2017 IEEE International Conference on Computer Vision (ICCV)最新文献_第8页

Higher-Order Integration of Hierarchical Convolutional Activations for Fine-Grained Visual Categorization 用于细粒度视觉分类的层次卷积激活的高阶集成

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.63

Sijia Cai, W. Zuo, Lei Zhang

{"title":"Higher-Order Integration of Hierarchical Convolutional Activations for Fine-Grained Visual Categorization","authors":"Sijia Cai, W. Zuo, Lei Zhang","doi":"10.1109/ICCV.2017.63","DOIUrl":"https://doi.org/10.1109/ICCV.2017.63","url":null,"abstract":"The success of fine-grained visual categorization (FGVC) extremely relies on the modeling of appearance and interactions of various semantic parts. This makes FGVC very challenging because: (i) part annotation and detection require expert guidance and are very expensive; (ii) parts are of different sizes; and (iii) the part interactions are complex and of higher-order. To address these issues, we propose an end-to-end framework based on higherorder integration of hierarchical convolutional activations for FGVC. By treating the convolutional activations as local descriptors, hierarchical convolutional activations can serve as a representation of local parts from different scales. A polynomial kernel based predictor is proposed to capture higher-order statistics of convolutional activations for modeling part interaction. To model inter-layer part interactions, we extend polynomial predictor to integrate hierarchical activations via kernel fusion. Our work also provides a new perspective for combining convolutional activations from multiple layers. While hypercolumns simply concatenate maps from different layers, and holistically-nested network uses weighted fusion to combine side-outputs, our approach exploits higher-order intra-layer and inter-layer relations for better integration of hierarchical convolutional features. The proposed framework yields more discriminative representation and achieves competitive results on the widely used FGVC datasets.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"25 1","pages":"511-520"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91078721","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: 163

Monocular Video-Based Trailer Coupler Detection Using Multiplexer Convolutional Neural Network 基于多路卷积神经网络的单目视频拖车耦合器检测

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.584

Yousef Atoum, Joseph Roth, Michael Bliss, Wende Zhang, Xiaoming Liu

引用次数: 8

Summarization and Classification of Wearable Camera Streams by Learning the Distributions over Deep Features of Out-of-Sample Image Sequences 基于样本外图像序列深度特征分布的可穿戴相机流总结与分类

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.464

Alessandro Penna, Sadegh Mohammadi, N. Jojic, Vittorio Murino

{"title":"Summarization and Classification of Wearable Camera Streams by Learning the Distributions over Deep Features of Out-of-Sample Image Sequences","authors":"Alessandro Penna, Sadegh Mohammadi, N. Jojic, Vittorio Murino","doi":"10.1109/ICCV.2017.464","DOIUrl":"https://doi.org/10.1109/ICCV.2017.464","url":null,"abstract":"A popular approach to training classifiers of new image classes is to use lower levels of a pre-trained feed-forward neural network and retrain only the top. Thus, most layers simply serve as highly nonlinear feature extractors. While these features were found useful for classifying a variety of scenes and objects, previous work also demonstrated unusual levels of sensitivity to the input especially for images which are veering too far away from the training distribution. This can lead to surprising results as an imperceptible change in an image can be enough to completely change the predicted class. This occurs in particular in applications involving personal data, typically acquired with wearable cameras (e.g., visual lifelogs), where the problem is also made more complex by the dearth of new labeled training data that make supervised learning with deep models difficult. To alleviate these problems, in this paper we propose a new generative model that captures the feature distribution in new data. Its latent space then becomes more representative of the new data, while still retaining the generalization properties. In particular, we use constrained Markov walks over a counting grid for modeling image sequences, which not only yield good latent representations, but allow for excellent classification with only a handful of labeled training examples of the new scenes or objects, a scenario typical in lifelogging applications.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"14 1","pages":"4336-4344"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81916672","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: 5

AnnArbor: Approximate Nearest Neighbors Using Arborescence Coding AnnArbor:使用树影编码的近似近邻

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.523

Artem Babenko, V. Lempitsky

引用次数: 7

Multi-label Learning of Part Detectors for Heavily Occluded Pedestrian Detection 重度遮挡行人检测中局部检测器的多标签学习

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.377

Chunluan Zhou, Junsong Yuan

引用次数: 112

Transferring Objects: Joint Inference of Container and Human Pose 转移对象:容器与人体姿态的联合推理

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.319

Hanqing Wang, Wei Liang, L. Yu

{"title":"Transferring Objects: Joint Inference of Container and Human Pose","authors":"Hanqing Wang, Wei Liang, L. Yu","doi":"10.1109/ICCV.2017.319","DOIUrl":"https://doi.org/10.1109/ICCV.2017.319","url":null,"abstract":"Transferring objects from one place to another place is a common task performed by human in daily life. During this process, it is usually intuitive for humans to choose an object as a proper container and to use an efficient pose to carry objects; yet, it is non-trivial for current computer vision and machine learning algorithms. In this paper, we propose an approach to jointly infer container and human pose for transferring objects by minimizing the costs associated both object and pose candidates. Our approach predicts which object to choose as a container while reasoning about how humans interact with physical surroundings to accomplish the task of transferring objects given visual input. In the learning phase, the presented method learns how humans make rational choices of containers and poses for transferring different objects, as well as the physical quantities required by the transfer task (e.g., compatibility between container and containee, energy cost of carrying pose) via a structured learning approach. In the inference phase, given a scanned 3D scene with different object candidates and a dictionary of human poses, our approach infers the best object as a container together with human pose for transferring a given object.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"156 1","pages":"2952-2960"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77483028","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: 10

Multi-label Image Recognition by Recurrently Discovering Attentional Regions 递归发现注意区域的多标签图像识别

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.58

Zhouxia Wang, Tianshui Chen, Guanbin Li, Ruijia Xu, Liang Lin

{"title":"Multi-label Image Recognition by Recurrently Discovering Attentional Regions","authors":"Zhouxia Wang, Tianshui Chen, Guanbin Li, Ruijia Xu, Liang Lin","doi":"10.1109/ICCV.2017.58","DOIUrl":"https://doi.org/10.1109/ICCV.2017.58","url":null,"abstract":"This paper proposes a novel deep architecture to address multi-label image recognition, a fundamental and practical task towards general visual understanding. Current solutions for this task usually rely on an extra step of extracting hypothesis regions (i.e., region proposals), resulting in redundant computation and sub-optimal performance. In this work, we achieve the interpretable and contextualized multi-label image classification by developing a recurrent memorized-attention module. This module consists of two alternately performed components: i) a spatial transformer layer to locate attentional regions from the convolutional feature maps in a region-proposal-free way and ii) an LSTM (Long-Short Term Memory) sub-network to sequentially predict semantic labeling scores on the located regions while capturing the global dependencies of these regions. The LSTM also output the parameters for computing the spatial transformer. On large-scale benchmarks of multi-label image classification (e.g., MS-COCO and PASCAL VOC 07), our approach demonstrates superior performances over other existing state-of-the-arts in both accuracy and efficiency.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"18 1","pages":"464-472"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79276494","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: 238

Region-Based Correspondence Between 3D Shapes via Spatially Smooth Biclustering 基于空间平滑双聚类的三维形状区域对应

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.457

M. Denitto, S. Melzi, M. Bicego, U. Castellani, A. Farinelli, Mário A. T. Figueiredo, Yanir Kleiman, M. Ovsjanikov

{"title":"Region-Based Correspondence Between 3D Shapes via Spatially Smooth Biclustering","authors":"M. Denitto, S. Melzi, M. Bicego, U. Castellani, A. Farinelli, Mário A. T. Figueiredo, Yanir Kleiman, M. Ovsjanikov","doi":"10.1109/ICCV.2017.457","DOIUrl":"https://doi.org/10.1109/ICCV.2017.457","url":null,"abstract":"Region-based correspondence (RBC) is a highly relevant and non-trivial computer vision problem. Given two 3D shapes, RBC seeks segments/regions on these shapes that can be reliably put in correspondence. The problem thus consists both in finding the regions and determining the correspondences between them. This problem statement is similar to that of “biclustering ”, implying that RBC can be cast as a biclustering problem. Here, we exploit this implication by tackling RBC via a novel biclustering approach, called S4B (spatially smooth spike and slab biclustering), which: (i) casts the problem in a probabilistic low-rank matrix factorization perspective; (ii) uses a spike and slab prior to induce sparsity; (iii) is enriched with a spatial smoothness prior, based on geodesic distances, encouraging nearby vertices to belong to the same bicluster. This type of spatial prior cannot be used in classical biclustering techniques. We test the proposed approach on the FAUST dataset, outperforming both state-of-the-art RBC techniques and classical biclustering methods.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"93 1","pages":"4270-4279"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79433023","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: 6

2D-Driven 3D Object Detection in RGB-D Images RGB-D图像中2d驱动的3D目标检测

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.495

Jean Lahoud, Bernard Ghanem

{"title":"2D-Driven 3D Object Detection in RGB-D Images","authors":"Jean Lahoud, Bernard Ghanem","doi":"10.1109/ICCV.2017.495","DOIUrl":"https://doi.org/10.1109/ICCV.2017.495","url":null,"abstract":"In this paper, we present a technique that places 3D bounding boxes around objects in an RGB-D scene. Our approach makes best use of the 2D information to quickly reduce the search space in 3D, benefiting from state-of-the-art 2D object detection techniques. We then use the 3D information to orient, place, and score bounding boxes around objects. We independently estimate the orientation for every object, using previous techniques that utilize normal information. Object locations and sizes in 3D are learned using a multilayer perceptron (MLP). In the final step, we refine our detections based on object class relations within a scene. When compared to state-of-the-art detection methods that operate almost entirely in the sparse 3D domain, extensive experiments on the well-known SUN RGB-D dataset [29] show that our proposed method is much faster (4.1s per image) in detecting 3D objects in RGB-D images and performs better (3 mAP higher) than the state-of-the-art method that is 4.7 times slower and comparably to the method that is two orders of magnitude slower. This work hints at the idea that 2D-driven object detection in 3D should be further explored, especially in cases where the 3D input is sparse.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"12 1","pages":"4632-4640"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73220590","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: 146

Mutual Enhancement for Detection of Multiple Logos in Sports Videos 体育视频中多个logo检测的相互增强

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.519

Yuan Liao, Xiaoqing Lu, Chengcui Zhang, Yongtao Wang, Zhi Tang

{"title":"Mutual Enhancement for Detection of Multiple Logos in Sports Videos","authors":"Yuan Liao, Xiaoqing Lu, Chengcui Zhang, Yongtao Wang, Zhi Tang","doi":"10.1109/ICCV.2017.519","DOIUrl":"https://doi.org/10.1109/ICCV.2017.519","url":null,"abstract":"Detecting logo frequency and duration in sports videos provides sponsors an effective way to evaluate their advertising efforts. However, general-purposed object detection methods cannot address all the challenges in sports videos. In this paper, we propose a mutual-enhanced approach that can improve the detection of a logo through the information obtained from other simultaneously occurred logos. In a Fast-RCNN-based framework, we first introduce a homogeneity-enhanced re-ranking method by analyzing the characteristics of homogeneous logos in each frame, including type repetition, color consistency, and mutual exclusion. Different from conventional enhance mechanism that improves the weak proposals with the dominant proposals, our mutual method can also enhance the relatively significant proposals with weak proposals. Mutual enhancement is also included in our frame propagation mechanism that improves logo detection by utilizing the continuity of logos across frames. We use a tennis video dataset and an associated logo collection for detection evaluation. Experiments show that the proposed method outperforms existing methods with a higher accuracy.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"25 1","pages":"4856-4865"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73657557","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: 27