LiDAR sensors, for instance, noise is present in the distance measurements, the calibration between sensors cannot be perfect, and the measurements are typically much sparser than the camera images. In this paper, we propose a novel approach to depth map prediction from monocular images that learns in a semi-supervised way. While we use sparse ground-truth depth for supervised learning, we also enforce our deep network to produce photoconsistent dense depth maps in a stereo setup using a direct image alignment loss. In experiments we demonstrate superior performance in depth map prediction from single images compared to the state-of-the-art methods.

关键词： MAPPING(MAthEMATICAL) maps Semi-supervised Learning OUTDOOR ENVIRONMENT ground truth Sensors Perfect images distance measurement

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Deep Semantic Feature Matching 30

Deep Semantic Feature Matching

引用

30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ufer, Nikolai Ommer, Bjoern Heidelberg Univ Heidelberg Collaboratory Image Proc IWR Heidelberg Germany

ISBN: (纸本)9781538604571

Estimating dense visual correspondences between objects with intra-class variation, deformations and background clutter remains a challenging problem. thanks to the breakthrough of CNNs there are new powerful features available. Despite their easy accessibility and great success, existing semantic flow methods could not significantly benefit from these without extensive additional training. We introduce a novel method for semantic matching with pre-trained CNN features which is based on convolutional feature pyramids and activation guided feature selection. For the final matching we propose a sparse graph matching framework where each salient feature selects among a small subset of nearest neighbors in the target image. To improve our method in the unconstrained setting without bounding box annotations we introduce novel object proposal based matching constraints. Furthermore, we show that the sparse matching can be transformed into a dense correspondence field. Extensive experimental evaluations on benchmark datasets show that our method significantly outperforms existing semantic matching methods.

关键词： Feature extraction Semantics Proposals computer vision Strain Entropy

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Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields 30

Realtime Multi-Person 2D Pose Estimation using Part Affinity...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Cao, Zhe Simon, Tomas Wei, Shih-En Sheikh, Yaser Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA

ISBN: (纸本)9781538604571

We present an approach to efficiently detect the 2D pose of multiple people in an image. the approach uses a non-parametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. the architecture encodes global context, allowing a greedy bottom-up parsing step that maintains high accuracy while achieving realtime performance, irrespective of the number of people in the image. the architecture is designed to jointly learn part locations and their association via two branches of the same sequential prediction process. Our method placed first in the inaugural COCO 2016 keypoints challenge, and significantly exceeds the previous state-of-the-art result on the MPII Multi-Person benchmark, both in performance and efficiency.

关键词： Two dimensional displays Pose estimation Runtime Detectors Training computer architecture Benchmark testing pose estimation Benchmark testing computer Architecture Runtime Functional training detector Parsing Training Body Parts

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A clever elimination strategy for efficient minimal solvers 30

A clever elimination strategy for efficient minimal solvers

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kukelova, Zuzana Kileel, Joe Sturmfels, Bernd Pajdla, Tomas Czech Tech Univ Fac Elect Engn Prague Czech Republic Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781538604571

We present a new insight into the systematic generation of minimal solvers in computer vision, which leads to smaller and faster solvers. Many minimal problem formulations are coupled sets of linear and polynomial equations where image measurements enter the linear equations only. We show that it is useful to solve such systems by first eliminating all the unknowns that do not appear in the linear equations and then extending solutions to the rest of unknowns. this can be generalized to fully non-linear systems by linearization via lifting. We demonstrate that this approach leads to more efficient solvers in three problems of partially calibrated relative camera pose computation with unknown focal length and/or radial distortion. Our approach also generates new interesting constraints on the fundamental matrices of partially calibrated cameras, which were not known before.

关键词： computer vision Mathematical model Cameras Generators Systematics Geometry Standards

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Spatially Adaptive Computation Time for Residual Networks 30

Spatially Adaptive Computation Time for Residual Networks

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Figurnov, Michael Collins, Maxwell D. Zhu, Yukun Zhang, Li Huang, Jonathan Vetrov, Dmitry Salakhutdinov, Ruslan Natl Res Univ Higher Sch Econ Moscow Russia Google Inc Mountain View CA USA Yandex Moscow Russia Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781538604571

this paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image. this architecture is end-to-end trainable, deterministic and problem-agnostic. It is therefore applicable without any modifications to a wide range of computer vision problems such as image classification, object detection and image segmentation. We present experimental results showing that this model improves the computational efficiency of Residual Networks on the challenging ImageNet classification and COCO object detection datasets. Additionally, we evaluate the computation time maps on the visual saliency dataset cat2000 and find that they correlate surprisingly well with human eye fixation positions.

关键词： computational time images Adaptive computation Computational efficiency computer vision image segmentation Object detection RESIDUAL Ocular Fixation Dataset Network ARCHITECTURE Building construction

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GuessWhat?! Visual object discovery through multi-modal dialogue 30

GuessWhat?! Visual object discovery through multi-modal dial...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： de Vries, Harm Strub, Florian Chandar, Sarath Pietquin, Olivier Larochelle, Hugo Courville, Aaron Univ Montreal Montreal PQ Canada Univ Lille CNRS Cent Lille InriaUMR 9189CRIStAL Villeneuve Dascq France DeepMind London England Twitter San Francisco CA USA

ISBN: (纸本)9781538604571

We introduce GuessWhat?!, a two-player guessing game as a testbed for research on the interplay of computer vision and dialogue systems. the goal of the game is to locate an unknown object in a rich image scene by asking a sequence of questions. Higher-level image understanding, like spatial reasoning and language grounding, is required to solve the proposed task. Our key contribution is the collection of a large-scale dataset consisting of 150K human-played games with a total of 800K visual question-answer pairs on 66K images. We explain our design decisions in collecting the dataset and introduce the oracle and questioner tasks that are associated with the two players of the game. We prototyped deep learning models to establish initial baselines of the introduced tasks.

关键词： Games Visualization Natural languages Databases computer vision Knowledge discovery

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Zero-Shot Classification with Discriminative Semantic Representation Learning 30

Zero-Shot Classification with Discriminative Semantic Repres...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ye, Meng Guo, Yuhong Temple Univ Comp & Informat Sci Philadelphia PA 19122 USA Carleton Univ Sch Comp Sci Ottawa ON Canada

ISBN: (纸本)9781538604571

Zero-shot learning, a special case of unsupervised domain adaptation where the source and target domains have disjoint label spaces, has become increasingly popular in the computer vision community. In this paper, we propose a novel zero-shot learning method based on discriminative sparse non-negative matrix factorization. the proposed approach aims to identify a set of common high-level semantic components across the two domains via non-negative sparse matrix factorization, while enforcing the representation vectors of the images in this common component-based space to be discriminatively aligned with the attributebased label representation vectors. To fully exploit the aligned semantic information contained in the learned representation vectors of the instances, we develop a label propagation based testing procedure to classify the unlabeled instances from the unseen classes in the target domain. We conduct experiments on four standard zero-shot learning image datasets, by comparing the proposed approach to the state-of-the-art zero-shot learning methods. the empirical results demonstrate the efficacy of the proposed approach.

关键词： images Semantics matrix decomposition computer vision Learning methods Testing Procedures Nonnegative

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Cognitive Mapping and Planning for Visual Navigation 30

Cognitive Mapping and Planning for Visual Navigation

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Gupta, Saurabh Davidson, James Levine, Sergey Sukthankar, Rahul Malik, Jitendra Univ Calif Berkeley Berkeley CA USA Google Mountain View CA USA

ISBN: (纸本)9781538604571

We introduce a neural architecture for navigation in novel environments. Our proposed architecture learns to map from first-person views and plans a sequence of actions towards goals in the environment. the Cognitive Mapper and Planner (CMP) is based on two key ideas: a) a unified joint architecture for mapping and planning, such that the mapping is driven by the needs of the planner, and b) a spatial memory with the ability to plan given an incomplete set of observations about the world. CMP constructs a top-down belief map of the world and applies a differentiable neural net planner to produce the next action at each time step. the accumulated belief of the world enables the agent to track visited regions of the environment. Our experiments demonstrate that CMP outperforms both reactive strategies and standard memory-based architectures and performs well in novel environments. Furthermore, we show that CMP can also achieve semantically specified goals, such as "go to a chair".

关键词： vision Navigation Basic metabolic panel cytidine monophosphate Chemical mechanical polishing MAPPING(MAthEMATICAL) Environments Cognitive Maps Planning ARCHITECTURE navigation Building construction

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