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title string | authors string | abstract string | pdf_path string | bibtex string | download_url string |
|---|---|---|---|---|---|
Hyperbolic Harmonic Mapping for Constrained Brain Surface Registration | Rui Shi, Wei Zeng, Zhengyu Su, Hanna Damasio, Zhonglin Lu, Yalin Wang, Shing-Tung Yau, Xianfeng Gu | Automatic computation of surface correspondence via harmonic map is an active research field in computer vision, computer graphics and computational geometry. It may help document and understand physical and biological phenomena and also has broad applications in biometrics, medical imaging and motion capture. Although... | 2013/Shi_Hyperbolic_Harmonic_Mapping_2013_CVPR_paper.pdf | @InProceedings{Shi_2013_ICCV_Workshops,author = {Shi, Rui and Zeng, Wei and Su, Zhengyu and Damasio, Hanna and Lu, Zhonglin and Wang, Yalin and Yau, Shing-Tung and Gu, Xianfeng},title = {Hyperbolic Harmonic Mapping for Constrained Brain Surface Registration},booktitle = {Proceedings of the IEEE Conference on Computer V... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Shi_Hyperbolic_Harmonic_Mapping_2013_CVPR_paper.pdf |
Exploring Compositional High Order Pattern Potentials for Structured Output Learning | Yujia Li, Daniel Tarlow, Richard Zemel | When modeling structured outputs such as image segmentations, prediction can be improved by accurately modeling structure present in the labels. A key challenge is developing tractable models that are able to capture complex high level structure like shape. In this work, we study the learning of a general class of patt... | 2013/Li_Exploring_Compositional_High_2013_CVPR_paper.pdf | @InProceedings{Li_2013_ICCV_Workshops,author = {Li, Yujia and Tarlow, Daniel and Zemel, Richard},title = {Exploring Compositional High Order Pattern Potentials for Structured Output Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Li_Exploring_Compositional_High_2013_CVPR_paper.pdf |
Dense Variational Reconstruction of Non-rigid Surfaces from Monocular Video | Ravi Garg, Anastasios Roussos, Lourdes Agapito | This paper offers the first variational approach to the problem of dense 3D reconstruction of non-rigid surfaces from a monocular video sequence. We formulate nonrigid structure from motion ( NRS f M ) as a global variational energy minimization problem to estimate dense low-rank smooth 3D shapes for every frame along ... | 2013/Garg_Dense_Variational_Reconstruction_2013_CVPR_paper.pdf | @InProceedings{Garg_2013_ICCV_Workshops,author = {Garg, Ravi and Roussos, Anastasios and Agapito, Lourdes},title = {Dense Variational Reconstruction of Non-rigid Surfaces from Monocular Video},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Garg_Dense_Variational_Reconstruction_2013_CVPR_paper.pdf |
A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles | Dror Sholomon, Omid David, Nathan S. Netanyahu | In this paper we propose the first effective automated, genetic algorithm (GA)-based jigsaw puzzle solver. We introduce a novel procedure of merging two "parent" solutions to an improved "child" solution by detecting, extracting, and combining correctly assembled puzzle segments. The solver proposed exhibits state-of-t... | 2013/Sholomon_A_Genetic_Algorithm-Based_2013_CVPR_paper.pdf | @InProceedings{Sholomon_2013_ICCV_Workshops,author = {Sholomon, Dror and David, Omid and Netanyahu, Nathan S.},title = {A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Sholomon_A_Genetic_Algorithm-Based_2013_CVPR_paper.pdf |
Deformable Spatial Pyramid Matching for Fast Dense Correspondences | Jaechul Kim, Ce Liu, Fei Sha, Kristen Grauman | We introduce a fast deformable spatial pyramid (DSP) matching algorithm for computing dense pixel correspondences. Dense matching methods typically enforce both appearance agreement between matched pixels as well as geometric smoothness between neighboring pixels. Whereas the prevailing approaches operate at the pixel ... | 2013/Kim_Deformable_Spatial_Pyramid_2013_CVPR_paper.pdf | @InProceedings{Kim_2013_ICCV_Workshops,author = {Kim, Jaechul and Liu, Ce and Sha, Fei and Grauman, Kristen},title = {Deformable Spatial Pyramid Matching for Fast Dense Correspondences},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Kim_Deformable_Spatial_Pyramid_2013_CVPR_paper.pdf |
Fusing Depth from Defocus and Stereo with Coded Apertures | Yuichi Takeda, Shinsaku Hiura, Kosuke Sato | In this paper we propose a novel depth measurement method by fusing depth from defocus (DFD) and stereo. One of the problems of passive stereo method is the difficulty of finding correct correspondence between images when an object has a repetitive pattern or edges parallel to the epipolar line. On the other hand, the ... | 2013/Takeda_Fusing_Depth_from_2013_CVPR_paper.pdf | @InProceedings{Takeda_2013_ICCV_Workshops,author = {Takeda, Yuichi and Hiura, Shinsaku and Sato, Kosuke},title = {Fusing Depth from Defocus and Stereo with Coded Apertures},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Takeda_Fusing_Depth_from_2013_CVPR_paper.pdf |
A Non-parametric Framework for Document Bleed-through Removal | Roisin Rowley-Brooke, Francois Pitie, Anil Kokaram | This paper presents recent work on a new framework for non-blind document bleed-through removal. The framework includes image preprocessing to remove local intensity variations, pixel region classification based on a segmentation of the joint recto-verso intensity histogram and connected component analysis on the subse... | 2013/Rowley-Brooke_A_Non-parametric_Framework_2013_CVPR_paper.pdf | @InProceedings{Rowley-Brooke_2013_ICCV_Workshops,author = {Rowley-Brooke, Roisin and Pitie, Francois and Kokaram, Anil},title = {A Non-parametric Framework for Document Bleed-through Removal},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Rowley-Brooke_A_Non-parametric_Framework_2013_CVPR_paper.pdf |
Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera | Lu Xia, J.K. Aggarwal | Local spatio-temporal interest points (STIPs) and the resulting features from RGB videos have been proven successful at activity recognition that can handle cluttered backgrounds and partial occlusions. In this paper, we propose its counterpart in depth video and show its efficacy on activity recognition. We present a ... | 2013/Xia_Spatio-temporal_Depth_Cuboid_2013_CVPR_paper.pdf | @InProceedings{Xia_2013_ICCV_Workshops,author = {Xia, Lu and Aggarwal, J.K.},title = {Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Xia_Spatio-temporal_Depth_Cuboid_2013_CVPR_paper.pdf |
A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems | J. Kappes, B. Andres, F. Hamprecht, C. Schnorr, S. Nowozin, D. Batra, S. Kim, B. Kausler, J. Lellmann, N. Komodakis, C. Rother | Seven years ago, Szeliski et al. published an influential study on energy minimization methods for Markov random fields (MRF). This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenominal success of ... | 2013/Kappes_A_Comparative_Study_2013_CVPR_paper.pdf | @InProceedings{Kappes_2013_ICCV_Workshops,author = {Kappes, J. and Andres, B. and Hamprecht, F. and Schnorr, C. and Nowozin, S. and Batra, D. and Kim, S. and Kausler, B. and Lellmann, J. and Komodakis, N. and Rother, C.},title = {A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Proble... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Kappes_A_Comparative_Study_2013_CVPR_paper.pdf |
Submodular Salient Region Detection | Zhuolin Jiang, Larry S. Davis | The problem of salient region detection is formulated as the well-studied facility location problem from operations research. High-level priors are combined with low-level features to detect salient regions. Salient region detection is achieved by maximizing a submodular objective function, which maximizes the total si... | 2013/Jiang_Submodular_Salient_Region_2013_CVPR_paper.pdf | @InProceedings{Jiang_2013_ICCV_Workshops,author = {Jiang, Zhuolin and Davis, Larry S.},title = {Submodular Salient Region Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Jiang_Submodular_Salient_Region_2013_CVPR_paper.pdf |
Fast Multiple-Part Based Object Detection Using KD-Ferns | Dan Levi, Shai Silberstein, Aharon Bar-Hillel | In this work we present a new part-based object detection algorithm with hundreds of parts performing realtime detection. Part-based models are currently state-ofthe-art for object detection due to their ability to represent large appearance variations. However, due to their high computational demands such methods are ... | 2013/Levi_Fast_Multiple-Part_Based_2013_CVPR_paper.pdf | @InProceedings{Levi_2013_ICCV_Workshops,author = {Levi, Dan and Silberstein, Shai and Bar-Hillel, Aharon},title = {Fast Multiple-Part Based Object Detection Using KD-Ferns},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Levi_Fast_Multiple-Part_Based_2013_CVPR_paper.pdf |
Bringing Semantics into Focus Using Visual Abstraction | C. L. Zitnick, Devi Parikh | Relating visual information to its linguistic semantic meaning remains an open and challenging area of research. The semantic meaning of images depends on the presence of objects, their attributes and their relations to other objects. But precisely characterizing this dependence requires extracting complex visual infor... | 2013/Zitnick_Bringing_Semantics_into_2013_CVPR_paper.pdf | @InProceedings{Zitnick_2013_ICCV_Workshops,author = {Zitnick, C. L. and Parikh, Devi},title = {Bringing Semantics into Focus Using Visual Abstraction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Zitnick_Bringing_Semantics_into_2013_CVPR_paper.pdf |
Wide-Baseline Hair Capture Using Strand-Based Refinement | Linjie Luo, Cha Zhang, Zhengyou Zhang, Szymon Rusinkiewicz | We propose a novel algorithm to reconstruct the 3D geometry of human hairs in wide-baseline setups using strand-based refinement. The hair strands are first extracted in each 2D view, and projected onto the 3D visual hull for initialization. The 3D positions of these strands are then refined by optimizing an objective ... | 2013/Luo_Wide-Baseline_Hair_Capture_2013_CVPR_paper.pdf | @InProceedings{Luo_2013_ICCV_Workshops,author = {Luo, Linjie and Zhang, Cha and Zhang, Zhengyou and Rusinkiewicz, Szymon},title = {Wide-Baseline Hair Capture Using Strand-Based Refinement},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Luo_Wide-Baseline_Hair_Capture_2013_CVPR_paper.pdf |
Radial Distortion Self-Calibration | Jose Henrique Brito, Roland Angst, Kevin Koser, Marc Pollefeys | In cameras with radial distortion, straight lines in space are in general mapped to curves in the image. Although epipolar geometry also gets distorted, there is a set of special epipolar lines that remain straight, namely those that go through the distortion center. By finding these straight epipolar lines in camera p... | 2013/Brito_Radial_Distortion_Self-Calibration_2013_CVPR_paper.pdf | @InProceedings{Brito_2013_ICCV_Workshops,author = {Henrique Brito, Jose and Angst, Roland and Koser, Kevin and Pollefeys, Marc},title = {Radial Distortion Self-Calibration},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Brito_Radial_Distortion_Self-Calibration_2013_CVPR_paper.pdf |
Computing Diffeomorphic Paths for Large Motion Interpolation | Dohyung Seo, Jeffrey Ho, Baba C. Vemuri | In this paper, we introduce a novel framework for computing a path of diffeomorphisms between a pair of input diffeomorphisms. Direct computation of a geodesic path on the space of diffeomorphisms Diff(?) is difficult, and it can be attributed mainly to the infinite dimensionality of Diff(?). Our proposed framework, to... | 2013/Seo_Computing_Diffeomorphic_Paths_2013_CVPR_paper.pdf | @InProceedings{Seo_2013_ICCV_Workshops,author = {Seo, Dohyung and Ho, Jeffrey and Vemuri, Baba C.},title = {Computing Diffeomorphic Paths for Large Motion Interpolation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Seo_Computing_Diffeomorphic_Paths_2013_CVPR_paper.pdf |
Separating Signal from Noise Using Patch Recurrence across Scales | Maria Zontak, Inbar Mosseri, Michal Irani | Recurrence of small clean image patches across different scales of a natural image has been successfully used for solving ill-posed problems in clean images (e.g., superresolution from a single image). In this paper we show how this multi-scale property can be extended to solve ill-posed problems under noisy conditions... | 2013/Zontak_Separating_Signal_from_2013_CVPR_paper.pdf | @InProceedings{Zontak_2013_ICCV_Workshops,author = {Zontak, Maria and Mosseri, Inbar and Irani, Michal},title = {Separating Signal from Noise Using Patch Recurrence across Scales},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Zontak_Separating_Signal_from_2013_CVPR_paper.pdf |
Detection Evolution with Multi-order Contextual Co-occurrence | Guang Chen, Yuanyuan Ding, Jing Xiao, Tony X. Han | Context has been playing an increasingly important role to improve the object detection performance. In this paper we propose an effective representation, Multi-Order Contextual co-Occurrence (MOCO), to implicitly model the high level context using solely detection responses from a baseline object detector. The so-call... | 2013/Chen_Detection_Evolution_with_2013_CVPR_paper.pdf | @InProceedings{Chen_2013_ICCV_Workshops,author = {Chen, Guang and Ding, Yuanyuan and Xiao, Jing and Han, Tony X.},title = {Detection Evolution with Multi-order Contextual Co-occurrence},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Chen_Detection_Evolution_with_2013_CVPR_paper.pdf |
Manhattan Scene Understanding via XSlit Imaging | Jinwei Ye, Yu Ji, Jingyi Yu | A Manhattan World (MW) [3] is composed of planar surfaces and parallel lines aligned with three mutually orthogonal principal axes. Traditional MW understanding algorithms rely on geometry priors such as the vanishing points and reference (ground) planes for grouping coplanar structures. In this paper, we present a nov... | 2013/Ye_Manhattan_Scene_Understanding_2013_CVPR_paper.pdf | @InProceedings{Ye_2013_ICCV_Workshops,author = {Ye, Jinwei and Ji, Yu and Yu, Jingyi},title = {Manhattan Scene Understanding via XSlit Imaging},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Ye_Manhattan_Scene_Understanding_2013_CVPR_paper.pdf |
Tensor-Based High-Order Semantic Relation Transfer for Semantic Scene Segmentation | Heesoo Myeong, Kyoung Mu Lee | We propose a novel nonparametric approach for semantic segmentation using high-order semantic relations. Conventional context models mainly focus on learning pairwise relationships between objects. Pairwise relations, however, are not enough to represent high-level contextual knowledge within images. In this paper, we ... | 2013/Myeong_Tensor-Based_High-Order_Semantic_2013_CVPR_paper.pdf | @InProceedings{Myeong_2013_ICCV_Workshops,author = {Myeong, Heesoo and Mu Lee, Kyoung},title = {Tensor-Based High-Order Semantic Relation Transfer for Semantic Scene Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Myeong_Tensor-Based_High-Order_Semantic_2013_CVPR_paper.pdf |
Cumulative Attribute Space for Age and Crowd Density Estimation | Ke Chen, Shaogang Gong, Tao Xiang, Chen Change Loy | A number of computer vision problems such as human age estimation, crowd density estimation and body/face pose (view angle) estimation can be formulated as a regression problem by learning a mapping function between a high dimensional vector-formed feature input and a scalarvalued output. Such a learning problem is mad... | 2013/Chen_Cumulative_Attribute_Space_2013_CVPR_paper.pdf | @InProceedings{Chen_2013_ICCV_Workshops,author = {Chen, Ke and Gong, Shaogang and Xiang, Tao and Change Loy, Chen},title = {Cumulative Attribute Space for Age and Crowd Density Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Chen_Cumulative_Attribute_Space_2013_CVPR_paper.pdf |
Accurate and Robust Registration of Nonrigid Surface Using Hierarchical Statistical Shape Model | Hidekata Hontani, Yuto Tsunekawa, Yoshihide Sawada | In this paper, we propose a new non-rigid robust registration method that registers a point distribution model (PDM) of a surface to given 3D images. The contributions of the paper are (1) a new hierarchical statistical shape model (SSM) of the surface that has better generalization ability is introduced, (2) the regis... | 2013/Hontani_Accurate_and_Robust_2013_CVPR_paper.pdf | @InProceedings{Hontani_2013_ICCV_Workshops,author = {Hontani, Hidekata and Tsunekawa, Yuto and Sawada, Yoshihide},title = {Accurate and Robust Registration of Nonrigid Surface Using Hierarchical Statistical Shape Model},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},m... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Hontani_Accurate_and_Robust_2013_CVPR_paper.pdf |
POOF: Part-Based One-vs.-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation | Thomas Berg, Peter N. Belhumeur | From a set of images in a particular domain, labeled with part locations and class, we present a method to automatically learn a large and diverse set of highly discriminative intermediate features that we call Part-based One-vs-One Features (POOFs). Each of these features specializes in discrimination between two part... | 2013/Berg_POOF_Part-Based_One-vs.-One_2013_CVPR_paper.pdf | @InProceedings{Berg_2013_ICCV_Workshops,author = {Berg, Thomas and Belhumeur, Peter N.},title = {POOF: Part-Based One-vs.-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = ... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Berg_POOF_Part-Based_One-vs.-One_2013_CVPR_paper.pdf |
Sparse Quantization for Patch Description | Xavier Boix, Michael Gygli, Gemma Roig, Luc Van Gool | The representation of local image patches is crucial for the good performance and efficiency of many vision tasks. Patch descriptors have been designed to generalize towards diverse variations, depending on the application, as well as the desired compromise between accuracy and efficiency. We present a novel formulatio... | 2013/Boix_Sparse_Quantization_for_2013_CVPR_paper.pdf | @InProceedings{Boix_2013_ICCV_Workshops,author = {Boix, Xavier and Gygli, Michael and Roig, Gemma and Van Gool, Luc},title = {Sparse Quantization for Patch Description},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Boix_Sparse_Quantization_for_2013_CVPR_paper.pdf |
Context-Aware Modeling and Recognition of Activities in Video | Yingying Zhu, Nandita M. Nayak, Amit K. Roy-Chowdhury | In this paper, rather than modeling activities in videos individually, we propose a hierarchical framework that jointly models and recognizes related activities using motion and various context features. This is motivated from the observations that the activities related in space and time rarely occur independently and... | 2013/Zhu_Context-Aware_Modeling_and_2013_CVPR_paper.pdf | @InProceedings{Zhu_2013_ICCV_Workshops,author = {Zhu, Yingying and Nayak, Nandita M. and Roy-Chowdhury, Amit K.},title = {Context-Aware Modeling and Recognition of Activities in Video},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Zhu_Context-Aware_Modeling_and_2013_CVPR_paper.pdf |
Learning to Detect Partially Overlapping Instances | Carlos Arteta, Victor Lempitsky, J. A. Noble, Andrew Zisserman | The objective of this work is to detect all instances of a class (such as cells or people) in an image. The instances may be partially overlapping and clustered, and hence quite challenging for traditional detectors, which aim at localizing individual instances. Our approach is to propose a set of candidate regions, an... | 2013/Arteta_Learning_to_Detect_2013_CVPR_paper.pdf | @InProceedings{Arteta_2013_ICCV_Workshops,author = {Arteta, Carlos and Lempitsky, Victor and Noble, J. A. and Zisserman, Andrew},title = {Learning to Detect Partially Overlapping Instances},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Arteta_Learning_to_Detect_2013_CVPR_paper.pdf |
What's in a Name? First Names as Facial Attributes | Huizhong Chen, Andrew C. Gallagher, Bernd Girod | This paper introduces a new idea in describing people using their first names, i.e., the name assigned at birth. We show that describing people in terms of similarity to a vector of possible first names is a powerful description of facial appearance that can be used for face naming and building facial attribute classif... | 2013/Chen_Whats_in_a_2013_CVPR_paper.pdf | @InProceedings{Chen_2013_ICCV_Workshops,author = {Chen, Huizhong and Gallagher, Andrew C. and Girod, Bernd},title = {What's in a Name? First Names as Facial Attributes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Chen_Whats_in_a_2013_CVPR_paper.pdf |
Exemplar-Based Face Parsing | Brandon M. Smith, Li Zhang, Jonathan Brandt, Zhe Lin, Jianchao Yang | In this work, we propose an exemplar-based face image segmentation algorithm. We take inspiration from previous works on image parsing for general scenes. Our approach assumes a database of exemplar face images, each of which is associated with a hand-labeled segmentation map. Given a test image, our algorithm first se... | 2013/Smith_Exemplar-Based_Face_Parsing_2013_CVPR_paper.pdf | @InProceedings{Smith_2013_ICCV_Workshops,author = {Smith, Brandon M. and Zhang, Li and Brandt, Jonathan and Lin, Zhe and Yang, Jianchao},title = {Exemplar-Based Face Parsing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Smith_Exemplar-Based_Face_Parsing_2013_CVPR_paper.pdf |
Optimized Product Quantization for Approximate Nearest Neighbor Search | Tiezheng Ge, Kaiming He, Qifa Ke, Jian Sun | Product quantization is an effective vector quantization approach to compactly encode high-dimensional vectors for fast approximate nearest neighbor (ANN) search. The essence of product quantization is to decompose the original high-dimensional space into the Cartesian product of a finite number of low-dimensional subs... | 2013/Ge_Optimized_Product_Quantization_2013_CVPR_paper.pdf | @InProceedings{Ge_2013_ICCV_Workshops,author = {Ge, Tiezheng and He, Kaiming and Ke, Qifa and Sun, Jian},title = {Optimized Product Quantization for Approximate Nearest Neighbor Search},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Ge_Optimized_Product_Quantization_2013_CVPR_paper.pdf |
Multipath Sparse Coding Using Hierarchical Matching Pursuit | Liefeng Bo, Xiaofeng Ren, Dieter Fox | Complex real-world signals, such as images, contain discriminative structures that differ in many aspects including scale, invariance, and data channel. While progress in deep learning shows the importance of learning features through multiple layers, it is equally important to learn features through multiple paths. We... | 2013/Bo_Multipath_Sparse_Coding_2013_CVPR_paper.pdf | @InProceedings{Bo_2013_ICCV_Workshops,author = {Bo, Liefeng and Ren, Xiaofeng and Fox, Dieter},title = {Multipath Sparse Coding Using Hierarchical Matching Pursuit},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Bo_Multipath_Sparse_Coding_2013_CVPR_paper.pdf |
Visual Tracking via Locality Sensitive Histograms | Shengfeng He, Qingxiong Yang, Rynson W.H. Lau, Jiang Wang, Ming-Hsuan Yang | null | 2013/He_Visual_Tracking_via_2013_CVPR_paper.pdf | @InProceedings{He_2013_ICCV_Workshops,author = {He, Shengfeng and Yang, Qingxiong and Lau, Rynson W.H. and Wang, Jiang and Yang, Ming-Hsuan},title = {Visual Tracking via Locality Sensitive Histograms},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year ... | https://openaccess.thecvf.com/content_cvpr_2013/papers/He_Visual_Tracking_via_2013_CVPR_paper.pdf |
Compressible Motion Fields | Giuseppe Ottaviano, Pushmeet Kohli | null | 2013/Ottaviano_Compressible_Motion_Fields_2013_CVPR_paper.pdf | @InProceedings{Ottaviano_2013_ICCV_Workshops,author = {Ottaviano, Giuseppe and Kohli, Pushmeet},title = {Compressible Motion Fields},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Ottaviano_Compressible_Motion_Fields_2013_CVPR_paper.pdf |
Multi-target Tracking by Lagrangian Relaxation to Min-cost Network Flow | Asad A. Butt, Robert T. Collins | null | 2013/Butt_Multi-target_Tracking_by_2013_CVPR_paper.pdf | @InProceedings{Butt_2013_ICCV_Workshops,author = {Butt, Asad A. and Collins, Robert T.},title = {Multi-target Tracking by Lagrangian Relaxation to Min-cost Network Flow},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Butt_Multi-target_Tracking_by_2013_CVPR_paper.pdf |
Tracking People and Their Objects | Tobias Baumgartner, Dennis Mitzel, Bastian Leibe | null | 2013/Baumgartner_Tracking_People_and_2013_CVPR_paper.pdf | @InProceedings{Baumgartner_2013_ICCV_Workshops,author = {Baumgartner, Tobias and Mitzel, Dennis and Leibe, Bastian},title = {Tracking People and Their Objects},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Baumgartner_Tracking_People_and_2013_CVPR_paper.pdf |
In Defense of 3D-Label Stereo | Carl Olsson, Johannes Ulen, Yuri Boykov | null | 2013/Olsson_In_Defense_of_2013_CVPR_paper.pdf | @InProceedings{Olsson_2013_ICCV_Workshops,author = {Olsson, Carl and Ulen, Johannes and Boykov, Yuri},title = {In Defense of 3D-Label Stereo},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Olsson_In_Defense_of_2013_CVPR_paper.pdf |
Dense Object Reconstruction with Semantic Priors | Sid Yingze Bao, Manmohan Chandraker, Yuanqing Lin, Silvio Savarese | null | 2013/Bao_Dense_Object_Reconstruction_2013_CVPR_paper.pdf | @InProceedings{Bao_2013_ICCV_Workshops,author = {Yingze Bao, Sid and Chandraker, Manmohan and Lin, Yuanqing and Savarese, Silvio},title = {Dense Object Reconstruction with Semantic Priors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Bao_Dense_Object_Reconstruction_2013_CVPR_paper.pdf |
Deformable Graph Matching | Feng Zhou, Fernando De la Torre | Graph matching (GM) is a fundamental problem in computer science, and it has been successfully applied to many problems in computer vision. Although widely used, existing GM algorithms cannot incorporate global consistence among nodes, which is a natural constraint in computer vision problems. This paper proposes defor... | 2013/Zhou_Deformable_Graph_Matching_2013_CVPR_paper.pdf | @InProceedings{Zhou_2013_ICCV_Workshops,author = {Zhou, Feng and De la Torre, Fernando},title = {Deformable Graph Matching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Zhou_Deformable_Graph_Matching_2013_CVPR_paper.pdf |
3D Visual Proxemics: Recognizing Human Interactions in 3D from a Single Image | Ishani Chakraborty, Hui Cheng, Omar Javed | We present a unified framework for detecting and classifying people interactions in unconstrained user generated images. g Unlike previous approaches that directly map people/face locations in 2D image space into features for classification, we first estimate camera viewpoint and people positions in 3D space and then e... | 2013/Chakraborty_3D_Visual_Proxemics_2013_CVPR_paper.pdf | @InProceedings{Chakraborty_2013_ICCV_Workshops,author = {Chakraborty, Ishani and Cheng, Hui and Javed, Omar},title = {3D Visual Proxemics: Recognizing Human Interactions in 3D from a Single Image},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Chakraborty_3D_Visual_Proxemics_2013_CVPR_paper.pdf |
Graph-Based Optimization with Tubularity Markov Tree for 3D Vessel Segmentation | Ning Zhu, Albert C.S. Chung | In this paper, we propose a graph-based method for 3D vessel tree structure segmentation based on a new tubularity Markov tree model (TMT ), which works as both new energy function and graph construction method. With the help of power-watershed implementation [7], a global optimal segmentation can be obtained with low ... | 2013/Zhu_Graph-Based_Optimization_with_2013_CVPR_paper.pdf | @InProceedings{Zhu_2013_ICCV_Workshops,author = {Zhu, Ning and Chung, Albert C.S.},title = {Graph-Based Optimization with Tubularity Markov Tree for 3D Vessel Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Zhu_Graph-Based_Optimization_with_2013_CVPR_paper.pdf |
Dictionary Learning from Ambiguously Labeled Data | Yi-Chen Chen, Vishal M. Patel, Jaishanker K. Pillai, Rama Chellappa, P. J. Phillips | We propose a novel dictionary-based learning method for ambiguously labeled multiclass classification, where each training sample has multiple labels and only one of them is the correct label. The dictionary learning problem is solved using an iterative alternating algorithm. At each iteration of the algorithm, two alt... | 2013/Chen_Dictionary_Learning_from_2013_CVPR_paper.pdf | @InProceedings{Chen_2013_ICCV_Workshops,author = {Chen, Yi-Chen and Patel, Vishal M. and Pillai, Jaishanker K. and Chellappa, Rama and Phillips, P. J.},title = {Dictionary Learning from Ambiguously Labeled Data},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Chen_Dictionary_Learning_from_2013_CVPR_paper.pdf |
Large-Scale Video Summarization Using Web-Image Priors | Aditya Khosla, Raffay Hamid, Chih-Jen Lin, Neel Sundaresan | Given the enormous growth in user-generated videos, it is becoming increasingly important to be able to navigate them efficiently. As these videos are generally of poor quality, summarization methods designed for well-produced videos do not generalize to them. To address this challenge, we propose to use web-images as ... | 2013/Khosla_Large-Scale_Video_Summarization_2013_CVPR_paper.pdf | @InProceedings{Khosla_2013_ICCV_Workshops,author = {Khosla, Aditya and Hamid, Raffay and Lin, Chih-Jen and Sundaresan, Neel},title = {Large-Scale Video Summarization Using Web-Image Priors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Khosla_Large-Scale_Video_Summarization_2013_CVPR_paper.pdf |
Block and Group Regularized Sparse Modeling for Dictionary Learning | Yu-Tseh Chi, Mohsen Ali, Ajit Rajwade, Jeffrey Ho | This paper proposes a dictionary learning framework that combines the proposed block/group (BGSC) or reconstructed block/group (R-BGSC) sparse coding schemes with the novel Intra-block Coherence Suppression Dictionary Learning (ICS-DL) algorithm. An important and distinguishing feature of the proposed framework is that... | 2013/Chi_Block_and_Group_2013_CVPR_paper.pdf | @InProceedings{Chi_2013_ICCV_Workshops,author = {Chi, Yu-Tseh and Ali, Mohsen and Rajwade, Ajit and Ho, Jeffrey},title = {Block and Group Regularized Sparse Modeling for Dictionary Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Chi_Block_and_Group_2013_CVPR_paper.pdf |
Part Discovery from Partial Correspondence | Subhransu Maji, Gregory Shakhnarovich | We study the problem of part discovery when partial correspondence between instances of a category are available. For visual categories that exhibit high diversity in structure such as buildings, our approach can be used to discover parts that are hard to name, but can be easily expressed as a correspondence between pa... | 2013/Maji_Part_Discovery_from_2013_CVPR_paper.pdf | @InProceedings{Maji_2013_ICCV_Workshops,author = {Maji, Subhransu and Shakhnarovich, Gregory},title = {Part Discovery from Partial Correspondence},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Maji_Part_Discovery_from_2013_CVPR_paper.pdf |
Fast Convolutional Sparse Coding | Hilton Bristow, Anders Eriksson, Simon Lucey | Sparse coding has become an increasingly popular method in learning and vision for a variety of classification, reconstruction and coding tasks. The canonical approach intrinsically assumes independence between observations during learning. For many natural signals however, sparse coding is applied to sub-elements ( i.... | 2013/Bristow_Fast_Convolutional_Sparse_2013_CVPR_paper.pdf | @InProceedings{Bristow_2013_ICCV_Workshops,author = {Bristow, Hilton and Eriksson, Anders and Lucey, Simon},title = {Fast Convolutional Sparse Coding},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Bristow_Fast_Convolutional_Sparse_2013_CVPR_paper.pdf |
Alternating Decision Forests | Samuel Schulter, Paul Wohlhart, Christian Leistner, Amir Saffari, Peter M. Roth, Horst Bischof | This paper introduces a novel classification method termed Alternating Decision Forests (ADFs), which formulates the training of Random Forests explicitly as a global loss minimization problem. During training, the losses are minimized via keeping an adaptive weight distribution over the training samples, similar to Bo... | 2013/Schulter_Alternating_Decision_Forests_2013_CVPR_paper.pdf | @InProceedings{Schulter_2013_ICCV_Workshops,author = {Schulter, Samuel and Wohlhart, Paul and Leistner, Christian and Saffari, Amir and Roth, Peter M. and Bischof, Horst},title = {Alternating Decision Forests},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {Ju... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Schulter_Alternating_Decision_Forests_2013_CVPR_paper.pdf |
Compressed Hashing | Yue Lin, Rong Jin, Deng Cai, Shuicheng Yan, Xuelong Li | Recent studies have shown that hashing methods are effective for high dimensional nearest neighbor search. A common problem shared by many existing hashing methods is that in order to achieve a satisfied performance, a large number of hash tables (i.e., long codewords) are required. To address this challenge, in this p... | 2013/Lin_Compressed_Hashing_2013_CVPR_paper.pdf | @InProceedings{Lin_2013_ICCV_Workshops,author = {Lin, Yue and Jin, Rong and Cai, Deng and Yan, Shuicheng and Li, Xuelong},title = {Compressed Hashing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Lin_Compressed_Hashing_2013_CVPR_paper.pdf |
Recognize Human Activities from Partially Observed Videos | Yu Cao, Daniel Barrett, Andrei Barbu, Siddharth Narayanaswamy, Haonan Yu, Aaron Michaux, Yuewei Lin, Sven Dickinson, Jeffrey Mark Siskind, Song Wang | Recognizing human activities in partially observed videos is a challenging problem and has many practical applications. When the unobserved subsequence is at the end of the video, the problem is reduced to activity prediction from unfinished activity streaming, which has been studied by many researchers. However, in th... | 2013/Cao_Recognize_Human_Activities_2013_CVPR_paper.pdf | @InProceedings{Cao_2013_ICCV_Workshops,author = {Cao, Yu and Barrett, Daniel and Barbu, Andrei and Narayanaswamy, Siddharth and Yu, Haonan and Michaux, Aaron and Lin, Yuewei and Dickinson, Sven and Mark Siskind, Jeffrey and Wang, Song},title = {Recognize Human Activities from Partially Observed Videos},booktitle = {Pro... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Cao_Recognize_Human_Activities_2013_CVPR_paper.pdf |
SWIGS: A Swift Guided Sampling Method | Victor Fragoso, Matthew Turk | We present SWIGS, a Swift and efficient Guided Sampling method for robust model estimation from image feature correspondences. Our method leverages the accuracy of our new confidence measure (MR-Rayleigh), which assigns a correctness-confidence to a putative correspondence in an online fashion. MR-Rayleigh is inspired ... | 2013/Fragoso_SWIGS_A_Swift_2013_CVPR_paper.pdf | @InProceedings{Fragoso_2013_ICCV_Workshops,author = {Fragoso, Victor and Turk, Matthew},title = {SWIGS: A Swift Guided Sampling Method},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Fragoso_SWIGS_A_Swift_2013_CVPR_paper.pdf |
Action Recognition by Hierarchical Sequence Summarization | Yale Song, Louis-Philippe Morency, Randall Davis | Recent progress has shown that learning from hierarchical feature representations leads to improvements in various computer vision tasks. Motivated by the observation that human activity data contains information at various temporal resolutions, we present a hierarchical sequence summarization approach for action recog... | 2013/Song_Action_Recognition_by_2013_CVPR_paper.pdf | @InProceedings{Song_2013_ICCV_Workshops,author = {Song, Yale and Morency, Louis-Philippe and Davis, Randall},title = {Action Recognition by Hierarchical Sequence Summarization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Song_Action_Recognition_by_2013_CVPR_paper.pdf |
Maximum Cohesive Grid of Superpixels for Fast Object Localization | Liang Li, Wei Feng, Liang Wan, Jiawan Zhang | This paper addresses a challenging problem of regularizing arbitrary superpixels into an optimal grid structure, which may significantly extend current low-level vision algorithms by allowing them to use superpixels (SPs) conveniently as using pixels. For this purpose, we aim at constructing maximum cohesive SP-grid, w... | 2013/Li_Maximum_Cohesive_Grid_2013_CVPR_paper.pdf | @InProceedings{Li_2013_ICCV_Workshops,author = {Li, Liang and Feng, Wei and Wan, Liang and Zhang, Jiawan},title = {Maximum Cohesive Grid of Superpixels for Fast Object Localization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Li_Maximum_Cohesive_Grid_2013_CVPR_paper.pdf |
A Convex Regularizer for Reducing Color Artifact in Color Image Recovery | Shunsuke Ono, Isao Yamada | We propose a new convex regularizer, named the local color nuclear norm (LCNN), for color image recovery. The LCNN is designed to promote a property inherent in natural color images - in which their local color distributions often exhibit strong linearity - and is thus expected to reduce color artifact effectively. In ... | 2013/Ono_A_Convex_Regularize_2013_CVPR_paper.pdf | @InProceedings{Ono_2013_ICCV_Workshops,author = {Ono, Shunsuke and Yamada, Isao},title = {A Convex Regularizer for Reducing Color Artifact in Color Image Recovery},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Ono_A_Convex_Regularize_2013_CVPR_paper.pdf |
Ensemble Video Object Cut in Highly Dynamic Scenes | Xiaobo Ren, Tony X. Han, Zhihai He | We consider video object cut as an ensemble of framelevel background-foreground object classifiers which fuses information across frames and refine their segmentation results in a collaborative and iterative manner. Our approach addresses the challenging issues of modeling of background with dynamic textures and segmen... | 2013/Ren_Ensemble_Video_Object_2013_CVPR_paper.pdf | @InProceedings{Ren_2013_ICCV_Workshops,author = {Ren, Xiaobo and Han, Tony X. and He, Zhihai},title = {Ensemble Video Object Cut in Highly Dynamic Scenes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Ren_Ensemble_Video_Object_2013_CVPR_paper.pdf |
An Iterated L1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision | Peter Ochs, Alexey Dosovitskiy, Thomas Brox, Thomas Pock | Natural image statistics indicate that we should use nonconvex norms for most regularization tasks in image processing and computer vision. Still, they are rarely used in practice due to the challenge to optimize them. Recently, iteratively reweighed 1 minimization has been proposed as a way to tackle a class of non-co... | 2013/Ochs_An_Iterated_L1_2013_CVPR_paper.pdf | @InProceedings{Ochs_2013_ICCV_Workshops,author = {Ochs, Peter and Dosovitskiy, Alexey and Brox, Thomas and Pock, Thomas},title = {An Iterated L1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month =... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Ochs_An_Iterated_L1_2013_CVPR_paper.pdf |
Reconstructing Gas Flows Using Light-Path Approximation | Yu Ji, Jinwei Ye, Jingyi Yu | Transparent gas flows are difficult to reconstruct: the refractive index field (RIF) within the gas volume is uneven and rapidly evolving, and correspondence matching under distortions is challenging. We present a novel computational imaging solution by exploiting the light field probe (LFProbe). A LF-probe resembles a... | 2013/Ji_Reconstructing_Gas_Flows_2013_CVPR_paper.pdf | @InProceedings{Ji_2013_ICCV_Workshops,author = {Ji, Yu and Ye, Jinwei and Yu, Jingyi},title = {Reconstructing Gas Flows Using Light-Path Approximation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Ji_Reconstructing_Gas_Flows_2013_CVPR_paper.pdf |
Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets | Aurelien Lucchi, Yunpeng Li, Pascal Fua | We propose a working set based approximate subgradient descent algorithm to minimize the margin-sensitive hinge loss arising from the soft constraints in max-margin learning frameworks, such as the structured SVM. We focus on the setting of general graphical models, such as loopy MRFs and CRFs commonly used in image se... | 2013/Lucchi_Learning_for_Structured_2013_CVPR_paper.pdf | @InProceedings{Lucchi_2013_ICCV_Workshops,author = {Lucchi, Aurelien and Li, Yunpeng and Fua, Pascal},title = {Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},yea... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Lucchi_Learning_for_Structured_2013_CVPR_paper.pdf |
Exploring Implicit Image Statistics for Visual Representativeness Modeling | Xiaoshuai Sun, Xin-Jing Wang, Hongxun Yao, Lei Zhang | In this paper, we propose a computational model of visual representativeness by integrating cognitive theories of representativeness heuristics with computer vision and machine learning techniques. Unlike previous models that build their representativeness measure based on the visible data, our model takes the initial ... | 2013/Sun_Exploring_Implicit_Image_2013_CVPR_paper.pdf | @InProceedings{Sun_2013_ICCV_Workshops,author = {Sun, Xiaoshuai and Wang, Xin-Jing and Yao, Hongxun and Zhang, Lei},title = {Exploring Implicit Image Statistics for Visual Representativeness Modeling},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year ... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Sun_Exploring_Implicit_Image_2013_CVPR_paper.pdf |
Learning Multiple Non-linear Sub-spaces Using K-RBMs | Siddhartha Chandra, Shailesh Kumar, C.V. Jawahar | Understanding the nature of data is the key to building good representations. In domains such as natural images, the data comes from very complex distributions which are hard to capture. Feature learning intends to discover or best approximate these underlying distributions and use their knowledge to weed out irrelevan... | 2013/Chandra_Learning_Multiple_Non-linear_2013_CVPR_paper.pdf | @InProceedings{Chandra_2013_ICCV_Workshops,author = {Chandra, Siddhartha and Kumar, Shailesh and Jawahar, C.V.},title = {Learning Multiple Non-linear Sub-spaces Using K-RBMs},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Chandra_Learning_Multiple_Non-linear_2013_CVPR_paper.pdf |
Articulated and Restricted Motion Subspaces and Their Signatures | Bastien Jacquet, Roland Angst, Marc Pollefeys | Articulated objects represent an important class of objects in our everyday environment. Automatic detection of the type of articulated or otherwise restricted motion and extraction of the corresponding motion parameters are therefore of high value, e.g. in order to augment an otherwise static 3D reconstruction with dy... | 2013/Jacquet_Articulated_and_Restricted_2013_CVPR_paper.pdf | @InProceedings{Jacquet_2013_ICCV_Workshops,author = {Jacquet, Bastien and Angst, Roland and Pollefeys, Marc},title = {Articulated and Restricted Motion Subspaces and Their Signatures},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Jacquet_Articulated_and_Restricted_2013_CVPR_paper.pdf |
Simultaneous Active Learning of Classifiers & Attributes via Relative Feedback | Arijit Biswas, Devi Parikh | Active learning provides useful tools to reduce annotation costs without compromising classifier performance. However it traditionally views the supervisor simply as a labeling machine. Recently a new interactive learning paradigm was introduced that allows the supervisor to additionally convey useful domain knowledge ... | 2013/Biswas_Simultaneous_Active_Learning_2013_CVPR_paper.pdf | @InProceedings{Biswas_2013_ICCV_Workshops,author = {Biswas, Arijit and Parikh, Devi},title = {Simultaneous Active Learning of Classifiers & Attributes via Relative Feedback},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Biswas_Simultaneous_Active_Learning_2013_CVPR_paper.pdf |
Monocular Template-Based 3D Reconstruction of Extensible Surfaces with Local Linear Elasticity | Abed Malti, Richard Hartley, Adrien Bartoli, Jae-Hak Kim | We propose a new approach for template-based extensible surface reconstruction from a single view. We extend the method of isometric surface reconstruction and more recent work on conformal surface reconstruction. Our approach relies on the minimization of a proposed stretching energy formalized with respect to the Poi... | 2013/Malti_Monocular_Template-Based_3D_2013_CVPR_paper.pdf | @InProceedings{Malti_2013_ICCV_Workshops,author = {Malti, Abed and Hartley, Richard and Bartoli, Adrien and Kim, Jae-Hak},title = {Monocular Template-Based 3D Reconstruction of Extensible Surfaces with Local Linear Elasticity},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (C... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Malti_Monocular_Template-Based_3D_2013_CVPR_paper.pdf |
Multi-view Photometric Stereo with Spatially Varying Isotropic Materials | Zhenglong Zhou, Zhe Wu, Ping Tan | We present a method to capture both 3D shape and spatially varying reflectance with a multi-view photometric stereo technique that works for general isotropic materials. Our data capture setup is simple, which consists of only a digital camera and a handheld light source. From a single viewpoint, we use a set of photom... | 2013/Zhou_Multi-view_Photometric_Stereo_2013_CVPR_paper.pdf | @InProceedings{Zhou_2013_ICCV_Workshops,author = {Zhou, Zhenglong and Wu, Zhe and Tan, Ping},title = {Multi-view Photometric Stereo with Spatially Varying Isotropic Materials},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Zhou_Multi-view_Photometric_Stereo_2013_CVPR_paper.pdf |
A New Model and Simple Algorithms for Multi-label Mumford-Shah Problems | Byung-Woo Hong, Zhaojin Lu, Ganesh Sundaramoorthi | In this work, we address the multi-label Mumford-Shah problem, i.e., the problem of jointly estimating a partitioning of the domain of the image, and functions defined within regions of the partition. We create algorithms that are efficient, robust to undesirable local minima, and are easy-toimplement. Our algorithms a... | 2013/Hong_A_New_Model_2013_CVPR_paper.pdf | @InProceedings{Hong_2013_ICCV_Workshops,author = {Hong, Byung-Woo and Lu, Zhaojin and Sundaramoorthi, Ganesh},title = {A New Model and Simple Algorithms for Multi-label Mumford-Shah Problems},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Hong_A_New_Model_2013_CVPR_paper.pdf |
Kernel Learning for Extrinsic Classification of Manifold Features | Raviteja Vemulapalli, Jaishanker K. Pillai, Rama Chellappa | In computer vision applications, features often lie on Riemannian manifolds with known geometry. Popular learning algorithms such as discriminant analysis, partial least squares, support vector machines, etc., are not directly applicable to such features due to the non-Euclidean nature of the underlying spaces. Hence, ... | 2013/Vemulapalli_Kernel_Learning_for_2013_CVPR_paper.pdf | @InProceedings{Vemulapalli_2013_ICCV_Workshops,author = {Vemulapalli, Raviteja and Pillai, Jaishanker K. and Chellappa, Rama},title = {Kernel Learning for Extrinsic Classification of Manifold Features},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Vemulapalli_Kernel_Learning_for_2013_CVPR_paper.pdf |
Finding Things: Image Parsing with Regions and Per-Exemplar Detectors | Joseph Tighe, Svetlana Lazebnik | This paper presents a system for image parsing, or labeling each pixel in an image with its semantic category, aimed at achieving broad coverage across hundreds of object categories, many of them sparsely sampled. The system combines region-level features with per-exemplar sliding window detectors. Per-exemplar detecto... | 2013/Tighe_Finding_Things_Image_2013_CVPR_paper.pdf | @InProceedings{Tighe_2013_ICCV_Workshops,author = {Tighe, Joseph and Lazebnik, Svetlana},title = {Finding Things: Image Parsing with Regions and Per-Exemplar Detectors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Tighe_Finding_Things_Image_2013_CVPR_paper.pdf |
Learning Collections of Part Models for Object Recognition | Ian Endres, Kevin J. Shih, Johnston Jiaa, Derek Hoiem | We propose a method to learn a diverse collection of discriminative parts from object bounding box annotations. Part detectors can be trained and applied individually, which simplifies learning and extension to new features or categories. We apply the parts to object category detection, pooling part detections within b... | 2013/Endres_Learning_Collections_of_2013_CVPR_paper.pdf | @InProceedings{Endres_2013_ICCV_Workshops,author = {Endres, Ian and Shih, Kevin J. and Jiaa, Johnston and Hoiem, Derek},title = {Learning Collections of Part Models for Object Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Endres_Learning_Collections_of_2013_CVPR_paper.pdf |
Complex Event Detection via Multi-source Video Attributes | Zhigang Ma, Yi Yang, Zhongwen Xu, Shuicheng Yan, Nicu Sebe, Alexander G. Hauptmann | Complex events essentially include human, scenes, objects and actions that can be summarized by visual attributes, so leveraging relevant attributes properly could be helpful for event detection. Many works have exploited attributes at image level for various applications. However, attributes at image level are possibl... | 2013/Ma_Complex_Event_Detection_2013_CVPR_paper.pdf | @InProceedings{Ma_2013_ICCV_Workshops,author = {Ma, Zhigang and Yang, Yi and Xu, Zhongwen and Yan, Shuicheng and Sebe, Nicu and Hauptmann, Alexander G.},title = {Complex Event Detection via Multi-source Video Attributes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Ma_Complex_Event_Detection_2013_CVPR_paper.pdf |
FrameBreak: Dramatic Image Extrapolation by Guided Shift-Maps | Yinda Zhang, Jianxiong Xiao, James Hays, Ping Tan | We significantly extrapolate the field of view of a photograph by learning from a roughly aligned, wide-angle guide image of the same scene category. Our method can extrapolate typical photos into complete panoramas. The extrapolation problem is formulated in the shift-map image synthesis framework. We analyze the self... | 2013/Zhang_FrameBreak_Dramatic_Image_2013_CVPR_paper.pdf | @InProceedings{Zhang_2013_ICCV_Workshops,author = {Zhang, Yinda and Xiao, Jianxiong and Hays, James and Tan, Ping},title = {FrameBreak: Dramatic Image Extrapolation by Guided Shift-Maps},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Zhang_FrameBreak_Dramatic_Image_2013_CVPR_paper.pdf |
Single Image Calibration of Multi-axial Imaging Systems | Amit Agrawal, Srikumar Ramalingam | Imaging systems consisting of a camera looking at multiple spherical mirrors (reflection) or multiple refractive spheres (refraction) have been used for wide-angle imaging applications. We describe such setups as multi-axial imaging systems, since a single sphere results in an axial system. Assuming an internally calib... | 2013/Agrawal_Single_Image_Calibration_2013_CVPR_paper.pdf | @InProceedings{Agrawal_2013_ICCV_Workshops,author = {Agrawal, Amit and Ramalingam, Srikumar},title = {Single Image Calibration of Multi-axial Imaging Systems},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Agrawal_Single_Image_Calibration_2013_CVPR_paper.pdf |
Bayesian Grammar Learning for Inverse Procedural Modeling | Andelo Martinovic, Luc Van Gool | Within the fields of urban reconstruction and city modeling, shape grammars have emerged as a powerful tool for both synthesizing novel designs and reconstructing buildings. Traditionally, a human expert was required to write grammars for specific building styles, which limited the scope of method applicability. We pre... | 2013/Martinovic_Bayesian_Grammar_Learning_2013_CVPR_paper.pdf | @InProceedings{Martinovic_2013_ICCV_Workshops,author = {Martinovic, Andelo and Van Gool, Luc},title = {Bayesian Grammar Learning for Inverse Procedural Modeling},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Martinovic_Bayesian_Grammar_Learning_2013_CVPR_paper.pdf |
3D R Transform on Spatio-temporal Interest Points for Action Recognition | Chunfeng Yuan, Xi Li, Weiming Hu, Haibin Ling, Stephen Maybank | Spatio-temporal interest points serve as an elementary building block in many modern action recognition algorithms, and most of them exploit the local spatio-temporal volume features using a Bag of Visual Words (BOVW) representation. Such representation, however, ignores potentially valuable information about the globa... | 2013/Yuan_3D_R_Transform_2013_CVPR_paper.pdf | @InProceedings{Yuan_2013_ICCV_Workshops,author = {Yuan, Chunfeng and Li, Xi and Hu, Weiming and Ling, Haibin and Maybank, Stephen},title = {3D R Transform on Spatio-temporal Interest Points for Action Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month =... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Yuan_3D_R_Transform_2013_CVPR_paper.pdf |
First-Person Activity Recognition: What Are They Doing to Me? | Michael S. Ryoo, Larry Matthies | This paper discusses the problem of recognizing interaction-level human activities from a first-person viewpoint. The goal is to enable an observer (e.g., a robot or a wearable camera) to understand 'what activity others are performing to it' from continuous video inputs. These include friendly interactions such as 'a ... | 2013/Ryoo_First-Person_Activity_Recognition_2013_CVPR_paper.pdf | @InProceedings{Ryoo_2013_ICCV_Workshops,author = {Ryoo, Michael S. and Matthies, Larry},title = {First-Person Activity Recognition: What Are They Doing to Me?},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Ryoo_First-Person_Activity_Recognition_2013_CVPR_paper.pdf |
Sparse Subspace Denoising for Image Manifolds | Bo Wang, Zhuowen Tu | With the increasing availability of high dimensional data and demand in sophisticated data analysis algorithms, manifold learning becomes a critical technique to perform dimensionality reduction, unraveling the intrinsic data structure. The real-world data however often come with noises and outliers; seldom, all the da... | 2013/Wang_Sparse_Subspace_Denoising_2013_CVPR_paper.pdf | @InProceedings{Wang_2013_ICCV_Workshops,author = {Wang, Bo and Tu, Zhuowen},title = {Sparse Subspace Denoising for Image Manifolds},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Wang_Sparse_Subspace_Denoising_2013_CVPR_paper.pdf |
Adding Unlabeled Samples to Categories by Learned Attributes | Jonghyun Choi, Mohammad Rastegari, Ali Farhadi, Larry S. Davis | We propose a method to expand the visual coverage of training sets that consist of a small number of labeled examples using learned attributes. Our optimization formulation discovers category specific attributes as well as the images that have high confidence in terms of the attributes. In addition, we propose a method... | 2013/Choi_Adding_Unlabeled_Samples_2013_CVPR_paper.pdf | @InProceedings{Choi_2013_ICCV_Workshops,author = {Choi, Jonghyun and Rastegari, Mohammad and Farhadi, Ali and Davis, Larry S.},title = {Adding Unlabeled Samples to Categories by Learned Attributes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Choi_Adding_Unlabeled_Samples_2013_CVPR_paper.pdf |
Auxiliary Cuts for General Classes of Higher Order Functionals | Ismail Ben Ayed, Lena Gorelick, Yuri Boykov | Several recent studies demonstrated that higher order (non-linear) functionals can yield outstanding performances in the contexts of segmentation, co-segmentation and tracking. In general, higher order functionals result in difficult problems that are not amenable to standard optimizers, and most of the existing works ... | 2013/Ayed_Auxiliary_Cuts_for_2013_CVPR_paper.pdf | @InProceedings{Ayed_2013_ICCV_Workshops,author = {Ben Ayed, Ismail and Gorelick, Lena and Boykov, Yuri},title = {Auxiliary Cuts for General Classes of Higher Order Functionals},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Ayed_Auxiliary_Cuts_for_2013_CVPR_paper.pdf |
Template-Based Isometric Deformable 3D Reconstruction with Sampling-Based Focal Length Self-Calibration | Adrien Bartoli, Toby Collins | It has been shown that a surface deforming isometrically can be reconstructed from a single image and a template 3D shape. Methods from the literature solve this problem efficiently. However, they all assume that the camera model is calibrated, which drastically limits their applicability. We propose (i) a general vari... | 2013/Bartoli_Template-Based_Isometric_Deformable_2013_CVPR_paper.pdf | @InProceedings{Bartoli_2013_ICCV_Workshops,author = {Bartoli, Adrien and Collins, Toby},title = {Template-Based Isometric Deformable 3D Reconstruction with Sampling-Based Focal Length Self-Calibration},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Bartoli_Template-Based_Isometric_Deformable_2013_CVPR_paper.pdf |
Video Editing with Temporal, Spatial and Appearance Consistency | Xiaojie Guo, Xiaochun Cao, Xiaowu Chen, Yi Ma | Given an area of interest in a video sequence, one may want to manipulate or edit the area, e.g. remove occlusions from or replace with an advertisement on it. Such a task involves three main challenges including temporal consistency, spatial pose, and visual realism. The proposed method effectively seeks an optimal so... | 2013/Guo_Video_Editing_with_2013_CVPR_paper.pdf | @InProceedings{Guo_2013_ICCV_Workshops,author = {Guo, Xiaojie and Cao, Xiaochun and Chen, Xiaowu and Ma, Yi},title = {Video Editing with Temporal, Spatial and Appearance Consistency},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Guo_Video_Editing_with_2013_CVPR_paper.pdf |
Binary Code Ranking with Weighted Hamming Distance | Lei Zhang, Yongdong Zhang, Jinhu Tang, Ke Lu, Qi Tian | Binary hashing has been widely used for efficient similarity search due to its query and storage efficiency. In most existing binary hashing methods, the high-dimensional data are embedded into Hamming space and the distance or similarity of two points are approximated by the Hamming distance between their binary codes... | 2013/Zhang_Binary_Code_Ranking_2013_CVPR_paper.pdf | @InProceedings{Zhang_2013_ICCV_Workshops,author = {Zhang, Lei and Zhang, Yongdong and Tang, Jinhu and Lu, Ke and Tian, Qi},title = {Binary Code Ranking with Weighted Hamming Distance},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Zhang_Binary_Code_Ranking_2013_CVPR_paper.pdf |
Unsupervised Joint Object Discovery and Segmentation in Internet Images | Michael Rubinstein, Armand Joulin, Johannes Kopf, Ce Liu | We present a new unsupervised algorithm to discover and segment out common objects from large and diverse image collections. In contrast to previous co-segmentation methods, our algorithm performs well even in the presence of significant amounts of noise images (images not containing a common object), as typical for da... | 2013/Rubinstein_Unsupervised_Joint_Object_2013_CVPR_paper.pdf | @InProceedings{Rubinstein_2013_ICCV_Workshops,author = {Rubinstein, Michael and Joulin, Armand and Kopf, Johannes and Liu, Ce},title = {Unsupervised Joint Object Discovery and Segmentation in Internet Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {Jun... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Rubinstein_Unsupervised_Joint_Object_2013_CVPR_paper.pdf |
Learning SURF Cascade for Fast and Accurate Object Detection | Jianguo Li, Yimin Zhang | This paper presents a novel learning framework for training boosting cascade based object detector from large scale dataset. The framework is derived from the wellknown Viola-Jones (VJ) framework but distinguished by three key differences. First, the proposed framework adopts multi-dimensional SURF features instead of ... | 2013/Li_Learning_SURF_Cascade_2013_CVPR_paper.pdf | @InProceedings{Li_2013_ICCV_Workshops,author = {Li, Jianguo and Zhang, Yimin},title = {Learning SURF Cascade for Fast and Accurate Object Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Li_Learning_SURF_Cascade_2013_CVPR_paper.pdf |
Efficient Computation of Shortest Path-Concavity for 3D Meshes | Henrik Zimmer, Marcel Campen, Leif Kobbelt | In the context of shape segmentation and retrieval object-wide distributions of measures are needed to accurately evaluate and compare local regions of shapes. Lien et al. [16] proposed two point-wise concavity measures in the context of Approximate Convex Decompositions of polygons measuring the distance from a point ... | 2013/Zimmer_Efficient_Computation_of_2013_CVPR_paper.pdf | @InProceedings{Zimmer_2013_ICCV_Workshops,author = {Zimmer, Henrik and Campen, Marcel and Kobbelt, Leif},title = {Efficient Computation of Shortest Path-Concavity for 3D Meshes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Zimmer_Efficient_Computation_of_2013_CVPR_paper.pdf |
Leveraging Structure from Motion to Learn Discriminative Codebooks for Scalable Landmark Classification | Alessandro Bergamo, Sudipta N. Sinha, Lorenzo Torresani | In this paper we propose a new technique for learning a discriminative codebook for local feature descriptors, specifically designed for scalable landmark classification. The key contribution lies in exploiting the knowledge of correspondences within sets of feature descriptors during codebook learning. Feature corresp... | 2013/Bergamo_Leveraging_Structure_from_2013_CVPR_paper.pdf | @InProceedings{Bergamo_2013_ICCV_Workshops,author = {Bergamo, Alessandro and Sinha, Sudipta N. and Torresani, Lorenzo},title = {Leveraging Structure from Motion to Learn Discriminative Codebooks for Scalable Landmark Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognit... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Bergamo_Leveraging_Structure_from_2013_CVPR_paper.pdf |
Learning Discriminative Illumination and Filters for Raw Material Classification with Optimal Projections of Bidirectional Texture Functions | Chao Liu, Geifei Yang, Jinwei Gu | We present a computational imaging method for raw material classification using features of Bidirectional Texture Functions (BTF). Texture is an intrinsic feature for many materials, such as wood, fabric, and granite. At appropriate scales, even "uniform" materials will also exhibit texture features that can be helpful... | 2013/Liu_Learning_Discriminative_Illumination_2013_CVPR_paper.pdf | @InProceedings{Liu_2013_ICCV_Workshops,author = {Liu, Chao and Yang, Geifei and Gu, Jinwei},title = {Learning Discriminative Illumination and Filters for Raw Material Classification with Optimal Projections of Bidirectional Texture Functions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Patter... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Liu_Learning_Discriminative_Illumination_2013_CVPR_paper.pdf |
Illumination Estimation Based on Bilayer Sparse Coding | Bing Li, Weihua Xiong, Weiming Hu, Houwen Peng | Computational color constancy is a very important topic in computer vision and has attracted many researchers' attention. Recently, lots of research has shown the effects of using high level visual content cues for improving illumination estimation. However, nearly all the existing methods are essentially combinational... | 2013/Li_Illumination_Estimation_Based_2013_CVPR_paper.pdf | @InProceedings{Li_2013_ICCV_Workshops,author = {Li, Bing and Xiong, Weihua and Hu, Weiming and Peng, Houwen},title = {Illumination Estimation Based on Bilayer Sparse Coding},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Li_Illumination_Estimation_Based_2013_CVPR_paper.pdf |
Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object Recognition | Qiang Hao, Rui Cai, Zhiwei Li, Lei Zhang, Yanwei Pang, Feng Wu, Yong Rui | 3D model-based object recognition has been a noticeable research trend in recent years. Common methods find 2D-to-3D correspondences and make recognition decisions by pose estimation, whose efficiency usually suffers from noisy correspondences caused by the increasing number of target objects. To overcome this scalabil... | 2013/Hao_Efficient_2D-to-3D_Correspondence_2013_CVPR_paper.pdf | @InProceedings{Hao_2013_ICCV_Workshops,author = {Hao, Qiang and Cai, Rui and Li, Zhiwei and Zhang, Lei and Pang, Yanwei and Wu, Feng and Rui, Yong},title = {Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Reco... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Hao_Efficient_2D-to-3D_Correspondence_2013_CVPR_paper.pdf |
Jointly Aligning and Segmenting Multiple Web Photo Streams for the Inference of Collective Photo Storylines | Gunhee Kim, Eric P. Xing | With an explosion of popularity of online photo sharing, we can trivially collect a huge number of photo streams for any interesting topics such as scuba diving as an outdoor recreational activity class. Obviously, the retrieved photo streams are neither aligned nor calibrated since they are taken in different temporal... | 2013/Kim_Jointly_Aligning_and_2013_CVPR_paper.pdf | @InProceedings{Kim_2013_ICCV_Workshops,author = {Kim, Gunhee and Xing, Eric P.},title = {Jointly Aligning and Segmenting Multiple Web Photo Streams for the Inference of Collective Photo Storylines},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Kim_Jointly_Aligning_and_2013_CVPR_paper.pdf |
Weakly Supervised Learning for Attribute Localization in Outdoor Scenes | Shuo Wang, Jungseock Joo, Yizhou Wang, Song-Chun Zhu | In this paper, we propose a weakly supervised method for simultaneously learning scene parts and attributes from a collection of images associated with attributes in text, where the precise localization of the each attribute left unknown. Our method includes three aspects. (i) Compositional scene configuration. We lear... | 2013/Wang_Weakly_Supervised_Learning_2013_CVPR_paper.pdf | @InProceedings{Wang_2013_ICCV_Workshops,author = {Wang, Shuo and Joo, Jungseock and Wang, Yizhou and Zhu, Song-Chun},title = {Weakly Supervised Learning for Attribute Localization in Outdoor Scenes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = ... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Wang_Weakly_Supervised_Learning_2013_CVPR_paper.pdf |
SLAM++: Simultaneous Localisation and Mapping at the Level of Objects | Renato F. Salas-Moreno, Richard A. Newcombe, Hauke Strasdat, Paul H.J. Kelly, Andrew J. Davison | We present the major advantages of a new 'object oriented' 3D SLAM paradigm, which takes full advantage in the loop of prior knowledge that many scenes consist of repeated, domain-specific objects and structures. As a hand-held depth camera browses a cluttered scene, realtime 3D object recognition and tracking provides... | 2013/Salas-Moreno_SLAM_Simultaneous_Localisation_2013_CVPR_paper.pdf | @InProceedings{Salas-Moreno_2013_ICCV_Workshops,author = {Salas-Moreno, Renato F. and Newcombe, Richard A. and Strasdat, Hauke and Kelly, Paul H.J. and Davison, Andrew J.},title = {SLAM++: Simultaneous Localisation and Mapping at the Level of Objects},booktitle = {Proceedings of the IEEE Conference on Computer Vision a... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Salas-Moreno_SLAM_Simultaneous_Localisation_2013_CVPR_paper.pdf |
A Theory of Refractive Photo-Light-Path Triangulation | Visesh Chari, Peter Sturm | 3D reconstruction of transparent refractive objects like a plastic bottle is challenging: they lack appearance related visual cues and merely reflect and refract light from the surrounding environment. Amongst several approaches to reconstruct such objects, the seminal work of Light-Path triangulation [17] is highly po... | 2013/Chari_A_Theory_of_2013_CVPR_paper.pdf | @InProceedings{Chari_2013_ICCV_Workshops,author = {Chari, Visesh and Sturm, Peter},title = {A Theory of Refractive Photo-Light-Path Triangulation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Chari_A_Theory_of_2013_CVPR_paper.pdf |
Studying Relationships between Human Gaze, Description, and Computer Vision | Kiwon Yun, Yifan Peng, Dimitris Samaras, Gregory J. Zelinsky, Tamara L. Berg | We posit that user behavior during natural viewing of images contains an abundance of information about the content of images as well as information related to user intent and user defined content importance. In this paper, we conduct experiments to better understand the relationship between images, the eye movements p... | 2013/Yun_Studying_Relationships_between_2013_CVPR_paper.pdf | @InProceedings{Yun_2013_ICCV_Workshops,author = {Yun, Kiwon and Peng, Yifan and Samaras, Dimitris and Zelinsky, Gregory J. and Berg, Tamara L.},title = {Studying Relationships between Human Gaze, Description, and Computer Vision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Yun_Studying_Relationships_between_2013_CVPR_paper.pdf |
Learning Structured Low-Rank Representations for Image Classification | Yangmuzi Zhang, Zhuolin Jiang, Larry S. Davis | An approach to learn a structured low-rank representation for image classification is presented. We use a supervised learning method to construct a discriminative and reconstructive dictionary. By introducing an ideal regularization term, we perform low-rank matrix recovery for contaminated training data from all categ... | 2013/Zhang_Learning_Structured_Low-Rank_2013_CVPR_paper.pdf | @InProceedings{Zhang_2013_ICCV_Workshops,author = {Zhang, Yangmuzi and Jiang, Zhuolin and Davis, Larry S.},title = {Learning Structured Low-Rank Representations for Image Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Zhang_Learning_Structured_Low-Rank_2013_CVPR_paper.pdf |
Detecting and Aligning Faces by Image Retrieval | Xiaohui Shen, Zhe Lin, Jonathan Brandt, Ying Wu | Detecting faces in uncontrolled environments continues to be a challenge to traditional face detection methods[24] due to the large variation in facial appearances, as well as occlusion and clutter. In order to overcome these challenges, we present a novel and robust exemplarbased face detector that integrates image re... | 2013/Shen_Detecting_and_Aligning_2013_CVPR_paper.pdf | @InProceedings{Shen_2013_ICCV_Workshops,author = {Shen, Xiaohui and Lin, Zhe and Brandt, Jonathan and Wu, Ying},title = {Detecting and Aligning Faces by Image Retrieval},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Shen_Detecting_and_Aligning_2013_CVPR_paper.pdf |
Towards Contactless, Low-Cost and Accurate 3D Fingerprint Identification | Ajay Kumar, Cyril Kwong | In order to avail the benefits of higher user convenience, hygiene, and improved accuracy, contactless 3D fingerprint recognition techniques have recently been introduced. One of the key limitations of these emerging 3D fingerprint technologies to replace the conventional 2D fingerprint system is their bulk and high co... | 2013/Kumar_Towards_Contactless_Low-Cost_2013_CVPR_paper.pdf | @InProceedings{Kumar_2013_ICCV_Workshops,author = {Kumar, Ajay and Kwong, Cyril},title = {Towards Contactless, Low-Cost and Accurate 3D Fingerprint Identification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Kumar_Towards_Contactless_Low-Cost_2013_CVPR_paper.pdf |
Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling | Andrew Kae, Kihyuk Sohn, Honglak Lee, Erik Learned-Miller | Conditional random fields (CRFs) provide powerful tools for building models to label image segments. They are particularly well-suited to modeling local interactions among adjacent regions (e.g., superpixels). However, CRFs are limited in dealing with complex, global (long-range) interactions between regions. Complemen... | 2013/Kae_Augmenting_CRFs_with_2013_CVPR_paper.pdf | @InProceedings{Kae_2013_ICCV_Workshops,author = {Kae, Andrew and Sohn, Kihyuk and Lee, Honglak and Learned-Miller, Erik},title = {Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Kae_Augmenting_CRFs_with_2013_CVPR_paper.pdf |
It's Not Polite to Point: Describing People with Uncertain Attributes | Amir Sadovnik, Andrew Gallagher, Tsuhan Chen | Visual attributes are powerful features for many different applications in computer vision such as object detection and scene recognition. Visual attributes present another application that has not been examined as rigorously: verbal communication from a computer to a human. Since many attributes are nameable, the comp... | 2013/Sadovnik_Its_Not_Polite_2013_CVPR_paper.pdf | @InProceedings{Sadovnik_2013_ICCV_Workshops,author = {Sadovnik, Amir and Gallagher, Andrew and Chen, Tsuhan},title = {It's Not Polite to Point: Describing People with Uncertain Attributes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Sadovnik_Its_Not_Polite_2013_CVPR_paper.pdf |
Reconstructing Loopy Curvilinear Structures Using Integer Programming | Engin Turetken, Fethallah Benmansour, Bjoern Andres, Hanspeter Pfister, Pascal Fua | We propose a novel approach to automated delineation of linear structures that form complex and potentially loopy networks. This is in contrast to earlier approaches that usually assume a tree topology for the networks. At the heart of our method is an Integer Programming formulation that allows us to find the global o... | 2013/Turetken_Reconstructing_Loopy_Curvilinear_2013_CVPR_paper.pdf | @InProceedings{Turetken_2013_ICCV_Workshops,author = {Turetken, Engin and Benmansour, Fethallah and Andres, Bjoern and Pfister, Hanspeter and Fua, Pascal},title = {Reconstructing Loopy Curvilinear Structures Using Integer Programming},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recogn... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Turetken_Reconstructing_Loopy_Curvilinear_2013_CVPR_paper.pdf |
Multi-image Blind Deblurring Using a Coupled Adaptive Sparse Prior | Haichao Zhang, David Wipf, Yanning Zhang | This paper presents a robust algorithm for estimating a single latent sharp image given multiple blurry and/or noisy observations. The underlying multi-image blind deconvolution problem is solved by linking all of the observations together via a Bayesian-inspired penalty function which couples the unknown latent image,... | 2013/Zhang_Multi-image_Blind_Deblurring_2013_CVPR_paper.pdf | @InProceedings{Zhang_2013_ICCV_Workshops,author = {Zhang, Haichao and Wipf, David and Zhang, Yanning},title = {Multi-image Blind Deblurring Using a Coupled Adaptive Sparse Prior},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Zhang_Multi-image_Blind_Deblurring_2013_CVPR_paper.pdf |
Templateless Quasi-rigid Shape Modeling with Implicit Loop-Closure | Ming Zeng, Jiaxiang Zheng, Xuan Cheng, Xinguo Liu | This paper presents a method for quasi-rigid objects modeling from a sequence of depth scans captured at different time instances. As quasi-rigid objects, such as human bodies, usually have shape motions during the capture procedure, it is difficult to reconstruct their geometries. We represent the shape motion by a de... | 2013/Zeng_Templateless_Quasi-rigid_Shape_2013_CVPR_paper.pdf | @InProceedings{Zeng_2013_ICCV_Workshops,author = {Zeng, Ming and Zheng, Jiaxiang and Cheng, Xuan and Liu, Xinguo},title = {Templateless Quasi-rigid Shape Modeling with Implicit Loop-Closure},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Zeng_Templateless_Quasi-rigid_Shape_2013_CVPR_paper.pdf |
Weakly-Supervised Dual Clustering for Image Semantic Segmentation | Yang Liu, Jing Liu, Zechao Li, Jinhui Tang, Hanqing Lu | In this paper, we propose a novel Weakly-Supervised Dual Clustering (WSDC) approach for image semantic segmentation with image-level labels, i.e., collaboratively performing image segmentation and tag alignment with those regions. The proposed approach is motivated from the observation that superpixels belonging to an ... | 2013/Liu_Weakly-Supervised_Dual_Clustering_2013_CVPR_paper.pdf | @InProceedings{Liu_2013_ICCV_Workshops,author = {Liu, Yang and Liu, Jing and Li, Zechao and Tang, Jinhui and Lu, Hanqing},title = {Weakly-Supervised Dual Clustering for Image Semantic Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Liu_Weakly-Supervised_Dual_Clustering_2013_CVPR_paper.pdf |
Multi-target Tracking by Rank-1 Tensor Approximation | Xinchu Shi, Haibin Ling, Junling Xing, Weiming Hu | In this paper we formulate multi-target tracking (MTT) as a rank-1 tensor approximation problem and propose an 1 norm tensor power iteration solution. In particular, a high order tensor is constructed based on trajectories in the time window, with each tensor element as the affinity of the corresponding trajectory cand... | 2013/Shi_Multi-target_Tracking_by_2013_CVPR_paper.pdf | @InProceedings{Shi_2013_ICCV_Workshops,author = {Shi, Xinchu and Ling, Haibin and Xing, Junling and Hu, Weiming},title = {Multi-target Tracking by Rank-1 Tensor Approximation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | https://openaccess.thecvf.com/content_cvpr_2013/papers/Shi_Multi-target_Tracking_by_2013_CVPR_paper.pdf |
Cross-View Action Recognition via a Continuous Virtual Path | Zhong Zhang, Chunheng Wang, Baihua Xiao, Wen Zhou, Shuang Liu, Cunzhao Shi | In this paper, we propose a novel method for cross-view action recognition via a continuous virtual path which connects the source view and the target view. Each point on this virtual path is a virtual view which is obtained by a linear transformation of the action descriptor. All the virtual views are concatenated int... | 2013/Zhang_Cross-View_Action_Recognition_2013_CVPR_paper.pdf | @InProceedings{Zhang_2013_ICCV_Workshops,author = {Zhang, Zhong and Wang, Chunheng and Xiao, Baihua and Zhou, Wen and Liu, Shuang and Shi, Cunzhao},title = {Cross-View Action Recognition via a Continuous Virtual Path},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},mon... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Zhang_Cross-View_Action_Recognition_2013_CVPR_paper.pdf |
Discriminative Non-blind Deblurring | Uwe Schmidt, Carsten Rother, Sebastian Nowozin, Jeremy Jancsary, Stefan Roth | Non-blind deblurring is an integral component of blind approaches for removing image blur due to camera shake. Even though learning-based deblurring methods exist, they have been limited to the generative case and are computationally expensive. To this date, manually-defined models are thus most widely used, though lim... | 2013/Schmidt_Discriminative_Non-blind_Deblurring_2013_CVPR_paper.pdf | @InProceedings{Schmidt_2013_ICCV_Workshops,author = {Schmidt, Uwe and Rother, Carsten and Nowozin, Sebastian and Jancsary, Jeremy and Roth, Stefan},title = {Discriminative Non-blind Deblurring},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013... | https://openaccess.thecvf.com/content_cvpr_2013/papers/Schmidt_Discriminative_Non-blind_Deblurring_2013_CVPR_paper.pdf |
End of preview.
CVPR Papers
Since 2013, deep learning has revolutionized computer vision, with CVPR (IEEE Conference on Computer Vision and Pattern Recognition) serving as the premier venue documenting this transformative journey. From AlexNet's breakthrough to the rise of Transformers, CVPR papers chronicle the complete trajectory of computer vision advancement.
CVPR Papers is a comprehensive dataset containing all papers from CVPR 2013 to present, including metadata and PDF files. It is designed for literature review, trend analysis, citation network construction, and various research tasks in computer vision.
Pipeline
The dataset is constructed through a systematic multi-stage pipeline:
- Web Scraping: Extract paper listings from CVF Open Access repository
- Metadata Extraction: Parse HTML to extract titles, authors, PDF links, and BibTeX citations
- Abstract Retrieval: Fetch abstracts from individual paper detail pages
- PDF Download: Concurrently download all paper PDF files
- Data Validation: Verify data integrity and format consistency
Dataset Structure
CVPR_Papers/
├── 2013/
│ ├── pdf/ # PDF files for all papers
│ │ ├── paper1.pdf
│ │ ├── paper2.pdf
│ │ └── ...
│ └── meta.jsonl # Metadata
│
├── 2014/
│ ├── pdf/
│ └── meta.jsonl
└── ...
Dataset Overview
- Total Papers: 18,452 (CVPR 2013-2025, continuously expanding)
- Data Format: JSONL for metadata, PDF for full papers, organized by year
- Source: CVF Open Access
| Field | Type | Description |
|---|---|---|
title |
string | Paper title |
authors |
string | Comma-separated list of authors |
abstract |
string | Paper abstract |
pdf_path |
string | Relative path to PDF file |
download_url |
string | Direct download URL for PDF |
bibtex |
string | BibTeX citation string |
Example Entry:
{
"title": "Deformable Spatial Pyramid Matching for Fast Dense Correspondences",
"authors": "Jaechul Kim, Ce Liu, Fei Sha, Kristen Grauman",
"abstract": "We introduce a fast deformable spatial pyramid (DSP) matching algorithm for computing dense pixel correspondences...",
"pdf_path": "2013/pdf/Kim_Deformable_Spatial_Pyramid_2013_CVPR_paper.pdf",
"download_url": "https://openaccess.thecvf.com/content_cvpr_2013/papers/Kim_Deformable_Spatial_Pyramid_2013_CVPR_paper.pdf",
"bibtex": "@InProceedings{Kim_2013_CVPR,author = {Kim, Jaechul and Liu, Ce and Sha, Fei and Grauman, Kristen},title = {Deformable Spatial Pyramid Matching for Fast Dense Correspondences},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}"
}
Quick Start
Installation
pip install huggingface_hub requests
Load Metadata
from huggingface_hub import hf_hub_download
import json
# Download metadata for a specific year
year = "2013"
meta_path = hf_hub_download(
repo_id="choucsan/CVPR_Papers",
filename=f"{year}/meta.jsonl",
repo_type="dataset",
)
# Load metadata
papers = []
with open(meta_path, 'r', encoding='utf-8') as f:
for line in f:
papers.append(json.loads(line))
print(f"Loaded {len(papers)} CVPR {year} papers")
Download PDF Files
Each paper has a download_url field pointing to the original PDF on CVF Open Access:
import requests
import os
# Create output directory
os.makedirs(f"cvpr_{year}_pdfs", exist_ok=True)
# Download a specific paper
paper = papers[0]
response = requests.get(paper['download_url'])
filename = os.path.basename(paper['pdf_path'])
with open(f"cvpr_{year}_pdfs/{filename}", 'wb') as f:
f.write(response.content)
print(f"Downloaded: {filename}")
# Download all papers for a year (optional)
for paper in papers:
if paper.get('download_url'):
response = requests.get(paper['download_url'])
filename = os.path.basename(paper['pdf_path'])
with open(f"cvpr_{year}_pdfs/{filename}", 'wb') as f:
f.write(response.content)
Applications
PDF Access
- Direct Download: Use
download_urlto download PDFs directly from CVF Open Access - Full Text Analysis: Extract text from PDFs for detailed content analysis
- Figure Extraction: Extract figures, tables, and equations from papers
- Layout Analysis: Analyze paper structure and formatting patterns
- OCR Processing: Optical character recognition for scanned documents
Research Applications
- Literature Review: Rapid retrieval of papers in specific domains for comprehensive literature review
- Trend Analysis: Analyze research hotspots and development trends in computer vision over the past decade
- Citation Networks: Build and analyze paper citation networks based on BibTeX data
- Knowledge Graphs: Construct knowledge graphs connecting papers, authors, institutions, and concepts
- Recommendation Systems: Build paper recommendation systems based on content similarity
Download
- Hugging Face: datasets/choucsan/CVPR_Papers
Contact
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