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检索条件"任意字段=30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017"

共 212 条记录，以下是61-70 订阅

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Emotion recognition in Context 30

Emotion Recognition in Context

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

作者： Kosti, Ronak Alvarez, Jose M. Recasens, Adria Lapedriza, Agata Univ Oberta Catalunya Barcelona Spain CSIRO Data61 Canberra ACT Australia MIT Cambridge MA 02139 USA

ISBN: (纸本)9781538604571

Understanding what a person is experiencing from her frame of reference is essential in our everyday life. For this reason, one can think that machines with this type of ability would interact better with people. However, there are no current systems capable of understanding in detail people's emotional states. Previous research on computer vision to recognize emotions has mainly focused on analyzing the facial expression, usually classifying it into the 6 basic emotions [11]. However, the context plays an important role in emotion perception, and when the context is incorporated, we can infer more emotional states. In this paper we present the "Emotions in Context Database" (EMOTIC), a dataset of images containing people in context in non-controlled environments. In these images, people are annotated with 26 emotional categories and also with the continuous dimensions valence, arousal, and dominance [21]. With the EMOTIC dataset, we trained a Convolutional Neural Network model that jointly analyses the person and the whole scene to recognize rich information about emotional states. With this, we show the importance of considering the context for recognizing people's emotions in images, and provide a benchmark in the task of emotion recognition in visual context.

关键词： Emotion recognition Databases Face recognition Face computer vision Psychology Image recognition

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Annotating Object Instances with a Polygon-RNN 30

Annotating Object Instances with a Polygon-RNN

引用

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

作者： Castrejon, Lluis Kundu, Kaustav Urtasun, Raquel Fidler, Sanja Univ Toronto Dept Comp Sci Toronto ON Canada

ISBN: (纸本)9781538604571

We propose an approach for semi-automatic annotation of object instances. While most current methods treat object segmentation as a pixel-labeling problem, we here cast it as a polygon prediction task, mimicking how most current datasets have been annotated. In particular, our approach takes as input an image crop and sequentially produces vertices of the polygon outlining the object. this allows a human annotator to interfere at any time and correct a vertex if needed, producing as accurate segmentation as desired by the annotator. We show that our approach speeds up the annotation process by a factor of 4.7 across all classes in Cityscapes, while achieving 78.4% agreement in IoU with original ground-truth, matching the typical agreement between human annotators. For cars, our speed-up factor is 7.3 for an agreement of 82.2%. We further show generalization capabilities of our approach to unseen datasets.

关键词： Image segmentation Labeling computer architecture Agriculture Two dimensional displays Kernel computer Architecture image segmentation agriculture Kernel Object segmentation Dataset Seeds Polygon urban landscape Cars ANNOTATIONS

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Predicting Salient Face in Multiple-face Videos 30

Predicting Salient Face in Multiple-face Videos

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

作者： Liu, Yufan Zhang, Songyang Xu, Mai He, Xuming Beihang Univ Beijing Peoples R China ShanghaiTech Univ Shanghai Peoples R China

ISBN: (纸本)9781538604571

Although the recent success of convolutional neural network (CNN) advances state-of-the-art saliency prediction in static images, few work has addressed the problem of predicting attention in videos. On the other hand, we nd that the attention of different subjects consistently focuses on a single face in each frame of videos involving multiple faces. therefore, we propose in this paper a novel deep learning (DL) based method to predict salient face in multiple-face videos, which is capable of learning features and transition of salient faces across video frames. In particular, we rst learn a CNN for each frame to locate salient face. Taking CNN features as input, we develop a multiple-stream long short-term memory (M-LSTM) network to predict the temporal transition of salient faces in video sequences. To evaluate our DL-based method, we build a new eye-tracking database of multiple-face videos. the experimental results show that our method outperforms the prior state-of-the-art methods in predicting visual attention on faces in multipleface videos.

关键词： Face Videos conferences computer vision pattern recognition Machine learning Video sequences

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DeepPermNet: Visual Permutation Learning 30

DeepPermNet: Visual Permutation Learning

引用

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

作者： Cruz, Rodrigo Santa Fernando, Basura Cherian, Anoop Gould, Stephen Australian Natl Univ Australian Ctr Robot Vis Canberra ACT Australia

ISBN: (纸本)9781538604571

We present a principled approach to uncover the structure of visual data by solving a novel deep learning task coined visual permutation learning. the goal of this task is to find the permutation that recovers the structure of data from shuffled versions of it. In the case of natural images, this task boils down to recovering the original image from patches shuffled by an unknown permutation matrix. Unfortunately, permutation matrices are discrete, thereby posing difficulties for gradient-based methods. To this end, we resort to a continuous approximation of these matrices using doubly-stochastic matrices which we generate from standard CNN predictions using Sinkhorn iterations. Unrolling these iterations in a Sinkhorn network layer, we propose DeepPermNet, an end-to-end CNN model for this task. the utility of DeepPermNet is demonstrated on two challenging computer vision problems, namely, (i) relative attributes learning and (ii) self-supervised representation learning. Our results show state-of-the-art performance on the Public Figures and OSR benchmarks for (i) and on the classification and segmentation tasks on the PASCAL VOC dataset for (ii).

关键词： Visualization Image sequences computer vision Computational modeling Machine learning Training

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

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

引用

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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Neural Face Editing with Intrinsic Image Disentangling 30

Neural Face Editing with Intrinsic Image Disentangling

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

作者： Shu, Zhixin Yumer, Ersin Hadap, Sunil Sunkavalli, Kalyan Shechtman, Eli Samaras, Dimitris SUNY Stony Brook Stony Brook NY 11794 USA Adobe Res San Jose CA USA Univ Paris Saclay Cent Supelec St Aubin France

ISBN: (纸本)9781538604571

Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other - a process that is tedious, fragile, and computationally intensive. In this paper, we propose an end-to-end generative adversarial network that infers a face-specific disentangled representation of intrinsic face properties, including shape (i.e. normals), albedo, and lighting, and an alpha matte. We show that this network can be trained on "in-the-wild" images by incorporating an in-network physically-based image formation module and appropriate loss functions. Our disentangling latent representation allows for semantically relevant edits, where one aspect of facial appearance can be manipulated while keeping orthogonal properties fixed, and we demonstrate its use for a number of facial editing applications.

关键词： Face Lighting Rendering (computer graphics) Geometry Image reconstruction Manifolds Lighting Face Face image reconstruction Manifolds geometry editing intrinsic image

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Scene Parsing through ADE20K Dataset 30

Scene Parsing through ADE20K Dataset

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

作者： Zhou, Bolei Zhao, Hang Puig, Xavier Fidler, Sanja Barriuso, Adela Torralba, Antonio MIT Cambridge MA 02139 USA Univ Toronto Toronto ON Canada

ISBN: (纸本)9781538604571

Scene parsing, or recognizing and segmenting objects and stuff in an image, is one of the key problems in computer vision. Despite the community's efforts in data collection, there are still few image datasets covering a wide range of scenes and object categories with dense and detailed annotations for scene parsing. In this paper, we introduce and analyze the ADE20K dataset, spanning diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. A scene parsing benchmark is built upon the ADE20K with 150 object and stuff classes included. Several segmentation baseline models are evaluated on the benchmark. A novel network design called Cascade Segmentation Module is proposed to parse a scene into stuff, objects, and object parts in a cascade and improve over the baselines. We further show that the trained scene parsing networks can lead to applications such as image content removal and scene synthesis.

关键词： Image segmentation Semantics Sun Labeling Visualization Neural networks computer vision

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Outlier-Robust Tensor PCA 30

Outlier-Robust Tensor PCA

引用

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

作者： Zhou, Pan Feng, Jiashi Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781538604571

Low-rank tensor analysis is important for various real applications in computer vision. However, existing methods focus on recovering a low-rank tensor contaminated by Gaussian or gross sparse noise and hence cannot effectively handle outliers that are common in practical tensor data. To solve this issue, we propose an outlier-robust tensor principle component analysis (OR-TPCA) method for simultaneous low-rank tensor recovery and outlier detection. For intrinsically low-rank tensor observations with arbitrary outlier corruption, OR-TPCA is the first method that has provable performance guarantee for exactly recovering the tensor subspace and detecting outliers under mild conditions. Since tensor data are naturally high-dimensional and multi-way, we further develop a fast randomized algorithm that requires small sampling size yet can substantially accelerate OR-TPCA without performance drop. Experimental results on four tasks: outlier detection, clustering, semi-supervised and supervised learning, clearly demonstrate the advantages of our method.

关键词： Tensile stress Principal component analysis Discrete Fourier transforms Algorithm design and analysis Optimization computer vision Sparse matrices

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Hierarchical Boundary-Aware Neural Encoder for Video Captioning 30

Hierarchical Boundary-Aware Neural Encoder for Video Caption...

引用

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

作者： Baraldi, Lorenzo Grana, Costantino Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy

ISBN: (纸本)9781538604571

the use of Recurrent Neural Networks for video captioning has recently gained a lot of attention, since they can be used both to encode the input video and to generate the corresponding description. In this paper, we present a recurrent video encoding scheme which can discover and leverage the hierarchical structure of the video. Unlike the classical encoder-decoder approach, in which a video is encoded continuously by a recurrent layer, we propose a novel LSTM cell which can identify discontinuity points between frames or segments and modify the temporal connections of the encoding layer accordingly. We evaluate our approach on three large-scale datasets: the Montreal Video Annotation dataset, the MPII Movie Description dataset and the Microsoft Video Description Corpus. Experiments show that our approach can discover appropriate hierarchical representations of input videos and improve the state of the art results on movie description datasets.

关键词： Streaming media Encoding computer architecture Logic gates Motion pictures Visualization Microprocessors

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Cognitive Mapping and Planning for Visual Navigation 30

Cognitive Mapping and Planning for Visual Navigation

引用

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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