paper_id stringlengths 30 73 | title stringlengths 21 141 | authors listlengths 1 14 | cvf_url stringlengths 88 131 | pdf_url stringlengths 89 132 | supp_url stringclasses 0
values | arxiv_id stringclasses 42
values | arxiv_id_source stringclasses 2
values | bibtex large_stringlengths 239 514 | abstract large_stringlengths 405 2.19k |
|---|---|---|---|---|---|---|---|---|---|
Zhang_Cross-Scale_Cost_Aggregation_2014_CVPR_paper | Cross-Scale Cost Aggregation for Stereo Matching | [
"Kang Zhang",
"Yuqiang Fang",
"Dongbo Min",
"Lifeng Sun",
"Shiqiang Yang",
"Shuicheng Yan",
"Qi Tian"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Zhang_Cross-Scale_Cost_Aggregation_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Zhang_Cross-Scale_Cost_Aggregation_2014_CVPR_paper.pdf | null | 1403.0316 | title_snapshot | @InProceedings{Zhang_2014_CVPR,author = {Zhang, Kang and Fang, Yuqiang and Min, Dongbo and Sun, Lifeng and Yang, Shiqiang and Yan, Shuicheng and Tian, Qi},title = {Cross-Scale Cost Aggregation for Stereo Matching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month =... | Human beings process stereoscopic correspondence across multiple scales. However, this bio-inspiration is ignored by state-of-the-art cost aggregation methods for dense stereo correspondence. In this paper, a generic cross-scale cost aggregation framework is proposed to allow multi-scale interaction in cost aggregation... |
Tao_Asymmetrical_Gauss_Mixture_2014_CVPR_paper | Asymmetrical Gauss Mixture Models for Point Sets Matching | [
"Wenbing Tao",
"Kun Sun"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Tao_Asymmetrical_Gauss_Mixture_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Tao_Asymmetrical_Gauss_Mixture_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Tao_2014_CVPR,author = {Tao, Wenbing and Sun, Kun},title = {Asymmetrical Gauss Mixture Models for Point Sets Matching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | The probabilistic methods based on Symmetrical Gauss Mixture Model (SGMM) have achieved great success in point sets registration, but are seldom used to find the correspondences between two images due to the complexity of the non-rigid transformation and too many outliers. In this paper we propose an Asymmetrical GMM (... |
Fredriksson_Fast_and_Reliable_2014_CVPR_paper | Fast and Reliable Two-View Translation Estimation | [
"Johan Fredriksson",
"Olof Enqvist",
"Fredrik Kahl"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Fredriksson_Fast_and_Reliable_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Fredriksson_Fast_and_Reliable_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Fredriksson_2014_CVPR,author = {Fredriksson, Johan and Enqvist, Olof and Kahl, Fredrik},title = {Fast and Reliable Two-View Translation Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | It has long been recognized that one of the fundamental difficulties in theestimation of two-view epipolar geometry is the capability of handling outliers. In this paper, we develop a fast and tractable algorithm that maximizes the number of inliers under the assumption of a purely translating camera. Compared to class... |
Taniai_Graph_Cut_based_2014_CVPR_paper | Graph Cut based Continuous Stereo Matching using Locally Shared Labels | [
"Tatsunori Taniai",
"Yasuyuki Matsushita",
"Takeshi Naemura"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Taniai_Graph_Cut_based_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Taniai_Graph_Cut_based_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Taniai_2014_CVPR,author = {Taniai, Tatsunori and Matsushita, Yasuyuki and Naemura, Takeshi},title = {Graph Cut based Continuous Stereo Matching using Locally Shared Labels},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | We present an accurate and efficient stereo matching method using locally shared labels, a new labeling scheme that enables spatial propagation in MRF inference using graph cuts. They give each pixel and region a set of candidate disparity labels, which are randomly initialized, spatially propagated, and refined for co... |
Spyropoulos_Learning_to_Detect_2014_CVPR_paper | Learning to Detect Ground Control Points for Improving the Accuracy of Stereo Matching | [
"Aristotle Spyropoulos",
"Nikos Komodakis",
"Philippos Mordohai"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Spyropoulos_Learning_to_Detect_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Spyropoulos_Learning_to_Detect_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Spyropoulos_2014_CVPR,author = {Spyropoulos, Aristotle and Komodakis, Nikos and Mordohai, Philippos},title = {Learning to Detect Ground Control Points for Improving the Accuracy of Stereo Matching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = ... | While machine learning has been instrumental to the ongoing progress in most areas of computer vision, it has not been applied to the problem of stereo matching with similar frequency or success. We present a supervised learning approach for predicting the correctness of stereo matches based on a random forest and a se... |
Jayaraman_Decorrelating_Semantic_Visual_2014_CVPR_paper | Decorrelating Semantic Visual Attributes by Resisting the Urge to Share | [
"Dinesh Jayaraman",
"Fei Sha",
"Kristen Grauman"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Jayaraman_Decorrelating_Semantic_Visual_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Jayaraman_Decorrelating_Semantic_Visual_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Jayaraman_2014_CVPR,author = {Jayaraman, Dinesh and Sha, Fei and Grauman, Kristen},title = {Decorrelating Semantic Visual Attributes by Resisting the Urge to Share},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Existing methods to learn visual attributes are prone to learning the wrong thing---namely, properties that are correlated with the attribute of interest among training samples. Yet, many proposed applications of attributes rely on being able to learn the correct semantic concept corresponding to each attribute. We p... |
Zhang_PANDA_Pose_Aligned_2014_CVPR_paper | PANDA: Pose Aligned Networks for Deep Attribute Modeling | [
"Ning Zhang",
"Manohar Paluri",
"Marc'Aurelio Ranzato",
"Trevor Darrell",
"Lubomir Bourdev"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Zhang_PANDA_Pose_Aligned_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Zhang_PANDA_Pose_Aligned_2014_CVPR_paper.pdf | null | 1311.5591 | title_snapshot | @InProceedings{Zhang_2014_CVPR,author = {Zhang, Ning and Paluri, Manohar and Ranzato, Marc'Aurelio and Darrell, Trevor and Bourdev, Lubomir},title = {PANDA: Pose Aligned Networks for Deep Attribute Modeling},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June... | We propose a method for inferring human attributes (such as gender, hair style, clothes style, expression, action) from images of people under large variation of viewpoint, pose, appearance, articulation and occlusion. Convolutional Neural Nets (CNN) have been shown to perform very well on large scale object recognitio... |
Feng_Learning_Scalable_Discriminative_2014_CVPR_paper | Learning Scalable Discriminative Dictionary with Sample Relatedness | [
"Jiashi Feng",
"Stefanie Jegelka",
"Shuicheng Yan",
"Trevor Darrell"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Feng_Learning_Scalable_Discriminative_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Feng_Learning_Scalable_Discriminative_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Feng_2014_CVPR,author = {Feng, Jiashi and Jegelka, Stefanie and Yan, Shuicheng and Darrell, Trevor},title = {Learning Scalable Discriminative Dictionary with Sample Relatedness},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}... | Attributes are widely used as mid-level descriptors of object properties in object recognition and retrieval. Mostly, such attributes are manually pre-defined based on domain knowledge, and their number is fixed. However, pre-defined attributes may fail to adapt to the properties of the data at hand, may not necessari... |
Toshev_DeepPose_Human_Pose_2014_CVPR_paper | DeepPose: Human Pose Estimation via Deep Neural Networks | [
"Alexander Toshev",
"Christian Szegedy"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Toshev_DeepPose_Human_Pose_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Toshev_DeepPose_Human_Pose_2014_CVPR_paper.pdf | null | 1312.4659 | title_snapshot | @InProceedings{Toshev_2014_CVPR,author = {Toshev, Alexander and Szegedy, Christian},title = {DeepPose: Human Pose Estimation via Deep Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | We propose a method for human pose estimation based on Deep Neural Networks (DNNs). The pose estimation is formulated as a DNN-based regression problem towards body joints. We present a cascade of such DNN regressors which results in high precision pose estimates. The approach has the advantage of reasoning about pose ... |
Ionescu_Iterated_Second-Order_Label_2014_CVPR_paper | Iterated Second-Order Label Sensitive Pooling for 3D Human Pose Estimation | [
"Catalin Ionescu",
"Joao Carreira",
"Cristian Sminchisescu"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Ionescu_Iterated_Second-Order_Label_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Ionescu_Iterated_Second-Order_Label_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Ionescu_2014_CVPR,author = {Ionescu, Catalin and Carreira, Joao and Sminchisescu, Cristian},title = {Iterated Second-Order Label Sensitive Pooling for 3D Human Pose Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Recently, the emergence of Kinect systems has demonstrated the benefits of predicting an intermediate body part labeling for 3D human pose estimation, in conjunction with RGB-D imagery. The availability of depth information plays a critical role, so an important question is whether a similar representation can be devel... |
Belagiannis_3D_Pictorial_Structures_2014_CVPR_paper | 3D Pictorial Structures for Multiple Human Pose Estimation | [
"Vasileios Belagiannis",
"Sikandar Amin",
"Mykhaylo Andriluka",
"Bernt Schiele",
"Nassir Navab",
"Slobodan Ilic"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Belagiannis_3D_Pictorial_Structures_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Belagiannis_3D_Pictorial_Structures_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Belagiannis_2014_CVPR,author = {Belagiannis, Vasileios and Amin, Sikandar and Andriluka, Mykhaylo and Schiele, Bernt and Navab, Nassir and Ilic, Slobodan},title = {3D Pictorial Structures for Multiple Human Pose Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Re... | In this work, we address the problem of 3D pose estimation of multiple humans from multiple views. This is a more challenging problem than single human 3D pose estimation due to the much larger state space, partial occlusions as well as across view ambiguities when not knowing the identity of the humans in advance. To ... |
Huang_Learning_Euclidean-to-Riemannian_Metric_2014_CVPR_paper | Learning Euclidean-to-Riemannian Metric for Point-to-Set Classification | [
"Zhiwu Huang",
"Ruiping Wang",
"Shiguang Shan",
"Xilin Chen"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Huang_Learning_Euclidean-to-Riemannian_Metric_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Huang_Learning_Euclidean-to-Riemannian_Metric_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Huang_2014_CVPR,author = {Huang, Zhiwu and Wang, Ruiping and Shan, Shiguang and Chen, Xilin},title = {Learning Euclidean-to-Riemannian Metric for Point-to-Set Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | In this paper, we focus on the problem of point-to-set classification, where single points are matched against sets of correlated points. Since the points commonly lie in Euclidean space while the sets are typically modeled as elements on Riemannian manifold, they can be treated as Euclidean points and Riemannian point... |
Ren_Face_Alignment_at_2014_CVPR_paper | Face Alignment at 3000 FPS via Regressing Local Binary Features | [
"Shaoqing Ren",
"Xudong Cao",
"Yichen Wei",
"Jian Sun"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Ren_Face_Alignment_at_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Ren_Face_Alignment_at_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Ren_2014_CVPR,author = {Ren, Shaoqing and Cao, Xudong and Wei, Yichen and Sun, Jian},title = {Face Alignment at 3000 FPS via Regressing Local Binary Features},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | This paper presents a highly efficient, very accurate regression approach for face alignment. Our approach has two novel components: a set of local binary features, and a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local binary features for ea... |
Parkhi_A_Compact_and_2014_CVPR_paper | A Compact and Discriminative Face Track Descriptor | [
"Omkar M. Parkhi",
"Karen Simonyan",
"Andrea Vedaldi",
"Andrew Zisserman"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Parkhi_A_Compact_and_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Parkhi_A_Compact_and_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Parkhi_2014_CVPR,author = {Parkhi, Omkar M. and Simonyan, Karen and Vedaldi, Andrea and Zisserman, Andrew},title = {A Compact and Discriminative Face Track Descriptor},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Our goal is to learn a compact, discriminative vector representation of a face track, suitable for the face recognition tasks of verification and classification. To this end, we propose a novel face track descriptor, based on the Fisher Vector representation, and demonstrate that it has a number of favourable propert... |
Taigman_DeepFace_Closing_the_2014_CVPR_paper | DeepFace: Closing the Gap to Human-Level Performance in Face Verification | [
"Yaniv Taigman",
"Ming Yang",
"Marc'Aurelio Ranzato",
"Lior Wolf"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Taigman_DeepFace_Closing_the_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Taigman_2014_CVPR,author = {Taigman, Yaniv and Yang, Ming and Ranzato, Marc'Aurelio and Wolf, Lior},title = {DeepFace: Closing the Gap to Human-Level Performance in Face Verification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = ... | In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine... |
Fanello_Filter_Forests_for_2014_CVPR_paper | Filter Forests for Learning Data-Dependent Convolutional Kernels | [
"Sean Ryan Fanello",
"Cem Keskin",
"Pushmeet Kohli",
"Shahram Izadi",
"Jamie Shotton",
"Antonio Criminisi",
"Ugo Pattacini",
"Tim Paek"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Fanello_Filter_Forests_for_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Fanello_Filter_Forests_for_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Fanello_2014_CVPR,author = {Ryan Fanello, Sean and Keskin, Cem and Kohli, Pushmeet and Izadi, Shahram and Shotton, Jamie and Criminisi, Antonio and Pattacini, Ugo and Paek, Tim},title = {Filter Forests for Learning Data-Dependent Convolutional Kernels},booktitle = {Proceedings of the IEEE Conference on C... | We propose 'filter forests' (FF), an efficient new discriminative approach for predicting continuous variables given a signal and its context. FF can be used for general signal restoration tasks that can be tackled via convolutional filtering, where it attempts to learn the optimal filtering kernels to be applied to ea... |
Oquab_Learning_and_Transferring_2014_CVPR_paper | Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks | [
"Maxime Oquab",
"Leon Bottou",
"Ivan Laptev",
"Josef Sivic"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Oquab_Learning_and_Transferring_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Oquab_Learning_and_Transferring_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Oquab_2014_CVPR,author = {Oquab, Maxime and Bottou, Leon and Laptev, Ivan and Sivic, Josef},title = {Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {J... | Convolutional neural networks (CNN) have recently shown outstanding image classification performance in the large- scale visual recognition challenge (ILSVRC2012). The success of CNNs is attributed to their ability to learn rich mid-level image representations as opposed to hand-designed low-level features used in othe... |
Karpathy_Large-scale_Video_Classification_2014_CVPR_paper | Large-scale Video Classification with Convolutional Neural Networks | [
"Andrej Karpathy",
"George Toderici",
"Sanketh Shetty",
"Thomas Leung",
"Rahul Sukthankar",
"Li Fei-Fei"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Karpathy_Large-scale_Video_Classification_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Karpathy_Large-scale_Video_Classification_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Karpathy_2014_CVPR,author = {Karpathy, Andrej and Toderici, George and Shetty, Sanketh and Leung, Thomas and Sukthankar, Rahul and Fei-Fei, Li},title = {Large-scale Video Classification with Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Reco... | Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems. Encouraged by these results, we provide an extensive empirical evaluation of CNNs on large-scale video classification using a new dataset of 1 million YouTube videos belonging to 487 classes. We stud... |
Kang_Convolutional_Neural_Networks_2014_CVPR_paper | Convolutional Neural Networks for No-Reference Image Quality Assessment | [
"Le Kang",
"Peng Ye",
"Yi Li",
"David Doermann"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Kang_Convolutional_Neural_Networks_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Kang_Convolutional_Neural_Networks_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Kang_2014_CVPR,author = {Kang, Le and Ye, Peng and Li, Yi and Doermann, David},title = {Convolutional Neural Networks for No-Reference Image Quality Assessment},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | In this work we describe a Convolutional Neural Network (CNN) to accurately predict image quality without a reference image. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer ... |
Smith_Nonparametric_Context_Modeling_2014_CVPR_paper | Nonparametric Context Modeling of Local Appearance for Pose- and Expression-Robust Facial Landmark Localization | [
"Brandon M. Smith",
"Jonathan Brandt",
"Zhe Lin",
"Li Zhang"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Smith_Nonparametric_Context_Modeling_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Smith_Nonparametric_Context_Modeling_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Smith_2014_CVPR,author = {Smith, Brandon M. and Brandt, Jonathan and Lin, Zhe and Zhang, Li},title = {Nonparametric Context Modeling of Local Appearance for Pose- and Expression-Robust Facial Landmark Localization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition... | We propose a data-driven approach to facial landmark localization that models the correlations between each landmark and its surrounding appearance features. At runtime, each feature casts a weighted vote to predict landmark locations, where the weight is precomputed to take into account the feature's discriminative po... |
Liu_Learning_Expressionlets_on_2014_CVPR_paper | Learning Expressionlets on Spatio-Temporal Manifold for Dynamic Facial Expression Recognition | [
"Mengyi Liu",
"Shiguang Shan",
"Ruiping Wang",
"Xilin Chen"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Liu_Learning_Expressionlets_on_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Liu_Learning_Expressionlets_on_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Liu_2014_CVPR,author = {Liu, Mengyi and Shan, Shiguang and Wang, Ruiping and Chen, Xilin},title = {Learning Expressionlets on Spatio-Temporal Manifold for Dynamic Facial Expression Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {Jun... | Facial expression is temporally dynamic event which can be decomposed into a set of muscle motions occurring in different facial regions over various time intervals. For dynamic expression recognition, two key issues, temporal alignment and semantics-aware dynamic representation, must be taken into account. In this pap... |
Dehghan_Who_Do_I_2014_CVPR_paper | Who Do I Look Like? Determining Parent-Offspring Resemblance via Gated Autoencoders | [
"Afshin Dehghan",
"Enrique G. Ortiz",
"Ruben Villegas",
"Mubarak Shah"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Dehghan_Who_Do_I_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Dehghan_Who_Do_I_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Dehghan_2014_CVPR,author = {Dehghan, Afshin and Ortiz, Enrique G. and Villegas, Ruben and Shah, Mubarak},title = {Who Do I Look Like? Determining Parent-Offspring Resemblance via Gated Autoencoders},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month =... | Recent years have seen a major push for face recognition technology due to the large expansion of image sharing on social networks. In this paper, we consider the difficult task of determining parent-offspring resemblance using deep learning to answer the question "Who do I look like?" Although humans can perform this ... |
Zhao_Unified_Face_Analysis_2014_CVPR_paper | Unified Face Analysis by Iterative Multi-Output Random Forests | [
"Xiaowei Zhao",
"Tae-Kyun Kim",
"Wenhan Luo"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Zhao_Unified_Face_Analysis_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Zhao_Unified_Face_Analysis_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Zhao_2014_CVPR,author = {Zhao, Xiaowei and Kim, Tae-Kyun and Luo, Wenhan},title = {Unified Face Analysis by Iterative Multi-Output Random Forests},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | In this paper, we present a unified method for joint face image analysis, i.e., simultaneously estimating head pose, facial expression and landmark positions in real-world face images. To achieve this goal, we propose a novel iterative Multi-Output Random Forests (iMORF) algorithm, which explicitly models the relations... |
Mora_Geometric_Generative_Gaze_2014_CVPR_paper | Geometric Generative Gaze Estimation (G3E) for Remote RGB-D Cameras | [
"Kenneth Alberto Funes Mora",
"Jean-Marc Odobez"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Mora_Geometric_Generative_Gaze_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Mora_Geometric_Generative_Gaze_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Mora_2014_CVPR,author = {Alberto Funes Mora, Kenneth and Odobez, Jean-Marc},title = {Geometric Generative Gaze Estimation (G3E) for Remote RGB-D Cameras},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | We propose a head pose invariant gaze estimation model for distant RGB-D cameras. It relies on a geometric understanding of the 3D gaze action and generation of eye images. By introducing a semantic segmentation of the eye region within a generative process, the model (i) avoids the critical feature tracking of geome... |
Wu_A_Hierarchical_Probabilistic_2014_CVPR_paper | A Hierarchical Probabilistic Model for Facial Feature Detection | [
"Yue Wu",
"Ziheng Wang",
"Qiang Ji"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Wu_A_Hierarchical_Probabilistic_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Wu_A_Hierarchical_Probabilistic_2014_CVPR_paper.pdf | null | 1709.05732 | title_snapshot | @InProceedings{Wu_2014_CVPR,author = {Wu, Yue and Wang, Ziheng and Ji, Qiang},title = {A Hierarchical Probabilistic Model for Facial Feature Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Facial feature detection from facial images has attracted great attention in the field of computer vision. It is a nontrivial task since the appearance and shape of the face tend to change under different conditions. In this paper, we propose a hierarchical probabilistic model that could infer the true locations of fac... |
Sagonas_RAPS_Robust_and_2014_CVPR_paper | RAPS: Robust and Efficient Automatic Construction of Person-Specific Deformable Models | [
"Christos Sagonas",
"Yannis Panagakis",
"Stefanos Zafeiriou",
"Maja Pantic"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Sagonas_RAPS_Robust_and_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Sagonas_RAPS_Robust_and_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Sagonas_2014_CVPR,author = {Sagonas, Christos and Panagakis, Yannis and Zafeiriou, Stefanos and Pantic, Maja},title = {RAPS: Robust and Efficient Automatic Construction of Person-Specific Deformable Models},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}... | The construction of Facial Deformable Models (FDMs) is a very challenging computer vision problem, since the face is a highly deformable object and its appearance drastically changes under different poses, expressions, and illuminations. Although several methods for generic FDMs construction, have been proposed for fac... |
Martins_Non-Parametric_Bayesian_Constrained_2014_CVPR_paper | Non-Parametric Bayesian Constrained Local Models | [
"Pedro Martins",
"Rui Caseiro",
"Jorge Batista"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Martins_Non-Parametric_Bayesian_Constrained_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Martins_Non-Parametric_Bayesian_Constrained_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Martins_2014_CVPR,author = {Martins, Pedro and Caseiro, Rui and Batista, Jorge},title = {Non-Parametric Bayesian Constrained Local Models},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | This work presents a novel non-parametric Bayesian formulation for aligning faces in unseen images. Popular approaches, such as the Constrained Local Models (CLM) or the Active Shape Models (ASM), perform facial alignment through a local search, combining an ensemble of detectors with a global optimization strategy tha... |
Liu_Facial_Expression_Recognition_2014_CVPR_paper | Facial Expression Recognition via a Boosted Deep Belief Network | [
"Ping Liu",
"Shizhong Han",
"Zibo Meng",
"Yan Tong"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Liu_Facial_Expression_Recognition_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Liu_Facial_Expression_Recognition_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Liu_2014_CVPR,author = {Liu, Ping and Han, Shizhong and Meng, Zibo and Tong, Yan},title = {Facial Expression Recognition via a Boosted Deep Belief Network},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | A training process for facial expression recognition is usually performed sequentially in three individual stages: feature learning, feature selection, and classifier construction. Extensive empirical studies are needed to search for an optimal combination of feature representation, feature set, and classifier to achie... |
Antonakos_Automatic_Construction_of_2014_CVPR_paper | Automatic Construction of Deformable Models In-The-Wild | [
"Epameinondas Antonakos",
"Stefanos Zafeiriou"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Antonakos_Automatic_Construction_of_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Antonakos_Automatic_Construction_of_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Antonakos_2014_CVPR,author = {Antonakos, Epameinondas and Zafeiriou, Stefanos},title = {Automatic Construction of Deformable Models In-The-Wild},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Deformable objects are everywhere. Faces, cars, bicycles, chairs etc. Recently, there has been a wealth of research on training deformable models for object detection, part localization and recognition using annotated data. In order to train deformable models with good generalization ability, a large amount of carefull... |
Sugano_Learning-by-Synthesis_for_Appearance-based_2014_CVPR_paper | Learning-by-Synthesis for Appearance-based 3D Gaze Estimation | [
"Yusuke Sugano",
"Yasuyuki Matsushita",
"Yoichi Sato"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Sugano_Learning-by-Synthesis_for_Appearance-based_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Sugano_Learning-by-Synthesis_for_Appearance-based_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Sugano_2014_CVPR,author = {Sugano, Yusuke and Matsushita, Yasuyuki and Sato, Yoichi},title = {Learning-by-Synthesis for Appearance-based 3D Gaze Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Inferring human gaze from low-resolution eye images is still a challenging task despite its practical importance in many application scenarios. This paper presents a learning-by-synthesis approach to accurate image-based gaze estimation that is person- and head pose-independent. Unlike existing appearance-based methods... |
Xing_Towards_Multi-view_and_2014_CVPR_paper | Towards Multi-view and Partially-Occluded Face Alignment | [
"Junliang Xing",
"Zhiheng Niu",
"Junshi Huang",
"Weiming Hu",
"Shuicheng Yan"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Xing_Towards_Multi-view_and_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Xing_Towards_Multi-view_and_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Xing_2014_CVPR,author = {Xing, Junliang and Niu, Zhiheng and Huang, Junshi and Hu, Weiming and Yan, Shuicheng},title = {Towards Multi-view and Partially-Occluded Face Alignment},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}... | We present a robust model to locate facial landmarks under different views and possibly severe occlusions. To build reliable relationships between face appearance and shape with large view variations, we propose to formulate face alignment as an L1-induced Stagewise Relational Dictionary (SRD) learning problem. During ... |
Geng_Head_Pose_Estimation_2014_CVPR_paper | Head Pose Estimation Based on Multivariate Label Distribution | [
"Xin Geng",
"Yu Xia"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Geng_Head_Pose_Estimation_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Geng_Head_Pose_Estimation_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Geng_2014_CVPR,author = {Geng, Xin and Xia, Yu},title = {Head Pose Estimation Based on Multivariate Label Distribution},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Accurate ground truth pose is essential to the training of most existing head pose estimation algorithms. However, in many cases, the "ground truth" pose is obtained in rather subjective ways, such as asking the human subjects to stare at different markers on the wall. In such case, it is better to use soft labels rath... |
Li_Efficient_Boosted_Exemplar-based_2014_CVPR_paper | Efficient Boosted Exemplar-based Face Detection | [
"Haoxiang Li",
"Zhe Lin",
"Jonathan Brandt",
"Xiaohui Shen",
"Gang Hua"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Li_Efficient_Boosted_Exemplar-based_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Li_Efficient_Boosted_Exemplar-based_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Li_2014_CVPR,author = {Li, Haoxiang and Lin, Zhe and Brandt, Jonathan and Shen, Xiaohui and Hua, Gang},title = {Efficient Boosted Exemplar-based Face Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Despite the fact that face detection has been studied intensively over the past several decades, the problem is still not completely solved. Challenging conditions, such as extreme pose, lighting, and occlusion, have historically hampered traditional, model-based methods. In contrast, exemplar-based face detection has ... |
Tzimiropoulos_Gauss-Newton_Deformable_Part_2014_CVPR_paper | Gauss-Newton Deformable Part Models for Face Alignment in-the-Wild | [
"Georgios Tzimiropoulos",
"Maja Pantic"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Tzimiropoulos_Gauss-Newton_Deformable_Part_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Tzimiropoulos_Gauss-Newton_Deformable_Part_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Tzimiropoulos_2014_CVPR,author = {Tzimiropoulos, Georgios and Pantic, Maja},title = {Gauss-Newton Deformable Part Models for Face Alignment in-the-Wild},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Arguably, Deformable Part Models (DPMs) are one of the most prominent approaches for face alignment with impressive results being recently reported for both controlled lab and unconstrained settings. Fitting in most DPM methods is typically formulated as a two-step process during which discriminatively trained part tem... |
Asthana_Incremental_Face_Alignment_2014_CVPR_paper | Incremental Face Alignment in the Wild | [
"Akshay Asthana",
"Stefanos Zafeiriou",
"Shiyang Cheng",
"Maja Pantic"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Asthana_Incremental_Face_Alignment_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Asthana_Incremental_Face_Alignment_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Asthana_2014_CVPR,author = {Asthana, Akshay and Zafeiriou, Stefanos and Cheng, Shiyang and Pantic, Maja},title = {Incremental Face Alignment in the Wild},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | The development of facial databases with an abundance of annotated facial data captured under unconstrained 'in-the-wild' conditions have made discriminative facial deformable models the de facto choice for generic facial landmark localization. Even though very good performance for the facial landmark localization has ... |
Kazemi_One_Millisecond_Face_2014_CVPR_paper | One Millisecond Face Alignment with an Ensemble of Regression Trees | [
"Vahid Kazemi",
"Josephine Sullivan"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Kazemi_One_Millisecond_Face_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Kazemi_One_Millisecond_Face_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Kazemi_2014_CVPR,author = {Kazemi, Vahid and Sullivan, Josephine},title = {One Millisecond Face Alignment with an Ensemble of Regression Trees},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | This paper addresses the problem of Face Alignment for a single image. We show how an ensemble of regression trees can be used to estimate the face's landmark positions directly from a sparse subset of pixel intensities, achieving super-realtime performance with high quality predictions. We present a general framework ... |
Hu_Discriminative_Deep_Metric_2014_CVPR_paper | Discriminative Deep Metric Learning for Face Verification in the Wild | [
"Junlin Hu",
"Jiwen Lu",
"Yap-Peng Tan"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Hu_Discriminative_Deep_Metric_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Hu_Discriminative_Deep_Metric_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Hu_2014_CVPR,author = {Hu, Junlin and Lu, Jiwen and Tan, Yap-Peng},title = {Discriminative Deep Metric Learning for Face Verification in the Wild},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | This paper presents a new discriminative deep metric learning (DDML) method for face verification in the wild. Different from existing metric learning-based face verification methods which aim to learn a Mahalanobis distance metric to maximize the inter-class variations and minimize the intra-class variations, simultan... |
Kan_Stacked_Progressive_Auto-Encoders_2014_CVPR_paper | Stacked Progressive Auto-Encoders (SPAE) for Face Recognition Across Poses | [
"Meina Kan",
"Shiguang Shan",
"Hong Chang",
"Xilin Chen"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Kan_Stacked_Progressive_Auto-Encoders_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Kan_Stacked_Progressive_Auto-Encoders_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Kan_2014_CVPR,author = {Kan, Meina and Shan, Shiguang and Chang, Hong and Chen, Xilin},title = {Stacked Progressive Auto-Encoders (SPAE) for Face Recognition Across Poses},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Identifying subjects with variations caused by poses is one of the most challenging tasks in face recognition, since the difference in appearances caused by poses may be even larger than the difference due to identity. Inspired by the observation that pose variations change non-linearly but smoothly, we propose to lear... |
Sun_Deep_Learning_Face_2014_CVPR_paper | Deep Learning Face Representation from Predicting 10,000 Classes | [
"Yi Sun",
"Xiaogang Wang",
"Xiaoou Tang"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Sun_Deep_Learning_Face_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Sun_Deep_Learning_Face_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Sun_2014_CVPR,author = {Sun, Yi and Wang, Xiaogang and Tang, Xiaoou},title = {Deep Learning Face Representation from Predicting 10,000 Classes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | This paper proposes to learn a set of high-level feature representations through deep learning, referred to as Deep hidden IDentity features (DeepID), for face verification. We argue that DeepID can be effectively learned through challenging multi-class face identification tasks, whilst they can be generalized to other... |
Chu_3D-aided_Face_Recognition_2014_CVPR_paper | 3D-aided Face Recognition Robust to Expression and Pose Variations | [
"Baptiste Chu",
"Sami Romdhani",
"Liming Chen"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Chu_3D-aided_Face_Recognition_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Chu_3D-aided_Face_Recognition_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Chu_2014_CVPR,author = {Chu, Baptiste and Romdhani, Sami and Chen, Liming},title = {3D-aided Face Recognition Robust to Expression and Pose Variations},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Expression and pose variations are major challenges for reliable face recognition (FR) in 2D. In this paper, we aim to endow state of the art face recognition SDKs with robustness to facial expression variations and pose changes by using an extended 3D Morphable Model (3DMM) which isolates identity variations from thos... |
Hayat_Learning_Non-Linear_Reconstruction_2014_CVPR_paper | Learning Non-Linear Reconstruction Models for Image Set Classification | [
"Munawar Hayat",
"Mohammed Bennamoun",
"Senjian An"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Hayat_Learning_Non-Linear_Reconstruction_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Hayat_Learning_Non-Linear_Reconstruction_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Hayat_2014_CVPR,author = {Hayat, Munawar and Bennamoun, Mohammed and An, Senjian},title = {Learning Non-Linear Reconstruction Models for Image Set Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | We propose a deep learning framework for image set classification with application to face recognition. An Adaptive Deep Network Template (ADNT) is defined whose parameters are initialized by performing unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines (GRBMs). The pre-initi... |
Yao_Gesture_Recognition_Portfolios_2014_CVPR_paper | Gesture Recognition Portfolios for Personalization | [
"Angela Yao",
"Luc Van Gool",
"Pushmeet Kohli"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Yao_Gesture_Recognition_Portfolios_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Yao_Gesture_Recognition_Portfolios_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Yao_2014_CVPR,author = {Yao, Angela and Van Gool, Luc and Kohli, Pushmeet},title = {Gesture Recognition Portfolios for Personalization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Human gestures, similar to speech and handwriting, are often unique to the individual. Training a generic classifier applicable to everyone can be very difficult and as such, it has become a standard to use personalized classifiers in speech and handwriting recognition. In this paper, we address the problem of personal... |
Ong_Sign_Spotting_using_2014_CVPR_paper | Sign Spotting using Hierarchical Sequential Patterns with Temporal Intervals | [
"Eng-Jon Ong",
"Oscar Koller",
"Nicolas Pugeault",
"Richard Bowden"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Ong_Sign_Spotting_using_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Ong_Sign_Spotting_using_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Ong_2014_CVPR,author = {Ong, Eng-Jon and Koller, Oscar and Pugeault, Nicolas and Bowden, Richard},title = {Sign Spotting using Hierarchical Sequential Patterns with Temporal Intervals},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year =... | This paper tackles the problem of spotting a set of signs occuring in videos with sequences of signs. To achieve this, we propose to model the spatio-temporal signatures of a sign using an extension of sequential patterns that contain temporal intervals called {\em Sequential Interval Patterns} (SIP). We then propose a... |
Khan_Automatic_Feature_Learning_2014_CVPR_paper | Automatic Feature Learning for Robust Shadow Detection | [
"Salman Hameed Khan",
"Mohammed Bennamoun",
"Ferdous Sohel",
"Roberto Togneri"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Khan_Automatic_Feature_Learning_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Khan_Automatic_Feature_Learning_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Khan_2014_CVPR,author = {Hameed Khan, Salman and Bennamoun, Mohammed and Sohel, Ferdous and Togneri, Roberto},title = {Automatic Feature Learning for Robust Shadow Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | We present a practical framework to automatically detect shadows in real world scenes from a single photograph. Previous works on shadow detection put a lot of effort in designing shadow variant and invariant hand-crafted features. In contrast, our framework automatically learns the most relevant features in a supervis... |
Zheng_Packing_and_Padding_2014_CVPR_paper | Packing and Padding: Coupled Multi-index for Accurate Image Retrieval | [
"Liang Zheng",
"Shengjin Wang",
"Ziqiong Liu",
"Qi Tian"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Zheng_Packing_and_Padding_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Zheng_Packing_and_Padding_2014_CVPR_paper.pdf | null | 1402.2681 | title_snapshot | @InProceedings{Zheng_2014_CVPR,author = {Zheng, Liang and Wang, Shengjin and Liu, Ziqiong and Tian, Qi},title = {Packing and Padding: Coupled Multi-index for Accurate Image Retrieval},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | In Bag-of-Words (BoW) based image retrieval, the SIFT visual word has a low discriminative power, so false positive matches occur prevalently. Apart from the information loss during quantization, another cause is that the SIFT feature only describes the local gradient distribution. To address this problem, this paper p... |
Yang_Adaptive_Object_Retrieval_2014_CVPR_paper | Adaptive Object Retrieval with Kernel Reconstructive Hashing | [
"Haichuan Yang",
"Xiao Bai",
"Jun Zhou",
"Peng Ren",
"Zhihong Zhang",
"Jian Cheng"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Yang_Adaptive_Object_Retrieval_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Yang_Adaptive_Object_Retrieval_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Yang_2014_CVPR,author = {Yang, Haichuan and Bai, Xiao and Zhou, Jun and Ren, Peng and Zhang, Zhihong and Cheng, Jian},title = {Adaptive Object Retrieval with Kernel Reconstructive Hashing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},ye... | Hashing is very useful for fast approximate similarity search on large database. In the unsupervised settings, most hashing methods aim at preserving the similarity defined by Euclidean distance. Hash codes generated by these approaches only keep their Hamming distance corresponding to the pairwise Euclidean distance, ... |
Zheng_Bayes_Merging_of_2014_CVPR_paper | Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval | [
"Liang Zheng",
"Shengjin Wang",
"Wengang Zhou",
"Qi Tian"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Zheng_Bayes_Merging_of_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Zheng_Bayes_Merging_of_2014_CVPR_paper.pdf | null | 1403.0284 | title_snapshot | @InProceedings{Zheng_2014_CVPR,author = {Zheng, Liang and Wang, Shengjin and Zhou, Wengang and Tian, Qi},title = {Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | In the Bag-of-Words (BoW) model, the vocabulary is of key importance. Typically, multiple vocabularies are generated to correct quantization artifacts and improve recall. However, this routine is corrupted by vocabulary correlation, i.e., overlapping among different vocabularies. Vocabulary correlation leads to an over... |
Lin_Fast_Supervised_Hashing_2014_CVPR_paper | Fast Supervised Hashing with Decision Trees for High-Dimensional Data | [
"Guosheng Lin",
"Chunhua Shen",
"Qinfeng Shi",
"Anton van den Hengel",
"David Suter"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Lin_Fast_Supervised_Hashing_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Lin_Fast_Supervised_Hashing_2014_CVPR_paper.pdf | null | 1404.1561 | title_snapshot | @InProceedings{Lin_2014_CVPR,author = {Lin, Guosheng and Shen, Chunhua and Shi, Qinfeng and van den Hengel, Anton and Suter, David},title = {Fast Supervised Hashing with Decision Trees for High-Dimensional Data},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {... | Supervised hashing aims to map the original features to compact binary codes that are able to preserve label based similarity in the Hamming space. Non-linear hash functions have demonstrated their advantage over linear ones due to their powerful generalization capability. In the literature, kernel functions are typica... |
Chen_Detect_What_You_2014_CVPR_paper | Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts | [
"Xianjie Chen",
"Roozbeh Mottaghi",
"Xiaobai Liu",
"Sanja Fidler",
"Raquel Urtasun",
"Alan Yuille"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Chen_Detect_What_You_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Chen_Detect_What_You_2014_CVPR_paper.pdf | null | 1406.2031 | title_snapshot | @InProceedings{Chen_2014_CVPR,author = {Chen, Xianjie and Mottaghi, Roozbeh and Liu, Xiaobai and Fidler, Sanja and Urtasun, Raquel and Yuille, Alan},title = {Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts},booktitle = {Proceedings of the IEEE Conference on Computer Vision a... | Detecting objects becomes difficult when we need to deal with large shape deformation, occlusion and low resolution. We propose a novel approach to i) handle large deformations and partial occlusions in animals (as examples of highly deformable objects), ii) describe them in terms of body parts, and iii) detect them wh... |
Vezhnevets_Associative_Embeddings_for_2014_CVPR_paper | Associative Embeddings for Large-scale Knowledge Transfer with Self-assessment | [
"Alexander Vezhnevets",
"Vittorio Ferrari"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Vezhnevets_Associative_Embeddings_for_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Vezhnevets_Associative_Embeddings_for_2014_CVPR_paper.pdf | null | 1312.3240 | title_snapshot | @InProceedings{Vezhnevets_2014_CVPR,author = {Vezhnevets, Alexander and Ferrari, Vittorio},title = {Associative Embeddings for Large-scale Knowledge Transfer with Self-assessment},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | We propose a method for knowledge transfer between semantically related classes in ImageNet. By transferring knowledge from the images that have bounding-box annotations to the others, our method is capable of automatically populating ImageNet with many more bounding-boxes. The underlying assumption that objects from s... |
Hariharan_Detecting_Objects_using_2014_CVPR_paper | Detecting Objects using Deformation Dictionaries | [
"Bharath Hariharan",
"C. L. Zitnick",
"Piotr Dollar"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Hariharan_Detecting_Objects_using_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Hariharan_Detecting_Objects_using_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Hariharan_2014_CVPR,author = {Hariharan, Bharath and Zitnick, C. L. and Dollar, Piotr},title = {Detecting Objects using Deformation Dictionaries},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Several popular and effective object detectors separately model intra-class variations arising from deformations and appearance changes. This reduces model complexity while enabling the detection of objects across changes in view- point, object pose, etc. The Deformable Part Model (DPM) is perhaps the most successful s... |
Li_Persistence-based_Structural_Recognition_2014_CVPR_paper | Persistence-based Structural Recognition | [
"Chunyuan Li",
"Maks Ovsjanikov",
"Frederic Chazal"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Li_Persistence-based_Structural_Recognition_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Li_Persistence-based_Structural_Recognition_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Li_2014_CVPR,author = {Li, Chunyuan and Ovsjanikov, Maks and Chazal, Frederic},title = {Persistence-based Structural Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | This paper presents a framework for object recognition using topological persistence. In particular, we show that the so-called persistence diagrams built from functions defined on the objects can serve as compact and informative descriptors for images and shapes. Complementary to the bag-of-features representation, wh... |
Chen_Inferring_Unseen_Views_2014_CVPR_paper | Inferring Unseen Views of People | [
"Chao-Yeh Chen",
"Kristen Grauman"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Chen_Inferring_Unseen_Views_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Chen_Inferring_Unseen_Views_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Chen_2014_CVPR,author = {Chen, Chao-Yeh and Grauman, Kristen},title = {Inferring Unseen Views of People},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | We pose unseen view synthesis as a probabilistic tensor completion problem. Given images of people organized by their rough viewpoint, we form a 3D appearance tensor indexed by images (pose examples), viewpoints, and image positions. After discovering the low-dimensional latent factors that approximate that tensor, we ... |
Berg_Birdsnap_Large-scale_Fine-grained_2014_CVPR_paper | Birdsnap: Large-scale Fine-grained Visual Categorization of Birds | [
"Thomas Berg",
"Jiongxin Liu",
"Seung Woo Lee",
"Michelle L. Alexander",
"David W. Jacobs",
"Peter N. Belhumeur"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Berg_Birdsnap_Large-scale_Fine-grained_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Berg_Birdsnap_Large-scale_Fine-grained_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Berg_2014_CVPR,author = {Berg, Thomas and Liu, Jiongxin and Woo Lee, Seung and Alexander, Michelle L. and Jacobs, David W. and Belhumeur, Peter N.},title = {Birdsnap: Large-scale Fine-grained Visual Categorization of Birds},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Re... | We address the problem of large-scale fine-grained visual categorization, describing new methods we have used to produce an online field guide to 500 North American bird species. We focus on the challenges raised when such a system is asked to distinguish between highly similar species of birds. First, we introduce ... |
Fouhey_Predicting_Object_Dynamics_2014_CVPR_paper | Predicting Object Dynamics in Scenes | [
"David F. Fouhey",
"C. L. Zitnick"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Fouhey_Predicting_Object_Dynamics_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Fouhey_Predicting_Object_Dynamics_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Fouhey_2014_CVPR,author = {Fouhey, David F. and Zitnick, C. L.},title = {Predicting Object Dynamics in Scenes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Given a static scene, a human can trivially enumerate the myriad of things that can happen next and characterize the relative likelihood of each. In the process, we make use of enormous amounts of commonsense knowledge about how the world works. In this paper, we investigate learning this commonsense knowledge from dat... |
Chen_Enriching_Visual_Knowledge_2014_CVPR_paper | Enriching Visual Knowledge Bases via Object Discovery and Segmentation | [
"Xinlei Chen",
"Abhinav Shrivastava",
"Abhinav Gupta"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Chen_Enriching_Visual_Knowledge_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Chen_Enriching_Visual_Knowledge_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Chen_2014_CVPR,author = {Chen, Xinlei and Shrivastava, Abhinav and Gupta, Abhinav},title = {Enriching Visual Knowledge Bases via Object Discovery and Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | There have been some recent efforts to build visual knowledge bases from Internet images. But most of these approaches have focused on bounding box representation of objects. In this paper, we propose to enrich these knowledge bases by automatically discovering objects and their segmentations from noisy Internet images... |
Pickup_Seeing_the_Arrow_2014_CVPR_paper | Seeing the Arrow of Time | [
"Lyndsey C. Pickup",
"Zheng Pan",
"Donglai Wei",
"YiChang Shih",
"Changshui Zhang",
"Andrew Zisserman",
"Bernhard Scholkopf",
"William T. Freeman"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Pickup_Seeing_the_Arrow_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Pickup_Seeing_the_Arrow_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Pickup_2014_CVPR,author = {Pickup, Lyndsey C. and Pan, Zheng and Wei, Donglai and Shih, YiChang and Zhang, Changshui and Zisserman, Andrew and Scholkopf, Bernhard and Freeman, William T.},title = {Seeing the Arrow of Time},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Rec... | We explore whether we can observe Time's Arrow in a temporal sequence--is it possible to tell whether a video is running forwards or backwards? We investigate this somewhat philosophical question using computer vision and machine learning techniques. We explore three methods by which we might detect Time's Arrow in vi... |
Zhao_Hierarchical_Feature_Hashing_2014_CVPR_paper | Hierarchical Feature Hashing for Fast Dimensionality Reduction | [
"Bin Zhao",
"Eric P. Xing"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Zhao_Hierarchical_Feature_Hashing_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Zhao_Hierarchical_Feature_Hashing_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Zhao_2014_CVPR,author = {Zhao, Bin and Xing, Eric P.},title = {Hierarchical Feature Hashing for Fast Dimensionality Reduction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Curse of dimensionality is a practical and challenging problem in image categorization, especially in cases with a large number of classes. Multi-class classification encounters severe computational and storage problems when dealing with these large scale tasks. In this paper, we propose hierarchical feature hashing to... |
Papandreou_Modeling_Image_Patches_2014_CVPR_paper | Modeling Image Patches with a Generic Dictionary of Mini-Epitomes | [
"George Papandreou",
"Liang-Chieh Chen",
"Alan L. Yuille"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Papandreou_Modeling_Image_Patches_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Papandreou_Modeling_Image_Patches_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Papandreou_2014_CVPR,author = {Papandreou, George and Chen, Liang-Chieh and Yuille, Alan L.},title = {Modeling Image Patches with a Generic Dictionary of Mini-Epitomes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | The goal of this paper is to question the necessity of features like SIFT in categorical visual recognition tasks. As an alternative, we develop a generative model for the raw intensity of image patches and show that it can support image classification performance on par with optimized SIFT-based techniques in a bag-of... |
Zhang_Simplex-Based_3D_Spatio-Temporal_2014_CVPR_paper | Simplex-Based 3D Spatio-Temporal Feature Description for Action Recognition | [
"Hao Zhang",
"Wenjun Zhou",
"Christopher Reardon",
"Lynne E. Parker"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Zhang_Simplex-Based_3D_Spatio-Temporal_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Zhang_Simplex-Based_3D_Spatio-Temporal_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Zhang_2014_CVPR,author = {Zhang, Hao and Zhou, Wenjun and Reardon, Christopher and Parker, Lynne E.},title = {Simplex-Based 3D Spatio-Temporal Feature Description for Action Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year... | We present a novel feature description algorithm to describe 3D local spatio-temporal features for human action recognition. Our descriptor avoids the singularity and limited discrimination power issues of traditional 3D descriptors by quantizing and describing visual features in the simplex topological vector space. S... |
Buch_In_Search_of_2014_CVPR_paper | In Search of Inliers: 3D Correspondence by Local and Global Voting | [
"Anders Glent Buch",
"Yang Yang",
"Norbert Kruger",
"Henrik Gordon Petersen"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Buch_In_Search_of_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Buch_In_Search_of_2014_CVPR_paper.pdf | null | 1708.06966 | title_snapshot | @InProceedings{Buch_2014_CVPR,author = {Glent Buch, Anders and Yang, Yang and Kruger, Norbert and Gordon Petersen, Henrik},title = {In Search of Inliers: 3D Correspondence by Local and Global Voting},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year =... | We present a method for finding correspondence between 3D models. From an initial set of feature correspondences, our method uses a fast voting scheme to separate the inliers from the outliers. The novelty of our method lies in the use of a combination of local and global constraints to determine if a vote should be ca... |
Ding_Collective_Matrix_Factorization_2014_CVPR_paper | Collective Matrix Factorization Hashing for Multimodal Data | [
"Guiguang Ding",
"Yuchen Guo",
"Jile Zhou"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Ding_Collective_Matrix_Factorization_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Ding_Collective_Matrix_Factorization_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Ding_2014_CVPR,author = {Ding, Guiguang and Guo, Yuchen and Zhou, Jile},title = {Collective Matrix Factorization Hashing for Multimodal Data},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Nearest neighbor search methods based on hashing have attracted considerable attention for effective and efficient large-scale similarity search in computer vision and information retrieval community. In this paper, we study the problems of learning hash functions in the context of multimodal data for cross-view simila... |
Cho_Finding_Matches_in_2014_CVPR_paper | Finding Matches in a Haystack: A Max-Pooling Strategy for Graph Matching in the Presence of Outliers | [
"Minsu Cho",
"Jian Sun",
"Olivier Duchenne",
"Jean Ponce"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Cho_Finding_Matches_in_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Cho_Finding_Matches_in_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Cho_2014_CVPR,author = {Cho, Minsu and Sun, Jian and Duchenne, Olivier and Ponce, Jean},title = {Finding Matches in a Haystack: A Max-Pooling Strategy for Graph Matching in the Presence of Outliers},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month =... | A major challenge in real-world feature matching problems is to tolerate the numerous outliers arising in typical visual tasks. Variations in object appearance, shape, and structure within the same object class make it harder to distinguish inliers from outliers due to clutters. In this paper, we propose a max-pooling ... |
Tao_Locality_in_Generic_2014_CVPR_paper | Locality in Generic Instance Search from One Example | [
"Ran Tao",
"Efstratios Gavves",
"Cees G.M. Snoek",
"Arnold W.M. Smeulders"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Tao_Locality_in_Generic_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Tao_Locality_in_Generic_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Tao_2014_CVPR,author = {Tao, Ran and Gavves, Efstratios and Snoek, Cees G.M. and Smeulders, Arnold W.M.},title = {Locality in Generic Instance Search from One Example},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | This paper aims for generic instance search from a single example. Where the state-of-the-art relies on global image representation for the search, we proceed by including locality at all steps of the method. As the first novelty, we consider many boxes per database image as candidate targets to search locally in the p... |
Ben-Shalom_Congruency-Based_Reranking_2014_CVPR_paper | Congruency-Based Reranking | [
"Itai Ben-Shalom",
"Noga Levy",
"Lior Wolf",
"Nachum Dershowitz",
"Adiel Ben-Shalom",
"Roni Shweka",
"Yaacov Choueka",
"Tamir Hazan",
"Yaniv Bar"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Ben-Shalom_Congruency-Based_Reranking_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Ben-Shalom_Congruency-Based_Reranking_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Ben-Shalom_2014_CVPR,author = {Ben-Shalom, Itai and Levy, Noga and Wolf, Lior and Dershowitz, Nachum and Ben-Shalom, Adiel and Shweka, Roni and Choueka, Yaacov and Hazan, Tamir and Bar, Yaniv},title = {Congruency-Based Reranking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Patt... | We present a tool for re-ranking the results of a specific query by considering the (n+1) × (n+1) matrix of pairwise similarities among the elements of the set of n retrieved results and the query itself. The re-ranking thus makes use of the similarities between the various results and does not employ additional source... |
Davis_Asymmetric_Sparse_Kernel_2014_CVPR_paper | Asymmetric Sparse Kernel Approximations for Large-scale Visual Search | [
"Damek Davis",
"Jonathan Balzer",
"Stefano Soatto"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Davis_Asymmetric_Sparse_Kernel_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Davis_Asymmetric_Sparse_Kernel_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Davis_2014_CVPR,author = {Davis, Damek and Balzer, Jonathan and Soatto, Stefano},title = {Asymmetric Sparse Kernel Approximations for Large-scale Visual Search},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | We introduce an asymmetric sparse approximate embedding optimized for fast kernel comparison operations arising in large-scale visual search. In contrast to other methods that perform an explicit approximate embedding using kernel PCA followed by a distance compression technique in R^d, which loses information at both ... |
Irie_Locally_Linear_Hashing_2014_CVPR_paper | Locally Linear Hashing for Extracting Non-Linear Manifolds | [
"Go Irie",
"Zhenguo Li",
"Xiao-Ming Wu",
"Shih-Fu Chang"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Irie_Locally_Linear_Hashing_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Irie_Locally_Linear_Hashing_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Irie_2014_CVPR,author = {Irie, Go and Li, Zhenguo and Wu, Xiao-Ming and Chang, Shih-Fu},title = {Locally Linear Hashing for Extracting Non-Linear Manifolds},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Previous efforts in hashing intend to preserve data variance or pairwise affinity, but neither is adequate in capturing the manifold structures hidden in most visual data. In this paper, we tackle this problem by reconstructing the locally linear structures of manifolds in the binary Hamming space, which can be learned... |
Karasev_Active_Frame_Location_2014_CVPR_paper | Active Frame, Location, and Detector Selection for Automated and Manual Video Annotation | [
"Vasiliy Karasev",
"Avinash Ravichandran",
"Stefano Soatto"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Karasev_Active_Frame_Location_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Karasev_Active_Frame_Location_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Karasev_2014_CVPR,author = {Karasev, Vasiliy and Ravichandran, Avinash and Soatto, Stefano},title = {Active Frame, Location, and Detector Selection for Automated and Manual Video Annotation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},... | We describe an information-driven active selection approach to determine which detectors to deploy at which location in which frame of a video to minimize semantic class label uncertainty at every pixel, with the smallest computational cost that ensures a given uncertainty bound. We show minimal performance reduction c... |
Heo_Distance_Encoded_Product_2014_CVPR_paper | Distance Encoded Product Quantization | [
"Jae-Pil Heo",
"Zhe Lin",
"Sung-Eui Yoon"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Heo_Distance_Encoded_Product_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Heo_Distance_Encoded_Product_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Heo_2014_CVPR,author = {Heo, Jae-Pil and Lin, Zhe and Yoon, Sung-Eui},title = {Distance Encoded Product Quantization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Many binary code embedding techniques have been proposed for large-scale approximate nearest neighbor search in computer vision. Recently, product quantization that encodes the cluster index in each subspace has been shown to provide impressive accuracy for nearest neighbor search. In this paper, we explore a simple qu... |
Liu_Collaborative_Hashing_2014_CVPR_paper | Collaborative Hashing | [
"Xianglong Liu",
"Junfeng He",
"Cheng Deng",
"Bo Lang"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Liu_Collaborative_Hashing_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Liu_Collaborative_Hashing_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Liu_2014_CVPR,author = {Liu, Xianglong and He, Junfeng and Deng, Cheng and Lang, Bo},title = {Collaborative Hashing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Hashing technique has become a promising approach for fast similarity search. Most of existing hashing research pursue the binary codes for the same type of entities by preserving their similarities. In practice, there are many scenarios involving nearest neighbor search on the data given in matrix form, where two diff... |
Erhan_Scalable_Object_Detection_2014_CVPR_paper | Scalable Object Detection using Deep Neural Networks | [
"Dumitru Erhan",
"Christian Szegedy",
"Alexander Toshev",
"Dragomir Anguelov"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Erhan_Scalable_Object_Detection_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Erhan_Scalable_Object_Detection_2014_CVPR_paper.pdf | null | 1312.2249 | title_snapshot | @InProceedings{Erhan_2014_CVPR,author = {Erhan, Dumitru and Szegedy, Christian and Toshev, Alexander and Anguelov, Dragomir},title = {Scalable Object Detection using Deep Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Deep convolutional neural networks have recently achieved state-of-the-art performance on a number of image recognition benchmarks, including the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC-2012). The winning model on the localization sub-task was a network that predicts a single bounding box and a confid... |
Oxholm_Multiview_Shape_and_2014_CVPR_paper | Multiview Shape and Reflectance from Natural Illumination | [
"Geoffrey Oxholm",
"Ko Nishino"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Oxholm_Multiview_Shape_and_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Oxholm_Multiview_Shape_and_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Oxholm_2014_CVPR,author = {Oxholm, Geoffrey and Nishino, Ko},title = {Multiview Shape and Reflectance from Natural Illumination},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | The world is full of objects with complex reflectances, situated in complex illumination environments. Past work on full 3D geometry recovery, however, has tried to handle this complexity by framing it into simplistic models of reflectance (Lambetian, mirrored, or diffuse plus specular) or illumination (one or more poi... |
Fu_Reflectance_and_Fluorescent_2014_CVPR_paper | Reflectance and Fluorescent Spectra Recovery based on Fluorescent Chromaticity Invariance under Varying Illumination | [
"Ying Fu",
"Antony Lam",
"Yasuyuki Kobashi",
"Imari Sato",
"Takahiro Okabe",
"Yoichi Sato"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Fu_Reflectance_and_Fluorescent_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Fu_Reflectance_and_Fluorescent_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Fu_2014_CVPR,author = {Fu, Ying and Lam, Antony and Kobashi, Yasuyuki and Sato, Imari and Okabe, Takahiro and Sato, Yoichi},title = {Reflectance and Fluorescent Spectra Recovery based on Fluorescent Chromaticity Invariance under Varying Illumination},booktitle = {Proceedings of the IEEE Conference on Com... | In recent years, fluorescence analysis of scenes has received attention. Fluorescence can provide additional information about scenes, and has been used in applications such as camera spectral sensitivity estimation, 3D reconstruction, and color relighting. In particular, hyperspectral images of reflective-fluoresc... |
Chandraker_What_Camera_Motion_2014_CVPR_paper | What Camera Motion Reveals About Shape With Unknown BRDF | [
"Manmohan Chandraker"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Chandraker_What_Camera_Motion_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Chandraker_What_Camera_Motion_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Chandraker_2014_CVPR,author = {Chandraker, Manmohan},title = {What Camera Motion Reveals About Shape With Unknown BRDF},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Psychophysical studies show motion cues inform about shape even with unknown reflectance. Recent works in computer vision have considered shape recovery for an object of unknown BRDF using light source or object motions. This paper addresses the remaining problem of determining shape from the (small or differential) mo... |
Ikehata_Photometric_Stereo_using_2014_CVPR_paper | Photometric Stereo using Constrained Bivariate Regression for General Isotropic Surfaces | [
"Satoshi Ikehata",
"Kiyoharu Aizawa"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Ikehata_Photometric_Stereo_using_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Ikehata_Photometric_Stereo_using_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Ikehata_2014_CVPR,author = {Ikehata, Satoshi and Aizawa, Kiyoharu},title = {Photometric Stereo using Constrained Bivariate Regression for General Isotropic Surfaces},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | This paper presents a photometric stereo method that is purely pixelwise and handles general isotropic surfaces in a stable manner. Following the recently proposed sum-of-lobes representation of the isotropic reflectance function, we constructed a constrained bivariate regression problem where the regression function i... |
Guo_Robust_Separation_of_2014_CVPR_paper | Robust Separation of Reflection from Multiple Images | [
"Xiaojie Guo",
"Xiaochun Cao",
"Yi Ma"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Guo_Robust_Separation_of_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Guo_Robust_Separation_of_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Guo_2014_CVPR,author = {Guo, Xiaojie and Cao, Xiaochun and Ma, Yi},title = {Robust Separation of Reflection from Multiple Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | When one records a video/image sequence through a transparent medium (e.g. glass), the image is often a superposition of a transmitted layer (scene behind the medium) and a reflected layer. Recovering the two layers from such images seems to be a highly ill-posed problem since the number of unknowns to recover is twice... |
Xie_Surface-from-Gradients_An_Approach_2014_CVPR_paper | Surface-from-Gradients: An Approach Based on Discrete Geometry Processing | [
"Wuyuan Xie",
"Yunbo Zhang",
"Charlie C. L. Wang",
"Ronald C.-K. Chung"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Xie_Surface-from-Gradients_An_Approach_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Xie_Surface-from-Gradients_An_Approach_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Xie_2014_CVPR,author = {Xie, Wuyuan and Zhang, Yunbo and Wang, Charlie C. L. and Chung, Ronald C.-K.},title = {Surface-from-Gradients: An Approach Based on Discrete Geometry Processing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year ... | In this paper, we propose an efficient method to reconstruct surface-from-gradients (SfG). Our method is formulated under the framework of discrete geometry processing. Unlike the existing SfG approaches, we transfer the continuous reconstruction problem into a discrete space and efficiently solve the problem via a seq... |
Alahi_Socially-aware_Large-scale_Crowd_2014_CVPR_paper | Socially-aware Large-scale Crowd Forecasting | [
"Alexandre Alahi",
"Vignesh Ramanathan",
"Li Fei-Fei"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Alahi_Socially-aware_Large-scale_Crowd_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Alahi_Socially-aware_Large-scale_Crowd_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Alahi_2014_CVPR,author = {Alahi, Alexandre and Ramanathan, Vignesh and Fei-Fei, Li},title = {Socially-aware Large-scale Crowd Forecasting},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | In crowded spaces such as city centers or train stations, human mobility looks complex, but is often influenced only by a few causes. We propose to quantitatively study crowded environments by introducing a dataset of 42 million trajectories collected in train stations. Given this dataset, we address the problem of for... |
Yi_L0_Regularized_Stationary_2014_CVPR_paper | L0 Regularized Stationary Time Estimation for Crowd Group Analysis | [
"Shuai Yi",
"Xiaogang Wang",
"Cewu Lu",
"Jiaya Jia"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Yi_L0_Regularized_Stationary_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Yi_L0_Regularized_Stationary_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Yi_2014_CVPR,author = {Yi, Shuai and Wang, Xiaogang and Lu, Cewu and Jia, Jiaya},title = {L0 Regularized Stationary Time Estimation for Crowd Group Analysis},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | We tackle stationary crowd analysis in this paper, which is similarly important as modeling mobile groups in crowd scenes and finds many applications in surveillance. Our key contribution is to propose a robust algorithm of estimating how long a foreground pixel becomes stationary. It is much more challenging than only... |
Shao_Scene-Independent_Group_Profiling_2014_CVPR_paper | Scene-Independent Group Profiling in Crowd | [
"Jing Shao",
"Chen Change Loy",
"Xiaogang Wang"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Shao_Scene-Independent_Group_Profiling_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Shao_Scene-Independent_Group_Profiling_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Shao_2014_CVPR,author = {Shao, Jing and Change Loy, Chen and Wang, Xiaogang},title = {Scene-Independent Group Profiling in Crowd},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Groups are the primary entities that make up a crowd. Understanding group-level dynamics and properties is thus scientifically important and practically useful in a wide range of applications, especially for crowd understanding. In this study we show that fundamental group-level properties, such as intra-group stabilit... |
Cheng_Temporal_Sequence_Modeling_2014_CVPR_paper | Temporal Sequence Modeling for Video Event Detection | [
"Yu Cheng",
"Quanfu Fan",
"Sharath Pankanti",
"Alok Choudhary"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Cheng_Temporal_Sequence_Modeling_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Cheng_Temporal_Sequence_Modeling_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Cheng_2014_CVPR,author = {Cheng, Yu and Fan, Quanfu and Pankanti, Sharath and Choudhary, Alok},title = {Temporal Sequence Modeling for Video Event Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | We present a novel approach for event detection in video by temporal sequence modeling. Exploiting temporal information has lain at the core of many approaches for video analysis (i.e., action, activity and event recognition). Unlike previous works doing temporal modeling at semantic event level, we propose to model te... |
Bhattacharya_Recognition_of_Complex_2014_CVPR_paper | Recognition of Complex Events: Exploiting Temporal Dynamics between Underlying Concepts | [
"Subhabrata Bhattacharya",
"Mahdi M. Kalayeh",
"Rahul Sukthankar",
"Mubarak Shah"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Bhattacharya_Recognition_of_Complex_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Bhattacharya_Recognition_of_Complex_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Bhattacharya_2014_CVPR,author = {Bhattacharya, Subhabrata and Kalayeh, Mahdi M. and Sukthankar, Rahul and Shah, Mubarak},title = {Recognition of Complex Events: Exploiting Temporal Dynamics between Underlying Concepts},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recogni... | While approaches based on bags of features excel at low-level action classification, they are ill-suited for recognizing complex events in video, where concept-based temporal representations currently dominate. This paper proposes a novel representation that captures the temporal dynamics of windowed mid-level concept ... |
Lai_Video_Event_Detection_2014_CVPR_paper | Video Event Detection by Inferring Temporal Instance Labels | [
"Kuan-Ting Lai",
"Felix X. Yu",
"Ming-Syan Chen",
"Shih-Fu Chang"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Lai_Video_Event_Detection_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Lai_Video_Event_Detection_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Lai_2014_CVPR,author = {Lai, Kuan-Ting and Yu, Felix X. and Chen, Ming-Syan and Chang, Shih-Fu},title = {Video Event Detection by Inferring Temporal Instance Labels},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Video event detection allows intelligent indexing of video content based on events. Traditional approaches extract features from video frames or shots, then quantize and pool the features to form a single vector representation for the entire video. Though simple and efficient, the final pooling step may lead to loss of... |
Tsiotsios_Backscatter_Compensated_Photometric_2014_CVPR_paper | Backscatter Compensated Photometric Stereo with 3 Sources | [
"Chourmouzios Tsiotsios",
"Maria E. Angelopoulou",
"Tae-Kyun Kim",
"Andrew J. Davison"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Tsiotsios_Backscatter_Compensated_Photometric_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Tsiotsios_Backscatter_Compensated_Photometric_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Tsiotsios_2014_CVPR,author = {Tsiotsios, Chourmouzios and Angelopoulou, Maria E. and Kim, Tae-Kyun and Davison, Andrew J.},title = {Backscatter Compensated Photometric Stereo with 3 Sources},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},... | Photometric stereo offers the possibility of object shape reconstruction via reasoning about the amount of light reflected from oriented surfaces. However, in murky media such as sea water, the illuminating light interacts with the medium and some of it is backscattered towards the camera. Due to this additive light co... |
Park_Calibrating_a_Non-isotropic_2014_CVPR_paper | Calibrating a Non-isotropic Near Point Light Source using a Plane | [
"Jaesik Park",
"Sudipta N. Sinha",
"Yasuyuki Matsushita",
"Yu-Wing Tai",
"In So Kweon"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Park_Calibrating_a_Non-isotropic_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Park_Calibrating_a_Non-isotropic_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Park_2014_CVPR,author = {Park, Jaesik and Sinha, Sudipta N. and Matsushita, Yasuyuki and Tai, Yu-Wing and So Kweon, In},title = {Calibrating a Non-isotropic Near Point Light Source using a Plane},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {J... | We show that a non-isotropic near point light source rigidly attached to a camera can be calibrated using multiple images of a weakly textured planar scene. We prove that if the radiant intensity distribution (RID) of a light source is radially symmetric with respect to its dominant direction, then the shading observed... |
Shiradkar_A_New_Perspective_2014_CVPR_paper | A New Perspective on Material Classification and Ink Identification | [
"Rakesh Shiradkar",
"Li Shen",
"George Landon",
"Sim Heng Ong",
"Ping Tan"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Shiradkar_A_New_Perspective_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Shiradkar_A_New_Perspective_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Shiradkar_2014_CVPR,author = {Shiradkar, Rakesh and Shen, Li and Landon, George and Heng Ong, Sim and Tan, Ping},title = {A New Perspective on Material Classification and Ink Identification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},... | The surface bi-directional reflectance distribution function (BRDF) can be used to distinguish different materials. The BRDFs of many real materials are near isotropic and can be approximated well by a 2D function. When the camera principal axis is coincident with the surface normal of the material sample, the captured... |
Haque_High_Quality_Photometric_2014_CVPR_paper | High Quality Photometric Reconstruction using a Depth Camera | [
"Sk. Mohammadul Haque",
"Avishek Chatterjee",
"Venu Madhav Govindu"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Haque_High_Quality_Photometric_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Haque_High_Quality_Photometric_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Haque_2014_CVPR,author = {Mohammadul Haque, Sk. and Chatterjee, Avishek and Madhav Govindu, Venu},title = {High Quality Photometric Reconstruction using a Depth Camera},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | In this paper we present a depth-guided photometric 3D reconstruction method that works solely with a depth camera like the Kinect. Existing methods that fuse depth with normal estimates use an external RGB camera to obtain photometric information and treat the depth camera as a black box that provides a low quality de... |
Badri_Robust_Surface_Reconstruction_2014_CVPR_paper | Robust Surface Reconstruction via Triple Sparsity | [
"Hicham Badri",
"Hussein Yahia",
"Driss Aboutajdine"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Badri_Robust_Surface_Reconstruction_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Badri_Robust_Surface_Reconstruction_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Badri_2014_CVPR,author = {Badri, Hicham and Yahia, Hussein and Aboutajdine, Driss},title = {Robust Surface Reconstruction via Triple Sparsity},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Reconstructing a surface/image from corrupted gradient fields is a crucial step in many imaging applications where a gradient field is subject to both noise and unlocalized outliers, resulting typically in a non-integrable field. We present in this paper a new optimization method for robust surface reconstruction. Th... |
Dong_Scattering_Parameters_and_2014_CVPR_paper | Scattering Parameters and Surface Normals from Homogeneous Translucent Materials using Photometric Stereo | [
"Bo Dong",
"Kathleen D. Moore",
"Weiyi Zhang",
"Pieter Peers"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Dong_Scattering_Parameters_and_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Dong_Scattering_Parameters_and_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Dong_2014_CVPR,author = {Dong, Bo and Moore, Kathleen D. and Zhang, Weiyi and Peers, Pieter},title = {Scattering Parameters and Surface Normals from Homogeneous Translucent Materials using Photometric Stereo},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR... | This paper proposes a novel photometric stereo solution to jointly estimate surface normals and scattering parameters from a globally planar, homogeneous, translucent object. Similar to classic photometric stereo, our method only requires as few as three observations of the translucent object under directional lightin... |
El-Melegy_Better_Shading_for_2014_CVPR_paper | Better Shading for Better Shape Recovery | [
"Moumen T. El-Melegy",
"Aly S. Abdelrahim",
"Aly A. Farag"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/El-Melegy_Better_Shading_for_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/El-Melegy_Better_Shading_for_2014_CVPR_paper.pdf | null | null | null | @InProceedings{El-Melegy_2014_CVPR,author = {El-Melegy, Moumen T. and Abdelrahim, Aly S. and Farag, Aly A.},title = {Better Shading for Better Shape Recovery},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | The basic idea of shape from shading is to infer the shape of a surface from its shading information in a single image. Since this problem is ill-posed, a number of simplifying assumptions have been often used. However they rarely hold in practice. This paper presents a simple shading-correction algorithm that transfor... |
Hu_Stable_and_Informative_2014_CVPR_paper | Stable and Informative Spectral Signatures for Graph Matching | [
"Nan Hu",
"Raif M. Rustamov",
"Leonidas Guibas"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Hu_Stable_and_Informative_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Hu_Stable_and_Informative_2014_CVPR_paper.pdf | null | 1304.1572 | title_snapshot | @InProceedings{Hu_2014_CVPR,author = {Hu, Nan and Rustamov, Raif M. and Guibas, Leonidas},title = {Stable and Informative Spectral Signatures for Graph Matching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | In this paper, we consider the approximate weighted graph matching problem and introduce stable and informative first and second order compatibility terms suitable for inclusion into the popular integer quadratic program formulation. Our approach relies on a rigorous analysis of stability of spectral signatures based o... |
Liu_Deformable_Object_Matching_2014_CVPR_paper | Deformable Object Matching via Deformation Decomposition based 2D Label MRF | [
"Kangwei Liu",
"Junge Zhang",
"Kaiqi Huang",
"Tieniu Tan"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Liu_Deformable_Object_Matching_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Liu_Deformable_Object_Matching_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Liu_2014_CVPR,author = {Liu, Kangwei and Zhang, Junge and Huang, Kaiqi and Tan, Tieniu},title = {Deformable Object Matching via Deformation Decomposition based 2D Label MRF},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Deformable object matching, which is also called elastic matching or deformation matching, is an important and challenging problem in computer vision. Although numerous deformation models have been proposed in different matching tasks, not many of them investigate the intrinsic physics underlying deformation. Due to th... |
Kalantidis_Locally_Optimized_Product_2014_CVPR_paper | Locally Optimized Product Quantization for Approximate Nearest Neighbor Search | [
"Yannis Kalantidis",
"Yannis Avrithis"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Kalantidis_Locally_Optimized_Product_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Kalantidis_Locally_Optimized_Product_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Kalantidis_2014_CVPR,author = {Kalantidis, Yannis and Avrithis, Yannis},title = {Locally Optimized Product Quantization for Approximate Nearest Neighbor Search},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | We present a simple vector quantizer that combines low distortion with fast search and apply it to approximate nearest neighbor (ANN) search in high dimensional spaces. Leveraging the very same data structure that is used to provide non-exhaustive search, i.e., inverted lists or a multi-index, the idea is to locally op... |
Ouyang_Multi-source_Deep_Learning_2014_CVPR_paper | Multi-source Deep Learning for Human Pose Estimation | [
"Wanli Ouyang",
"Xiao Chu",
"Xiaogang Wang"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Ouyang_Multi-source_Deep_Learning_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Ouyang_Multi-source_Deep_Learning_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Ouyang_2014_CVPR,author = {Ouyang, Wanli and Chu, Xiao and Wang, Xiaogang},title = {Multi-source Deep Learning for Human Pose Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Visual appearance score, appearance mixture type and deformation are three important information sources for human pose estimation. This paper proposes to build a multi-source deep model in order to extract non-linear representation from these different aspects of information sources. With the deep model, the global, ... |
Pons-Moll_Posebits_for_Monocular_2014_CVPR_paper | Posebits for Monocular Human Pose Estimation | [
"Gerard Pons-Moll",
"David J. Fleet",
"Bodo Rosenhahn"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Pons-Moll_Posebits_for_Monocular_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Pons-Moll_Posebits_for_Monocular_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Pons-Moll_2014_CVPR,author = {Pons-Moll, Gerard and Fleet, David J. and Rosenhahn, Bodo},title = {Posebits for Monocular Human Pose Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | We advocate the inference of qualitative information about 3D human pose, called posebits, from images. Posebits represent boolean geometric relationships between body parts (e.g., left-leg in front of right-leg or hands close to each other). The advantages of posebits as a mid-level representation are 1) for many... |
Ye_Real-time_Simultaneous_Pose_2014_CVPR_paper | Real-time Simultaneous Pose and Shape Estimation for Articulated Objects Using a Single Depth Camera | [
"Mao Ye",
"Ruigang Yang"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Ye_Real-time_Simultaneous_Pose_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Ye_Real-time_Simultaneous_Pose_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Ye_2014_CVPR,author = {Ye, Mao and Yang, Ruigang},title = {Real-time Simultaneous Pose and Shape Estimation for Articulated Objects Using a Single Depth Camera},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | In this paper we present a novel real-time algorithm for simultaneous pose and shape estimation for articulated objects, such as human beings and animals. The key of our pose estimation component is to embed the articulated deformation model with exponential-maps-based parametrization into a Gaussian Mixture Model. Ben... |
Cherian_Mixing_Body-Part_Sequences_2014_CVPR_paper | Mixing Body-Part Sequences for Human Pose Estimation | [
"Anoop Cherian",
"Julien Mairal",
"Karteek Alahari",
"Cordelia Schmid"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Cherian_Mixing_Body-Part_Sequences_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Cherian_Mixing_Body-Part_Sequences_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Cherian_2014_CVPR,author = {Cherian, Anoop and Mairal, Julien and Alahari, Karteek and Schmid, Cordelia},title = {Mixing Body-Part Sequences for Human Pose Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | In this paper, we present a method for estimating articulated human poses in videos. We cast this as an optimization problem defined on body parts with spatio-temporal links between them. The resulting formulation is unfortunately intractable and previous approaches only provide approximate solutions. Although such met... |
Wang_Robust_Estimation_of_2014_CVPR_paper | Robust Estimation of 3D Human Poses from a Single Image | [
"Chunyu Wang",
"Yizhou Wang",
"Zhouchen Lin",
"Alan L. Yuille",
"Wen Gao"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Wang_Robust_Estimation_of_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Wang_Robust_Estimation_of_2014_CVPR_paper.pdf | null | 1406.2282 | title_snapshot | @InProceedings{Wang_2014_CVPR,author = {Wang, Chunyu and Wang, Yizhou and Lin, Zhouchen and Yuille, Alan L. and Gao, Wen},title = {Robust Estimation of 3D Human Poses from a Single Image},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | Human pose estimation is a key step to action recognition. We propose a method of estimating 3D human poses from a single image, which works in conjunction with an existing 2D pose/joint detector. 3D pose estimation is challenging because multiple 3D poses may correspond to the same 2D pose after projection due to the ... |
Sande_Fisher_and_VLAD_2014_CVPR_paper | Fisher and VLAD with FLAIR | [
"Koen E. A. van de Sande",
"Cees G. M. Snoek",
"Arnold W. M. Smeulders"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Sande_Fisher_and_VLAD_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Sande_Fisher_and_VLAD_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Sande_2014_CVPR,author = {van de Sande, Koen E. A. and Snoek, Cees G. M. and Smeulders, Arnold W. M.},title = {Fisher and VLAD with FLAIR},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | A major computational bottleneck in many current algorithms is the evaluation of arbitrary boxes. Dense local analysis and powerful bag-of-word encodings, such as Fisher vectors and VLAD, lead to improved accuracy at the expense of increased computation time. Where a simplification in the representation is tempting, we... |
Aytar_Immediate_Scalable_Object_2014_CVPR_paper | Immediate, Scalable Object Category Detection | [
"Yusuf Aytar",
"Andrew Zisserman"
] | https://openaccess.thecvf.com/content_cvpr_2014/html/Aytar_Immediate_Scalable_Object_2014_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2014/papers/Aytar_Immediate_Scalable_Object_2014_CVPR_paper.pdf | null | null | null | @InProceedings{Aytar_2014_CVPR,author = {Aytar, Yusuf and Zisserman, Andrew},title = {Immediate, Scalable Object Category Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}} | The objective of this work is object category detection in large scale image datasets in the manner of Video Google — an object category is specified by a HOG classifier template, and retrieval is immediate at run time. We make the following three contributions: (i) a new image representation based on mid-level discrim... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.