2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)最新文献_第10页

R²GAN: Cross-Modal Recipe Retrieval With Generative Adversarial Network 基于生成对抗网络的交叉模态配方检索

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2019-06-01 DOI: 10.1109/CVPR.2019.01174

B. Zhu, C. Ngo, Jingjing Chen, Y. Hao

{"title":"R²GAN: Cross-Modal Recipe Retrieval With Generative Adversarial Network","authors":"B. Zhu, C. Ngo, Jingjing Chen, Y. Hao","doi":"10.1109/CVPR.2019.01174","DOIUrl":"https://doi.org/10.1109/CVPR.2019.01174","url":null,"abstract":"Representing procedure text such as recipe for crossmodal retrieval is inherently a difficult problem, not mentioning to generate image from recipe for visualization. This paper studies a new version of GAN, named Recipe Retrieval Generative Adversarial Network (R2GAN), to explore the feasibility of generating image from procedure text for retrieval problem. The motivation of using GAN is twofold: learning compatible cross-modal features in an adversarial way, and explanation of search results by showing the images generated from recipes. The novelty of R2GAN comes from architecture design, specifically a GAN with one generator and dual discriminators is used, which makes the generation of image from recipe a feasible idea. Furthermore, empowered by the generated images, a two-level ranking loss in both embedding and image spaces are considered. These add-ons not only result in excellent retrieval performance, but also generate close-to-realistic food images useful for explaining ranking of recipes. On recipe1M dataset, R2GAN demonstrates high scalability to data size, outperforms all the existing approaches, and generates images intuitive for human to interpret the search results.","PeriodicalId":6711,"journal":{"name":"2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"50 1","pages":"11469-11478"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82531929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 23

Generalizing Eye Tracking With Bayesian Adversarial Learning 用贝叶斯对抗学习推广眼动追踪

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2019-06-01 DOI: 10.1109/CVPR.2019.01218

Kang Wang, Rui Zhao, Hui Su, Q. Ji

引用次数: 60

Learning to Film From Professional Human Motion Videos 学习从专业的人体运动视频拍摄

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2019-06-01 DOI: 10.1109/CVPR.2019.00437

Chong Huang, Chuan-en Lin, Zhenyu Yang, Yan Kong, Peng Chen, Xin Yang, K. Cheng

{"title":"Learning to Film From Professional Human Motion Videos","authors":"Chong Huang, Chuan-en Lin, Zhenyu Yang, Yan Kong, Peng Chen, Xin Yang, K. Cheng","doi":"10.1109/CVPR.2019.00437","DOIUrl":"https://doi.org/10.1109/CVPR.2019.00437","url":null,"abstract":"We investigate the problem of 6 degrees of freedom (DOF) camera planning for filming professional human motion videos using a camera drone. Existing methods either plan motions for only a pan-tilt-zoom (PTZ) camera, or adopt ad-hoc solutions without carefully considering the impact of video contents and previous camera motions on the future camera motions. As a result, they can hardly achieve satisfactory results in our drone cinematography task. In this study, we propose a learning-based framework which incorporates the video contents and previous camera motions to predict the future camera motions that enable the capture of professional videos. Specifically, the inputs of our framework are video contents which are represented using subject-related feature based on 2D skeleton and scene-related features extracted from background RGB images, and camera motions which are represented using optical flows. The correlation between the inputs and output future camera motions are learned via a sequence-to-sequence convolutional long short-term memory (Seq2Seq ConvLSTM) network from a large set of video clips. We deploy our approach to a real drone cinematography system by first predicting the future camera motions, and then converting them to the drone's control commands via an odometer. Our experimental results on extensive datasets and showcases exhibit significant improvements in our approach over conventional baselines and our approach can successfully mimic the footage of a professional cameraman.","PeriodicalId":6711,"journal":{"name":"2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"22 1","pages":"4239-4248"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83276649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

Coloring With Limited Data: Few-Shot Colorization via Memory Augmented Networks 有限数据的着色:通过记忆增强网络的少量着色

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2019-06-01 DOI: 10.1109/CVPR.2019.01154

Seungjoo Yoo, Hyojin Bahng, Sunghyo Chung, Junsoo Lee, Jaehyuk Chang, J. Choo

引用次数: 104

Double Nuclear Norm Based Low Rank Representation on Grassmann Manifolds for Clustering 基于双核范数的Grassmann流形低秩表示聚类

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2019-06-01 DOI: 10.1109/CVPR.2019.01235

Xinglin Piao, Yongli Hu, Junbin Gao, Yanfeng Sun, Baocai Yin

{"title":"Double Nuclear Norm Based Low Rank Representation on Grassmann Manifolds for Clustering","authors":"Xinglin Piao, Yongli Hu, Junbin Gao, Yanfeng Sun, Baocai Yin","doi":"10.1109/CVPR.2019.01235","DOIUrl":"https://doi.org/10.1109/CVPR.2019.01235","url":null,"abstract":"Unsupervised clustering for high-dimension data (such as imageset or video) is a hard issue in data processing and data mining area since these data always lie on a manifold (such as Grassmann manifold). Inspired of Low Rank representation theory, researchers proposed a series of effective clustering methods for high-dimension data with non-linear metric. However, most of these methods adopt the traditional single nuclear norm as the relaxation of the rank function, which would lead to suboptimal solution deviated from the original one. In this paper, we propose a new low rank model for high-dimension data clustering task on Grassmann manifold based on the Double Nuclear norm which is used to better approximate the rank minimization of matrix. Further, to consider the inner geometry or structure of data space, we integrated the adaptive Laplacian regularization to construct the local relationship of data samples. The proposed models have been assessed on several public datasets for imageset clustering. The experimental results show that the proposed models outperform the state-of-the-art clustering ones.","PeriodicalId":6711,"journal":{"name":"2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"1 12","pages":"12067-12076"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91480921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Learning Binary Code for Personalized Fashion Recommendation 学习二进制代码的个性化时尚推荐

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2019-06-01 DOI: 10.1109/CVPR.2019.01081

Zhi Lu, Yang Hu, Yu Jiang, Yan Chen, B. Zeng

{"title":"Learning Binary Code for Personalized Fashion Recommendation","authors":"Zhi Lu, Yang Hu, Yu Jiang, Yan Chen, B. Zeng","doi":"10.1109/CVPR.2019.01081","DOIUrl":"https://doi.org/10.1109/CVPR.2019.01081","url":null,"abstract":"With the rapid growth of fashion-focused social networks and online shopping, intelligent fashion recommendation is now in great needs. Recommending fashion outfits, each of which is composed of multiple interacted clothing and accessories, is relatively new to the field. The problem becomes even more interesting and challenging when considering users' personalized fashion style. Another challenge in a large-scale fashion outfit recommendation system is the efficiency issue of item/outfit search and storage. In this paper, we propose to learn binary code for efficient personalized fashion outfits recommendation. Our system consists of three components, a feature network for content extraction, a set of type-dependent hashing modules to learn binary codes, and a matching block that conducts pairwise matching. The whole framework is trained in an end-to-end manner. We collect outfit data together with user label information from a fashion-focused social website for the personalized recommendation task. Extensive experiments on our datasets show that the proposed framework outperforms the state-of-the-art methods significantly even with a simple backbone.","PeriodicalId":6711,"journal":{"name":"2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"115 1","pages":"10554-10562"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83142050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 48

It's Not About the Journey; It's About the Destination: Following Soft Paths Under Question-Guidance for Visual Reasoning 这不是关于旅程;这是关于目的地的:在问题引导下的软路径视觉推理

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2019-06-01 DOI: 10.1109/CVPR.2019.00203

Monica Haurilet, Alina Roitberg, R. Stiefelhagen

引用次数: 11

Deep Supervised Cross-Modal Retrieval 深度监督跨模态检索

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2019-06-01 DOI: 10.1109/CVPR.2019.01064

Liangli Zhen, Peng Hu, Xu Wang, Dezhong Peng

引用次数: 239

Searching for a Robust Neural Architecture in Four GPU Hours 在四个GPU小时内搜索一个鲁棒神经结构

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2019-06-01 DOI: 10.1109/CVPR.2019.00186

Xuanyi Dong, Yezhou Yang

引用次数: 536

Learning Joint Gait Representation via Quintuplet Loss Minimization 通过五联体损失最小化学习关节步态表征

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2019-06-01 DOI: 10.1109/CVPR.2019.00483

Kaihao Zhang, Wenhan Luo, Lin Ma, Wei Liu, Hongdong Li

{"title":"Learning Joint Gait Representation via Quintuplet Loss Minimization","authors":"Kaihao Zhang, Wenhan Luo, Lin Ma, Wei Liu, Hongdong Li","doi":"10.1109/CVPR.2019.00483","DOIUrl":"https://doi.org/10.1109/CVPR.2019.00483","url":null,"abstract":"Gait recognition is an important biometric method popularly used in video surveillance, where the task is to identify people at a distance by their walking patterns from video sequences. Most of the current successful approaches for gait recognition either use a pair of gait images to form a cross-gait representation or rely on a single gait image for unique-gait representation. These two types of representations emperically complement one another. In this paper, we propose a new Joint Unique-gait and Cross-gait Network (JUCNet), to combine the advantages of unique-gait representation with that of cross-gait representation, leading to an significantly improved performance. Another key contribution of this paper is a novel quintuplet loss function, which simultaneously increases the inter-class differences by pushing representations extracted from different subjects apart and decreases the intra-class variations by pulling representations extracted from the same subject together. Experiments show that our method achieves the state-of-the-art performance tested on standard benchmark datasets, demonstrating its superiority over existing methods.","PeriodicalId":6711,"journal":{"name":"2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"220 1","pages":"4695-4704"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79829695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 52