paper_id string | title string | authors list | cvf_url string | pdf_url string | supp_url string | arxiv_id string | arxiv_id_source string | bibtex large_string | abstract large_string |
|---|---|---|---|---|---|---|---|---|---|
Chang_They_Are_Not_CVPR_2016_paper | They Are Not Equally Reliable: Semantic Event Search Using Differentiated Concept Classifiers | [
"Xiaojun Chang",
"Yao-Liang Yu",
"Yi Yang",
"Eric P. Xing"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Chang_They_Are_Not_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Chang_They_Are_Not_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Chang_2016_CVPR,author = {Chang, Xiaojun and Yu, Yao-Liang and Yang, Yi and Xing, Eric P.},title = {They Are Not Equally Reliable: Semantic Event Search Using Differentiated Concept Classifiers},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {Ju... | Complex event detection on unconstrained Internet videos has seen much progress in recent years. However, state-of-the-art performance degrades dramatically when the number of positive training exemplars falls short. Since label acquisition is costly, laborious, and time-consuming, there is a real need to consider the ... |
Ma_Going_Deeper_into_CVPR_2016_paper | Going Deeper into First-Person Activity Recognition | [
"Minghuang Ma",
"Haoqi Fan",
"Kris M. Kitani"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Ma_Going_Deeper_into_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Ma_Going_Deeper_into_CVPR_2016_paper.pdf | null | 1605.03688 | title_snapshot | @InProceedings{Ma_2016_CVPR,author = {Ma, Minghuang and Fan, Haoqi and Kitani, Kris M.},title = {Going Deeper into First-Person Activity Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We bring together ideas from recent work on feature design for egocentric action recognition under one framework by exploring the use of deep convolutional neural networks (CNN). Recent work has shown that features such as hand appearance, object attributes, local hand motion and camera ego-motion are important for cha... |
Zhou_Cascaded_Interactional_Targeting_CVPR_2016_paper | Cascaded Interactional Targeting Network for Egocentric Video Analysis | [
"Yang Zhou",
"Bingbing Ni",
"Richang Hong",
"Xiaokang Yang",
"Qi Tian"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Zhou_Cascaded_Interactional_Targeting_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Zhou_Cascaded_Interactional_Targeting_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Zhou_2016_CVPR,author = {Zhou, Yang and Ni, Bingbing and Hong, Richang and Yang, Xiaokang and Tian, Qi},title = {Cascaded Interactional Targeting Network for Egocentric Video Analysis},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year =... | Knowing how hands move and what object is being manipulated are two key sub-tasks for analyzing first-person (egocentric) action. However, lack of fully annotated hand data as well as imprecise foreground segmentation make either sub-task challenging. This work aims to explicitly address these two issues via introducin... |
Heilbron_Fast_Temporal_Activity_CVPR_2016_paper | Fast Temporal Activity Proposals for Efficient Detection of Human Actions in Untrimmed Videos | [
"Fabian Caba Heilbron",
"Juan Carlos Niebles",
"Bernard Ghanem"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Heilbron_Fast_Temporal_Activity_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Heilbron_Fast_Temporal_Activity_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Heilbron_Fast_Temporal_Activity_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Heilbron_2016_CVPR,author = {Heilbron, Fabian Caba and Niebles, Juan Carlos and Ghanem, Bernard},title = {Fast Temporal Activity Proposals for Efficient Detection of Human Actions in Untrimmed Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month... | In many large-scale video analysis scenarios, one is interested in localizing and recognizing human activities that occur in short temporal intervals within long untrimmed videos. Current approaches for activity detection still struggle to handle large-scale video collections and the task remains relatively unexplored.... |
Fernando_Discriminative_Hierarchical_Rank_CVPR_2016_paper | Discriminative Hierarchical Rank Pooling for Activity Recognition | [
"Basura Fernando",
"Peter Anderson",
"Marcus Hutter",
"Stephen Gould"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Fernando_Discriminative_Hierarchical_Rank_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Fernando_Discriminative_Hierarchical_Rank_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Fernando_Discriminative_Hierarchical_Rank_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Fernando_2016_CVPR,author = {Fernando, Basura and Anderson, Peter and Hutter, Marcus and Gould, Stephen},title = {Discriminative Hierarchical Rank Pooling for Activity Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {20... | We present hierarchical rank pooling, a video sequence encoding method for activity recognition. It consists of a network of rank pooling functions which captures the dynamics of rich convolutional neural network features within a video sequence. By stacking non-linear feature functions and rank pooling over one anothe... |
Feichtenhofer_Convolutional_Two-Stream_Network_CVPR_2016_paper | Convolutional Two-Stream Network Fusion for Video Action Recognition | [
"Christoph Feichtenhofer",
"Axel Pinz",
"Andrew Zisserman"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Feichtenhofer_Convolutional_Two-Stream_Network_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Feichtenhofer_Convolutional_Two-Stream_Network_CVPR_2016_paper.pdf | null | 1604.06573 | title_snapshot | @InProceedings{Feichtenhofer_2016_CVPR,author = {Feichtenhofer, Christoph and Pinz, Axel and Zisserman, Andrew},title = {Convolutional Two-Stream Network Fusion for Video Action Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Recent applications of Convolutional Neural Networks (ConvNets) for human action recognition in videos have proposed different solutions for incorporating the appearance and motion information. We study a number of ways of fusing ConvNet towers both spatially and temporally in order to best take advantage of this spati... |
Ma_Learning_Activity_Progression_CVPR_2016_paper | Learning Activity Progression in LSTMs for Activity Detection and Early Detection | [
"Shugao Ma",
"Leonid Sigal",
"Stan Sclaroff"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Ma_Learning_Activity_Progression_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Ma_Learning_Activity_Progression_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Ma_2016_CVPR,author = {Ma, Shugao and Sigal, Leonid and Sclaroff, Stan},title = {Learning Activity Progression in LSTMs for Activity Detection and Early Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | In this work we improve training of temporal deep models to better learn activity progression for activity detection and early detection. Conventionally, when training a Recurrent Neural Network, specifically a Long Short Term Memory (LSTM) model, the training loss only considers classification error. However, we argue... |
Li_VLAD3_Encoding_Dynamics_CVPR_2016_paper | VLAD3: Encoding Dynamics of Deep Features for Action Recognition | [
"Yingwei Li",
"Weixin Li",
"Vijay Mahadevan",
"Nuno Vasconcelos"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Li_VLAD3_Encoding_Dynamics_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Li_VLAD3_Encoding_Dynamics_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Li_2016_CVPR,author = {Li, Yingwei and Li, Weixin and Mahadevan, Vijay and Vasconcelos, Nuno},title = {VLAD3: Encoding Dynamics of Deep Features for Action Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Previous approaches to action recognition with deep features tend to process video frames only within a small temporal region, and do not model long-range dynamic information explicitly. However, such information is important for the accurate recognition of actions, especially for the discrimination of complex activiti... |
Singh_A_Multi-Stream_Bi-Directional_CVPR_2016_paper | A Multi-Stream Bi-Directional Recurrent Neural Network for Fine-Grained Action Detection | [
"Bharat Singh",
"Tim K. Marks",
"Michael Jones",
"Oncel Tuzel",
"Ming Shao"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Singh_A_Multi-Stream_Bi-Directional_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Singh_A_Multi-Stream_Bi-Directional_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Singh_A_Multi-Stream_Bi-Directional_2016_CVPR_supplemental.zip | null | null | @InProceedings{Singh_2016_CVPR,author = {Singh, Bharat and Marks, Tim K. and Jones, Michael and Tuzel, Oncel and Shao, Ming},title = {A Multi-Stream Bi-Directional Recurrent Neural Network for Fine-Grained Action Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR... | We present a multi-stream bi-directional recurrent neural network for fine-grained action detection. Recently, two-stream convolutional neural networks (CNNs) trained on stacked optical flow and image frames have been successful for action recognition in videos. Our system uses a tracking algorithm to locate a bounding... |
Ibrahim_A_Hierarchical_Deep_CVPR_2016_paper | A Hierarchical Deep Temporal Model for Group Activity Recognition | [
"Mostafa S. Ibrahim",
"Srikanth Muralidharan",
"Zhiwei Deng",
"Arash Vahdat",
"Greg Mori"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Ibrahim_A_Hierarchical_Deep_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Ibrahim_A_Hierarchical_Deep_CVPR_2016_paper.pdf | null | 1511.06040 | title_snapshot | @InProceedings{Ibrahim_2016_CVPR,author = {Ibrahim, Mostafa S. and Muralidharan, Srikanth and Deng, Zhiwei and Vahdat, Arash and Mori, Greg},title = {A Hierarchical Deep Temporal Model for Group Activity Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},mont... | In group activity recognition, the temporal dynamics of the whole activity can be inferred based on the dynamics of the individual people representing the activity. We build a deep model to capture these dynamics based on LSTM (long short-term memory) models. To make use of these observations, we present a 2-stage deep... |
Lillo_A_Hierarchical_Pose-Based_CVPR_2016_paper | A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets | [
"Ivan Lillo",
"Juan Carlos Niebles",
"Alvaro Soto"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Lillo_A_Hierarchical_Pose-Based_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Lillo_A_Hierarchical_Pose-Based_CVPR_2016_paper.pdf | null | 1606.04992 | title_snapshot | @InProceedings{Lillo_2016_CVPR,author = {Lillo, Ivan and Niebles, Juan Carlos and Soto, Alvaro},title = {A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CV... | In this paper, we introduce a new hierarchical model for human action recognition that is able to categorize complex actions performed in videos. Our model is also able to perform spatio-temporal annotation of the atomic actions that compose the overall complex action. That is, for each atomic action, the model generat... |
Zhu_A_Key_Volume_CVPR_2016_paper | A Key Volume Mining Deep Framework for Action Recognition | [
"Wangjiang Zhu",
"Jie Hu",
"Gang Sun",
"Xudong Cao",
"Yu Qiao"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Zhu_A_Key_Volume_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Zhu_A_Key_Volume_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Zhu_2016_CVPR,author = {Zhu, Wangjiang and Hu, Jie and Sun, Gang and Cao, Xudong and Qiao, Yu},title = {A Key Volume Mining Deep Framework for Action Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Recently, deep learning approaches have demonstrated remarkable progresses for action recognition in videos. Most existing deep frameworks equally treat every volume i.e. spatial-temporal video clip, and directly assign a video label to all volumes sampled from it. However, within a video, discriminative actions may oc... |
Ong_Improved_Hamming_Distance_CVPR_2016_paper | Improved Hamming Distance Search Using Variable Length Substrings | [
"Eng-Jon Ong",
"Miroslaw Bober"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Ong_Improved_Hamming_Distance_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Ong_Improved_Hamming_Distance_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Ong_2016_CVPR,author = {Ong, Eng-Jon and Bober, Miroslaw},title = {Improved Hamming Distance Search Using Variable Length Substrings},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | This paper addresses the problem of ultra-large-scale search in Hamming spaces. There has been considerable research on generating compact binary codes in vision, for example for visual search tasks. However the issue of efficient searching through huge sets of binary codes remains largely unsolved. To this end, we pro... |
Heo_Shortlist_Selection_With_CVPR_2016_paper | Shortlist Selection With Residual-Aware Distance Estimator for K-Nearest Neighbor Search | [
"Jae-Pil Heo",
"Zhe Lin",
"Xiaohui Shen",
"Jonathan Brandt",
"Sung-eui Yoon"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Heo_Shortlist_Selection_With_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Heo_Shortlist_Selection_With_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Heo_2016_CVPR,author = {Heo, Jae-Pil and Lin, Zhe and Shen, Xiaohui and Brandt, Jonathan and Yoon, Sung-eui},title = {Shortlist Selection With Residual-Aware Distance Estimator for K-Nearest Neighbor Search},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)... | In this paper, we introduce a novel shortlist computation algorithm for approximate, high-dimensional nearest neighbor search. Our method relies on a novel distance estimator: the residual-aware distance estimator, that accounts for the residual distances of data points to their respective quantized centroids, and uses... |
Wang_Supervised_Quantization_for_CVPR_2016_paper | Supervised Quantization for Similarity Search | [
"Xiaojuan Wang",
"Ting Zhang",
"Guo-Jun Qi",
"Jinhui Tang",
"Jingdong Wang"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Wang_Supervised_Quantization_for_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Wang_Supervised_Quantization_for_CVPR_2016_paper.pdf | null | 1902.00617 | title_snapshot | @InProceedings{Wang_2016_CVPR,author = {Wang, Xiaojuan and Zhang, Ting and Qi, Guo-Jun and Tang, Jinhui and Wang, Jingdong},title = {Supervised Quantization for Similarity Search },booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | In this paper, we address the problem of searching for semantically similar images from a large database. We present a compact coding approach, supervised quantization. Our approach simultaneously learns feature selection that linearly transforms the database points into a low-dimensional discriminative subspace, and q... |
Wieschollek_Efficient_Large-Scale_Approximate_CVPR_2016_paper | Efficient Large-Scale Approximate Nearest Neighbor Search on the GPU | [
"Patrick Wieschollek",
"Oliver Wang",
"Alexander Sorkine-Hornung",
"Hendrik P. A. Lensch"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Wieschollek_Efficient_Large-Scale_Approximate_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Wieschollek_Efficient_Large-Scale_Approximate_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Wieschollek_Efficient_Large-Scale_Approximate_2016_CVPR_supplemental.pdf | 1702.05911 | title_snapshot | @InProceedings{Wieschollek_2016_CVPR,author = {Wieschollek, Patrick and Wang, Oliver and Sorkine-Hornung, Alexander and Lensch, Hendrik P. A.},title = {Efficient Large-Scale Approximate Nearest Neighbor Search on the GPU},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}... | We present a new approach for efficient approximate nearest neighbor (ANN) search in high dimensional spaces, extending the idea of Product Quantization. We propose a two level product and vector quantization tree that reduces the number of vector comparisons required during tree traversal. Our approach also includes a... |
Zhang_Collaborative_Quantization_for_CVPR_2016_paper | Collaborative Quantization for Cross-Modal Similarity Search | [
"Ting Zhang",
"Jingdong Wang"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Zhang_Collaborative_Quantization_for_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_Collaborative_Quantization_for_CVPR_2016_paper.pdf | null | 1902.00623 | title_snapshot | @InProceedings{Zhang_2016_CVPR,author = {Zhang, Ting and Wang, Jingdong},title = {Collaborative Quantization for Cross-Modal Similarity Search},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Cross-modal similarity search is a problem about designing a search system supporting querying across content modalities, e.g., using an image to search for texts or using a text to search for images. This paper presents a compact coding solution for efficient search, with a focus on the quantization approach which ... |
Tran_Aggregating_Image_and_CVPR_2016_paper | Aggregating Image and Text Quantized Correlated Components | [
"Thi Quynh Nhi Tran",
"Herve Le Borgne",
"Michel Crucianu"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Tran_Aggregating_Image_and_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Tran_Aggregating_Image_and_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Tran_Aggregating_Image_and_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Tran_2016_CVPR,author = {Tran, Thi Quynh Nhi and Le Borgne, Herve and Crucianu, Michel},title = {Aggregating Image and Text Quantized Correlated Components},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Cross-modal tasks occur naturally for multimedia content that can be described along two or more modalities like visual content and text. Such tasks require to "translate" information from one modality to another. Methods like kernelized canonical correlation analysis (KCCA) attempt to solve such tasks by finding align... |
Babenko_Efficient_Indexing_of_CVPR_2016_paper | Efficient Indexing of Billion-Scale Datasets of Deep Descriptors | [
"Artem Babenko",
"Victor Lempitsky"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Babenko_Efficient_Indexing_of_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Babenko_Efficient_Indexing_of_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Babenko_2016_CVPR,author = {Babenko, Artem and Lempitsky, Victor},title = {Efficient Indexing of Billion-Scale Datasets of Deep Descriptors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Existing billion-scale nearest neighbor search systems have mostly been compared on a single dataset of a billion of SIFT vectors, where systems based on the Inverted Multi-Index (IMI) have been performing very well, achieving state-of-the-art recall in several milliseconds. SIFT-like descriptors, however, are quickly ... |
Liu_Deep_Supervised_Hashing_CVPR_2016_paper | Deep Supervised Hashing for Fast Image Retrieval | [
"Haomiao Liu",
"Ruiping Wang",
"Shiguang Shan",
"Xilin Chen"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Liu_Deep_Supervised_Hashing_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Liu_Deep_Supervised_Hashing_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Liu_Deep_Supervised_Hashing_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Liu_2016_CVPR,author = {Liu, Haomiao and Wang, Ruiping and Shan, Shiguang and Chen, Xilin},title = {Deep Supervised Hashing for Fast Image Retrieval},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | In this paper, we present a new hashing method to learn compact binary codes for highly efficient image retrieval on large-scale datasets. While the complex image appearance variations still pose a great challenge to reliable retrieval, in light of the recent progress of Convolutional Neural Networks (CNNs) in learning... |
Iscen_Efficient_Large-Scale_Similarity_CVPR_2016_paper | Efficient Large-Scale Similarity Search Using Matrix Factorization | [
"Ahmet Iscen",
"Michael Rabbat",
"Teddy Furon"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Iscen_Efficient_Large-Scale_Similarity_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Iscen_Efficient_Large-Scale_Similarity_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Iscen_2016_CVPR,author = {Iscen, Ahmet and Rabbat, Michael and Furon, Teddy},title = {Efficient Large-Scale Similarity Search Using Matrix Factorization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We consider the image retrieval problem of finding the images in a dataset that are most similar to a query image. Our goal is to reduce the number of vector operations and memory for performing a search without sacrificing accuracy of the returned images. We adopt a group testing formulation and design the decoding ar... |
Kontogianni_Incremental_Object_Discovery_CVPR_2016_paper | Incremental Object Discovery in Time-Varying Image Collections | [
"Theodora Kontogianni",
"Markus Mathias",
"Bastian Leibe"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Kontogianni_Incremental_Object_Discovery_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Kontogianni_Incremental_Object_Discovery_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Kontogianni_Incremental_Object_Discovery_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Kontogianni_2016_CVPR,author = {Kontogianni, Theodora and Mathias, Markus and Leibe, Bastian},title = {Incremental Object Discovery in Time-Varying Image Collections},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Abstract In this paper, we address the problem of object discovery in time-varying, large-scale image collections. A core part of our approach is a novel Limited Horizon Minimum Spanning Tree (LH-MST) structure that closely approximates the Minimum Spanning Tree at a small fraction of the latter's computational cost. O... |
Huang_Detecting_Migrating_Birds_CVPR_2016_paper | Detecting Migrating Birds at Night | [
"Jia-Bin Huang",
"Rich Caruana",
"Andrew Farnsworth",
"Steve Kelling",
"Narendra Ahuja"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Huang_Detecting_Migrating_Birds_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Huang_Detecting_Migrating_Birds_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Huang_2016_CVPR,author = {Huang, Jia-Bin and Caruana, Rich and Farnsworth, Andrew and Kelling, Steve and Ahuja, Narendra},title = {Detecting Migrating Birds at Night},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Bird migration is a critical indicator of environmental health, biodiversity, and climate change. Existing techniques for monitoring bird migration are either expensive (e.g., satellite tracking), labor-intensive (e.g., moon watching), indirect and thus less accurate (e.g., weather radar), or intrusive (e.g., attaching... |
Kuzborskij_When_Naive_Bayes_CVPR_2016_paper | When Naive Bayes Nearest Neighbors Meet Convolutional Neural Networks | [
"Ilja Kuzborskij",
"Fabio Maria Carlucci",
"Barbara Caputo"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Kuzborskij_When_Naive_Bayes_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Kuzborskij_When_Naive_Bayes_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Kuzborskij_When_Naive_Bayes_2016_CVPR_supplemental.pdf | 1511.03853 | title_judge | @InProceedings{Kuzborskij_2016_CVPR,author = {Kuzborskij, Ilja and Carlucci, Fabio Maria and Caputo, Barbara},title = {When Naive Bayes Nearest Neighbors Meet Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Since Convolutional Neural Networks (CNNs) have become the leading learning paradigm in visual recognition, Naive Bayes Nearest Neighbor (NBNN)-based classifiers have lost momentum in the community. This is because (1) such algorithms cannot use CNN activations as input features; (2) they cannot be used as final layer ... |
Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper | Traffic-Sign Detection and Classification in the Wild | [
"Zhe Zhu",
"Dun Liang",
"Songhai Zhang",
"Xiaolei Huang",
"Baoli Li",
"Shimin Hu"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Zhu_Traffic-Sign_Detection_and_2016_CVPR_supplemental.zip | null | null | @InProceedings{Zhu_2016_CVPR,author = {Zhu, Zhe and Liang, Dun and Zhang, Songhai and Huang, Xiaolei and Li, Baoli and Hu, Shimin},title = {Traffic-Sign Detection and Classification in the Wild},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {201... | Although promising results have been achieved in the areas of traffic-sign detection and classification, few works have provided simultaneous solutions to these two tasks for realistic real world images. We make two contributions to this problem. Firstly, we have created a large traffic-sign benchmark from 100000 Tence... |
Tang_Large_Scale_Semi-Supervised_CVPR_2016_paper | Large Scale Semi-Supervised Object Detection Using Visual and Semantic Knowledge Transfer | [
"Yuxing Tang",
"Josiah Wang",
"Boyang Gao",
"Emmanuel Dellandrea",
"Robert Gaizauskas",
"Liming Chen"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Tang_Large_Scale_Semi-Supervised_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Tang_Large_Scale_Semi-Supervised_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Tang_2016_CVPR,author = {Tang, Yuxing and Wang, Josiah and Gao, Boyang and Dellandrea, Emmanuel and Gaizauskas, Robert and Chen, Liming},title = {Large Scale Semi-Supervised Object Detection Using Visual and Semantic Knowledge Transfer},booktitle = {Proceedings of the IEEE Conference on Computer Vision a... | Deep CNN-based object detection systems have achieved remarkable success on several large-scale object detection benchmarks. However, training such detectors requires a large number of labeled bounding boxes, which are more difficult to obtain than image-level annotations. Previous work addresses this issue by transfor... |
Yang_Exploit_All_the_CVPR_2016_paper | Exploit All the Layers: Fast and Accurate CNN Object Detector With Scale Dependent Pooling and Cascaded Rejection Classifiers | [
"Fan Yang",
"Wongun Choi",
"Yuanqing Lin"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Yang_Exploit_All_the_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Yang_Exploit_All_the_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Yang_2016_CVPR,author = {Yang, Fan and Choi, Wongun and Lin, Yuanqing},title = {Exploit All the Layers: Fast and Accurate CNN Object Detector With Scale Dependent Pooling and Cascaded Rejection Classifiers},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}... | In this paper, we investigate two new strategies to detect objects accurately and efficiently using deep convolutional neural network: 1) scale-dependent pooling and 2) layer-wise cascaded rejection classifiers. The scale-dependent pooling (SDP) improves detection accuracy by exploiting appropriate convolutional featur... |
Wang_Dictionary_Pair_Classifier_CVPR_2016_paper | Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection | [
"Keze Wang",
"Liang Lin",
"Wangmeng Zuo",
"Shuhang Gu",
"Lei Zhang"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Wang_Dictionary_Pair_Classifier_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Wang_Dictionary_Pair_Classifier_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Wang_2016_CVPR,author = {Wang, Keze and Lin, Liang and Zuo, Wangmeng and Gu, Shuhang and Zhang, Lei},title = {Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {J... | Feature representation and object category classification are two key components of most object detection methods. While significant improvements have been achieved for deep feature representation learning, traditional SVM/softmax classifiers remain the dominant methods for final object category classification. However... |
Chen_Monocular_3D_Object_CVPR_2016_paper | Monocular 3D Object Detection for Autonomous Driving | [
"Xiaozhi Chen",
"Kaustav Kundu",
"Ziyu Zhang",
"Huimin Ma",
"Sanja Fidler",
"Raquel Urtasun"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Chen_Monocular_3D_Object_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Chen_Monocular_3D_Object_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Chen_Monocular_3D_Object_2016_CVPR_supplemental.zip | null | null | @InProceedings{Chen_2016_CVPR,author = {Chen, Xiaozhi and Kundu, Kaustav and Zhang, Ziyu and Ma, Huimin and Fidler, Sanja and Urtasun, Raquel},title = {Monocular 3D Object Detection for Autonomous Driving},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},... | The goal of this paper is to perform 3D object detection in single monocular images in the domain of autonomous driving. Our method first aims to generate a set of candidate class-specific object proposals, which are then run through a standard CNN pipeline to obtain high-quality object detections. The focus of this p... |
Ionescu_How_Hard_Can_CVPR_2016_paper | How Hard Can It Be? Estimating the Difficulty of Visual Search in an Image | [
"Radu Tudor Ionescu",
"Bogdan Alexe",
"Marius Leordeanu",
"Marius Popescu",
"Dim P. Papadopoulos",
"Vittorio Ferrari"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Ionescu_How_Hard_Can_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Ionescu_How_Hard_Can_CVPR_2016_paper.pdf | null | 1705.08280 | title_snapshot | @InProceedings{Ionescu_2016_CVPR,author = {Ionescu, Radu Tudor and Alexe, Bogdan and Leordeanu, Marius and Popescu, Marius and Papadopoulos, Dim P. and Ferrari, Vittorio},title = {How Hard Can It Be? Estimating the Difficulty of Visual Search in an Image},booktitle = {Proceedings of the IEEE Conference on Computer Visi... | We address the problem of estimating image difficulty defined as the human response time for solving a visual search task. We collect human annotations of image difficulty for the PASCAL VOC 2012 data set through a crowd-sourcing platform. We then analyze what human interpretable image properties can have an impact on ... |
Liu_Deep_Relative_Distance_CVPR_2016_paper | Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles | [
"Hongye Liu",
"Yonghong Tian",
"Yaowei Yang",
"Lu Pang",
"Tiejun Huang"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Liu_Deep_Relative_Distance_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Liu_Deep_Relative_Distance_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Liu_2016_CVPR,author = {Liu, Hongye and Tian, Yonghong and Yang, Yaowei and Pang, Lu and Huang, Tiejun},title = {Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June}... | The growing explosion in the use of surveillance cameras in public security highlights the importance of vehicle search from a large-scale image or video database. However, compared with person re-identification or face recognition, vehicle search problem has long been neglected by researchers in vision community. This... |
Krafka_Eye_Tracking_for_CVPR_2016_paper | Eye Tracking for Everyone | [
"Kyle Krafka",
"Aditya Khosla",
"Petr Kellnhofer",
"Harini Kannan",
"Suchendra Bhandarkar",
"Wojciech Matusik",
"Antonio Torralba"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Krafka_Eye_Tracking_for_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Krafka_Eye_Tracking_for_CVPR_2016_paper.pdf | null | 1606.05814 | title_snapshot | @InProceedings{Krafka_2016_CVPR,author = {Krafka, Kyle and Khosla, Aditya and Kellnhofer, Petr and Kannan, Harini and Bhandarkar, Suchendra and Matusik, Wojciech and Torralba, Antonio},title = {Eye Tracking for Everyone},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},... | From scientific research to commercial applications, eye tracking is an important tool across many domains. Despite its range of applications, eye tracking has yet to become a pervasive technology. We believe that we can put the power of eye tracking in everyone's palm by building eye tracking software that works on co... |
Lahner_Efficient_Globally_Optimal_CVPR_2016_paper | Efficient Globally Optimal 2D-To-3D Deformable Shape Matching | [
"Zorah Lahner",
"Emanuele Rodola",
"Frank R. Schmidt",
"Michael M. Bronstein",
"Daniel Cremers"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Lahner_Efficient_Globally_Optimal_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Lahner_Efficient_Globally_Optimal_CVPR_2016_paper.pdf | null | 1601.06070 | title_snapshot | @InProceedings{Lahner_2016_CVPR,author = {Lahner, Zorah and Rodola, Emanuele and Schmidt, Frank R. and Bronstein, Michael M. and Cremers, Daniel},title = {Efficient Globally Optimal 2D-To-3D Deformable Shape Matching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},mon... | We propose the first algorithm for non-rigid 2D-to-3D shape matching, where the input is a 2D query shape as well as a 3D target shape and the output is a continuous matching curve represented as a closed contour on the 3D shape. We cast the problem as finding the shortest circular path on the product 3-manifold of the... |
Sharmanska_Ambiguity_Helps_Classification_CVPR_2016_paper | Ambiguity Helps: Classification With Disagreements in Crowdsourced Annotations | [
"Viktoriia Sharmanska",
"Daniel Hernandez-Lobato",
"Jose Miguel Hernandez-Lobato",
"Novi Quadrianto"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Sharmanska_Ambiguity_Helps_Classification_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Sharmanska_Ambiguity_Helps_Classification_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Sharmanska_Ambiguity_Helps_Classification_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Sharmanska_2016_CVPR,author = {Sharmanska, Viktoriia and Hernandez-Lobato, Daniel and Hernandez-Lobato, Jose Miguel and Quadrianto, Novi},title = {Ambiguity Helps: Classification With Disagreements in Crowdsourced Annotations},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern... | Imagine we show an image to a person and ask her/him to decide whether the scene in the image is warm or not warm, and whether it is easy or not to spot a squirrel in the image. For exactly the same image, the answers to those questions are likely to differ from person to person. This is because the task is inherently ... |
Mottaghi_A_Task-Oriented_Approach_CVPR_2016_paper | A Task-Oriented Approach for Cost-Sensitive Recognition | [
"Roozbeh Mottaghi",
"Hannaneh Hajishirzi",
"Ali Farhadi"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Mottaghi_A_Task-Oriented_Approach_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Mottaghi_A_Task-Oriented_Approach_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Mottaghi_A_Task-Oriented_Approach_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Mottaghi_2016_CVPR,author = {Mottaghi, Roozbeh and Hajishirzi, Hannaneh and Farhadi, Ali},title = {A Task-Oriented Approach for Cost-Sensitive Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | With the recent progress in visual recognition, we have already started to see a surge of vision related real-world applications. These applications, unlike general scene understanding, are task oriented and require specific information from visual data. Considering the current growth in new sensory devices, feature de... |
Shankar_Refining_Architectures_of_CVPR_2016_paper | Refining Architectures of Deep Convolutional Neural Networks | [
"Sukrit Shankar",
"Duncan Robertson",
"Yani Ioannou",
"Antonio Criminisi",
"Roberto Cipolla"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Shankar_Refining_Architectures_of_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Shankar_Refining_Architectures_of_CVPR_2016_paper.pdf | null | 1604.06832 | title_snapshot | @InProceedings{Shankar_2016_CVPR,author = {Shankar, Sukrit and Robertson, Duncan and Ioannou, Yani and Criminisi, Antonio and Cipolla, Roberto},title = {Refining Architectures of Deep Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month ... | Deep Convolutional Neural Networks (CNNs) have recently evinced immense success for various image recognition tasks. However, a question of paramount importance is somewhat unanswered in deep learning research - is the selected CNN optimal for the dataset in terms of accuracy and model size? In this paper, we intend t... |
Borji_iLab-20M_A_Large-Scale_CVPR_2016_paper | iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning | [
"Ali Borji",
"Saeed Izadi",
"Laurent Itti"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Borji_iLab-20M_A_Large-Scale_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Borji_iLab-20M_A_Large-Scale_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Borji_iLab-20M_A_Large-Scale_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Borji_2016_CVPR,author = {Borji, Ali and Izadi, Saeed and Itti, Laurent},title = {iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Tolerance to image variations (e.g. translation, scale, pose, illumination, background) is an important desired property of any object recognition system, be it human or machine. Moving towards increasingly bigger datasets has been trending in computer vision especially with the emergence of highly popular deep learnin... |
Lee_Recursive_Recurrent_Nets_CVPR_2016_paper | Recursive Recurrent Nets With Attention Modeling for OCR in the Wild | [
"Chen-Yu Lee",
"Simon Osindero"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Lee_Recursive_Recurrent_Nets_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Lee_Recursive_Recurrent_Nets_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Lee_Recursive_Recurrent_Nets_2016_CVPR_supplemental.pdf | 1603.03101 | title_snapshot | @InProceedings{Lee_2016_CVPR,author = {Lee, Chen-Yu and Osindero, Simon},title = {Recursive Recurrent Nets With Attention Modeling for OCR in the Wild},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We present recursive recurrent neural networks with attention modeling (R2AM) for lexicon-free optical character recognition in natural scene images. The primary advantages of the proposed method are: (1) use of recursive convolutional neural networks (CNNs), which allow for parametrically efficient and effective image... |
Murthy_Deep_Decision_Network_CVPR_2016_paper | Deep Decision Network for Multi-Class Image Classification | [
"Venkatesh N. Murthy",
"Vivek Singh",
"Terrence Chen",
"R. Manmatha",
"Dorin Comaniciu"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Murthy_Deep_Decision_Network_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Murthy_Deep_Decision_Network_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Murthy_2016_CVPR,author = {Murthy, Venkatesh N. and Singh, Vivek and Chen, Terrence and Manmatha, R. and Comaniciu, Dorin},title = {Deep Decision Network for Multi-Class Image Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June}... | In this paper, we present a novel Deep Decision Network (DDN) that provides an alternative approach towards building an efficient deep learning network. During the learning phase, starting from the root network node, DDN automatically builds a network that splits the data into disjoint clusters of classes which would b... |
Qiao_Less_Is_More_CVPR_2016_paper | Less Is More: Zero-Shot Learning From Online Textual Documents With Noise Suppression | [
"Ruizhi Qiao",
"Lingqiao Liu",
"Chunhua Shen",
"Anton van den Hengel"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Qiao_Less_Is_More_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Qiao_Less_Is_More_CVPR_2016_paper.pdf | null | 1604.01146 | title_snapshot | @InProceedings{Qiao_2016_CVPR,author = {Qiao, Ruizhi and Liu, Lingqiao and Shen, Chunhua and van den Hengel, Anton},title = {Less Is More: Zero-Shot Learning From Online Textual Documents With Noise Suppression},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {... | Classifying a visual concept merely from its associated online textual source, such as a Wikipedia article, is an attractive research topic in zero-shot learning because it alleviates the burden of manually collecting semantic attributes. Several recent works have pursued this approach by exploring various ways of conn... |
Li_Fast_Algorithms_for_CVPR_2016_paper | Fast Algorithms for Linear and Kernel SVM+ | [
"Wen Li",
"Dengxin Dai",
"Mingkui Tan",
"Dong Xu",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Li_Fast_Algorithms_for_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Li_Fast_Algorithms_for_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Li_2016_CVPR,author = {Li, Wen and Dai, Dengxin and Tan, Mingkui and Xu, Dong and Van Gool, Luc},title = {Fast Algorithms for Linear and Kernel SVM+},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | The SVM+ approach has shown excellent performance in visual recognition tasks for exploiting privileged information in the training data. In this paper, we propose two efficient algorithms for solving the linear and kernel SVM+, respectively. For linear SVM+, we absorb the bias term into the weight vector, and formul... |
Qi_Hierarchically_Gated_Deep_CVPR_2016_paper | Hierarchically Gated Deep Networks for Semantic Segmentation | [
"Guo-Jun Qi"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Qi_Hierarchically_Gated_Deep_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Qi_Hierarchically_Gated_Deep_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Qi_2016_CVPR,author = {Qi, Guo-Jun},title = {Hierarchically Gated Deep Networks for Semantic Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Semantic segmentation aims to parse the scene structure of images by annotating the labels to each pixel so that images can be segmented into different regions. While image structures usually have various scales, it is difficult to use a single scale to model the spatial contexts for all individual pixels. Multi-sca... |
Lin_Deep_Structured_Scene_CVPR_2016_paper | Deep Structured Scene Parsing by Learning With Image Descriptions | [
"Liang Lin",
"Guangrun Wang",
"Rui Zhang",
"Ruimao Zhang",
"Xiaodan Liang",
"Wangmeng Zuo"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Lin_Deep_Structured_Scene_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Lin_Deep_Structured_Scene_CVPR_2016_paper.pdf | null | 1604.02271 | title_snapshot | @InProceedings{Lin_2016_CVPR,author = {Lin, Liang and Wang, Guangrun and Zhang, Rui and Zhang, Ruimao and Liang, Xiaodan and Zuo, Wangmeng},title = {Deep Structured Scene Parsing by Learning With Image Descriptions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month... | This paper addresses the problem of structured scene parsing, i.e., parsing the input scene into a configuration including hierarchical semantic objects with their interaction relations. We propose a deep architecture consisting of two networks: i) a convolutional neural network (CNN) extracting the image representatio... |
Wang_CNN-RNN_A_Unified_CVPR_2016_paper | CNN-RNN: A Unified Framework for Multi-Label Image Classification | [
"Jiang Wang",
"Yi Yang",
"Junhua Mao",
"Zhiheng Huang",
"Chang Huang",
"Wei Xu"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Wang_CNN-RNN_A_Unified_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Wang_CNN-RNN_A_Unified_CVPR_2016_paper.pdf | null | 1604.04573 | title_snapshot | @InProceedings{Wang_2016_CVPR,author = {Wang, Jiang and Yang, Yi and Mao, Junhua and Huang, Zhiheng and Huang, Chang and Xu, Wei},title = {CNN-RNN: A Unified Framework for Multi-Label Image Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},... | While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that most real world images contain multiple labels, which could correspond to different objects, scenes, actions and attributes in an image. Traditional approaches to multi-label ima... |
Wang_Walk_and_Learn_CVPR_2016_paper | Walk and Learn: Facial Attribute Representation Learning From Egocentric Video and Contextual Data | [
"Jing Wang",
"Yu Cheng",
"Rogerio Schmidt Feris"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Wang_Walk_and_Learn_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Wang_Walk_and_Learn_CVPR_2016_paper.pdf | null | 1604.06433 | title_snapshot | @InProceedings{Wang_2016_CVPR,author = {Wang, Jing and Cheng, Yu and Feris, Rogerio Schmidt},title = {Walk and Learn: Facial Attribute Representation Learning From Egocentric Video and Contextual Data},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year... | The way people look in terms of facial attributes (ethnicity, hair color, facial hair, etc.) and the clothes or accessories they wear (sunglasses, hat, hoodies, etc.) is highly dependent on geo-location and weather condition, respectively. This work explores, for the first time, the use of this contextual information, ... |
Poznanski_CNN-N-Gram_for_Handwriting_CVPR_2016_paper | CNN-N-Gram for Handwriting Word Recognition | [
"Arik Poznanski",
"Lior Wolf"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Poznanski_CNN-N-Gram_for_Handwriting_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Poznanski_CNN-N-Gram_for_Handwriting_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Poznanski_2016_CVPR,author = {Poznanski, Arik and Wolf, Lior},title = {CNN-N-Gram for Handwriting Word Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Given an image of a handwritten word, a CNN is employed to estimate its n-gram frequency profile, which is the set of n-grams contained in the word. Frequencies for unigrams, bigrams and trigrams are estimated for the entire word and for parts of it. Canonical Correlation Analysis is then used to match the estimated pr... |
Gupta_Synthetic_Data_for_CVPR_2016_paper | Synthetic Data for Text Localisation in Natural Images | [
"Ankush Gupta",
"Andrea Vedaldi",
"Andrew Zisserman"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Gupta_Synthetic_Data_for_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Gupta_Synthetic_Data_for_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Gupta_Synthetic_Data_for_2016_CVPR_supplemental.pdf | 1604.06646 | title_snapshot | @InProceedings{Gupta_2016_CVPR,author = {Gupta, Ankush and Vedaldi, Andrea and Zisserman, Andrew},title = {Synthetic Data for Text Localisation in Natural Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | In this paper we introduce a new method for text detection in natural images. The method comprises two contributions: First, a fast and scalable engine to generate synthetic images of text in clutter. This engine overlays synthetic text to existing background images in a natural way, accounting for the local 3D scene ... |
Stewart_End-To-End_People_Detection_CVPR_2016_paper | End-To-End People Detection in Crowded Scenes | [
"Russell Stewart",
"Mykhaylo Andriluka",
"Andrew Y. Ng"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Stewart_End-To-End_People_Detection_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Stewart_End-To-End_People_Detection_CVPR_2016_paper.pdf | null | 1506.04878 | title_snapshot | @InProceedings{Stewart_2016_CVPR,author = {Stewart, Russell and Andriluka, Mykhaylo and Ng, Andrew Y.},title = {End-To-End People Detection in Crowded Scenes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Current people detectors operate either by scanning an image in a sliding window fashion or by classifying a discrete set of proposals. We propose a model that is based on decoding an image into a set of people detections. Our system takes an image as input and directly outputs a set of distinct detection hypotheses. B... |
Tu_Real-Time_Salient_Object_CVPR_2016_paper | Real-Time Salient Object Detection With a Minimum Spanning Tree | [
"Wei-Chih Tu",
"Shengfeng He",
"Qingxiong Yang",
"Shao-Yi Chien"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Tu_Real-Time_Salient_Object_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Tu_Real-Time_Salient_Object_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Tu_2016_CVPR,author = {Tu, Wei-Chih and He, Shengfeng and Yang, Qingxiong and Chien, Shao-Yi},title = {Real-Time Salient Object Detection With a Minimum Spanning Tree},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | In this paper, we present a real-time salient object detection system based on the minimum spanning tree. Due to the fact that background regions are typically connected to the image boundaries, salient objects can be extracted by computing the distances to the boundaries. However, measuring the image boundary connecti... |
Feng_Local_Background_Enclosure_CVPR_2016_paper | Local Background Enclosure for RGB-D Salient Object Detection | [
"David Feng",
"Nick Barnes",
"Shaodi You",
"Chris McCarthy"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Feng_Local_Background_Enclosure_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Feng_Local_Background_Enclosure_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Feng_Local_Background_Enclosure_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Feng_2016_CVPR,author = {Feng, David and Barnes, Nick and You, Shaodi and McCarthy, Chris},title = {Local Background Enclosure for RGB-D Salient Object Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Recent work in salient object detection has considered the incorporation of depth cues from RGB-D images. In most cases, depth contrast is used as the main feature. However, areas of high contrast in background regions cause false positives for such methods, as the background frequently contains regions that are highly... |
Lu_Adaptive_Object_Detection_CVPR_2016_paper | Adaptive Object Detection Using Adjacency and Zoom Prediction | [
"Yongxi Lu",
"Tara Javidi",
"Svetlana Lazebnik"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Lu_Adaptive_Object_Detection_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Lu_Adaptive_Object_Detection_CVPR_2016_paper.pdf | null | 1512.07711 | title_snapshot | @InProceedings{Lu_2016_CVPR,author = {Lu, Yongxi and Javidi, Tara and Lazebnik, Svetlana},title = {Adaptive Object Detection Using Adjacency and Zoom Prediction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | State-of-the-art object detection systems rely on an accurate set of region proposals. Several recent methods use a neural network architecture to hypothesize promising object locations. While these approaches are computationally efficient, they rely on fixed image regions as anchors for predictions. In this paper we p... |
Costea_Semantic_Channels_for_CVPR_2016_paper | Semantic Channels for Fast Pedestrian Detection | [
"Arthur Daniel Costea",
"Sergiu Nedevschi"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Costea_Semantic_Channels_for_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Costea_Semantic_Channels_for_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Costea_2016_CVPR,author = {Costea, Arthur Daniel and Nedevschi, Sergiu},title = {Semantic Channels for Fast Pedestrian Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Pedestrian detection and semantic segmentation are high potential tasks for many real-time applications. However most of the top performing approaches provide state of art results at high computational costs. In this work we propose a fast solution for achieving state of art results for both pedestrian detection and se... |
Najibi_G-CNN_An_Iterative_CVPR_2016_paper | G-CNN: An Iterative Grid Based Object Detector | [
"Mahyar Najibi",
"Mohammad Rastegari",
"Larry S. Davis"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Najibi_G-CNN_An_Iterative_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Najibi_G-CNN_An_Iterative_CVPR_2016_paper.pdf | null | 1512.07729 | title_snapshot | @InProceedings{Najibi_2016_CVPR,author = {Najibi, Mahyar and Rastegari, Mohammad and Davis, Larry S.},title = {G-CNN: An Iterative Grid Based Object Detector},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We introduce G-CNN, an object detection technique based on CNNs which works without proposal algorithms. G-CNN starts with a multi-scale grid of fixed bounding boxes. We train a regressor to move and scale elements of the grid towards objects iteratively. G-CNN models the problem of object detection as finding a path f... |
Wang_Recurrent_Face_Aging_CVPR_2016_paper | Recurrent Face Aging | [
"Wei Wang",
"Zhen Cui",
"Yan Yan",
"Jiashi Feng",
"Shuicheng Yan",
"Xiangbo Shu",
"Nicu Sebe"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Wang_Recurrent_Face_Aging_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Wang_Recurrent_Face_Aging_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Wang_2016_CVPR,author = {Wang, Wei and Cui, Zhen and Yan, Yan and Feng, Jiashi and Yan, Shuicheng and Shu, Xiangbo and Sebe, Nicu},title = {Recurrent Face Aging},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Modeling the aging process of human face is important for cross-age face verification and recognition. In this paper, we introduce a recurrent face aging (RFA) framework based on a recurrent neural network which can identify the ages of people from 0 to 80. Due to the lack of labeled face data of the same person captur... |
Thies_Face2Face_Real-Time_Face_CVPR_2016_paper | Face2Face: Real-Time Face Capture and Reenactment of RGB Videos | [
"Justus Thies",
"Michael Zollhofer",
"Marc Stamminger",
"Christian Theobalt",
"Matthias Niessner"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Thies_Face2Face_Real-Time_Face_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Thies_Face2Face_Real-Time_Face_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Thies_Face2Face_Real-Time_Face_2016_CVPR_supplemental.pdf | 2007.14808 | title_snapshot | @InProceedings{Thies_2016_CVPR,author = {Thies, Justus and Zollhofer, Michael and Stamminger, Marc and Theobalt, Christian and Niessner, Matthias},title = {Face2Face: Real-Time Face Capture and Reenactment of RGB Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},... | We present a novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). The source sequence is also a monocular video stream, captured live with a commodity webcam. Our goal is to animate the facial expressions of the target video by a source actor and re-render the mani... |
Tulyakov_Self-Adaptive_Matrix_Completion_CVPR_2016_paper | Self-Adaptive Matrix Completion for Heart Rate Estimation From Face Videos Under Realistic Conditions | [
"Sergey Tulyakov",
"Xavier Alameda-Pineda",
"Elisa Ricci",
"Lijun Yin",
"Jeffrey F. Cohn",
"Nicu Sebe"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Tulyakov_Self-Adaptive_Matrix_Completion_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Tulyakov_Self-Adaptive_Matrix_Completion_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Tulyakov_2016_CVPR,author = {Tulyakov, Sergey and Alameda-Pineda, Xavier and Ricci, Elisa and Yin, Lijun and Cohn, Jeffrey F. and Sebe, Nicu},title = {Self-Adaptive Matrix Completion for Heart Rate Estimation From Face Videos Under Realistic Conditions},booktitle = {Proceedings of the IEEE Conference on ... | Recent studies in computer vision have shown that, while practically invisible to a human observer, skin color changes due to blood flow can be captured on face videos and, surprisingly, be used to estimate the heart rate (HR). While considerable progress has been made in the last few years, still many issues remain op... |
Owens_Visually_Indicated_Sounds_CVPR_2016_paper | Visually Indicated Sounds | [
"Andrew Owens",
"Phillip Isola",
"Josh McDermott",
"Antonio Torralba",
"Edward H. Adelson",
"William T. Freeman"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Owens_Visually_Indicated_Sounds_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Owens_Visually_Indicated_Sounds_CVPR_2016_paper.pdf | null | 1512.08512 | title_snapshot | @InProceedings{Owens_2016_CVPR,author = {Owens, Andrew and Isola, Phillip and McDermott, Josh and Torralba, Antonio and Adelson, Edward H. and Freeman, William T.},title = {Visually Indicated Sounds},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year =... | Objects make distinctive sounds when they are hit or scratched. These sounds reveal aspects of an object's material properties, as well as the actions that produced them. In this paper, we propose the task of predicting what sound an object makes when struck as a way of studying physical interactions within a visual sc... |
Gatys_Image_Style_Transfer_CVPR_2016_paper | Image Style Transfer Using Convolutional Neural Networks | [
"Leon A. Gatys",
"Alexander S. Ecker",
"Matthias Bethge"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Gatys_Image_Style_Transfer_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Gatys_2016_CVPR,author = {Gatys, Leon A. and Ecker, Alexander S. and Bethge, Matthias},title = {Image Style Transfer Using Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic information and, thus, allow to separate image content from style. Here we use imag... |
Hou_Patch-Based_Convolutional_Neural_CVPR_2016_paper | Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification | [
"Le Hou",
"Dimitris Samaras",
"Tahsin M. Kurc",
"Yi Gao",
"James E. Davis",
"Joel H. Saltz"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Hou_Patch-Based_Convolutional_Neural_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Hou_Patch-Based_Convolutional_Neural_CVPR_2016_paper.pdf | null | 1504.07947 | title_snapshot | @InProceedings{Hou_2016_CVPR,author = {Hou, Le and Samaras, Dimitris and Kurc, Tahsin M. and Gao, Yi and Davis, James E. and Saltz, Joel H.},title = {Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recog... | Convolutional Neural Networks (CNN) are state-of-the-art models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cel... |
Isack_Hedgehog_Shape_Priors_CVPR_2016_paper | Hedgehog Shape Priors for Multi-Object Segmentation | [
"Hossam Isack",
"Olga Veksler",
"Milan Sonka",
"Yuri Boykov"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Isack_Hedgehog_Shape_Priors_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Isack_Hedgehog_Shape_Priors_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Isack_2016_CVPR,author = {Isack, Hossam and Veksler, Olga and Sonka, Milan and Boykov, Yuri},title = {Hedgehog Shape Priors for Multi-Object Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Star-convexity prior is popular for interactive single object segmentation due to its simplicity and amenability to binary graph cut optimization. We propose a more general multi-object segmentation approach. Moreover, each object can be constrained by a more descriptive shape prior, "hedgehog". Each hedgehog shape has... |
Kim_Latent_Variable_Graphical_CVPR_2016_paper | Latent Variable Graphical Model Selection Using Harmonic Analysis: Applications to the Human Connectome Project (HCP) | [
"Won Hwa Kim",
"Hyunwoo J. Kim",
"Nagesh Adluru",
"Vikas Singh"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Kim_Latent_Variable_Graphical_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Kim_Latent_Variable_Graphical_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Kim_2016_CVPR,author = {Kim, Won Hwa and Kim, Hyunwoo J. and Adluru, Nagesh and Singh, Vikas},title = {Latent Variable Graphical Model Selection Using Harmonic Analysis: Applications to the Human Connectome Project (HCP)},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Reco... | A major goal of imaging studies such as the (ongoing) Human Connectome Project (HCP) is to characterize the structural network map of the human brain and identify its associations with covariates such as genotype, risk factors, and so on that correspond to an individual. But the set of image derived measures and the se... |
Choe_Simultaneous_Estimation_of_CVPR_2016_paper | Simultaneous Estimation of Near IR BRDF and Fine-Scale Surface Geometry | [
"Gyeongmin Choe",
"Srinivasa G. Narasimhan",
"In So Kweon"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Choe_Simultaneous_Estimation_of_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Choe_Simultaneous_Estimation_of_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Choe_2016_CVPR,author = {Choe, Gyeongmin and Narasimhan, Srinivasa G. and Kweon, In So},title = {Simultaneous Estimation of Near IR BRDF and Fine-Scale Surface Geometry},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Near-Infrared (NIR) images of most materials exhibit less texture or albedo variations making them beneficial for vision tasks such as intrinsic image decomposition and structured light depth estimation. Understanding the reflectance properties (BRDF) of materials in the NIR wavelength range can be further useful for m... |
Oh_Do_It_Yourself_CVPR_2016_paper | Do It Yourself Hyperspectral Imaging With Everyday Digital Cameras | [
"Seoung Wug Oh",
"Michael S. Brown",
"Marc Pollefeys",
"Seon Joo Kim"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Oh_Do_It_Yourself_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Oh_Do_It_Yourself_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Oh_Do_It_Yourself_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Oh_2016_CVPR,author = {Oh, Seoung Wug and Brown, Michael S. and Pollefeys, Marc and Kim, Seon Joo},title = {Do It Yourself Hyperspectral Imaging With Everyday Digital Cameras},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Capturing hyperspectral images requires expensive and specialized hardware that is not readily accessible to most users. Digital cameras, on the other hand, are significantly cheaper in comparison and can be easily purchased and used. In this paper, we present a framework for reconstructing hyperspectral images by usin... |
Lee_Automatic_Content-Aware_Color_CVPR_2016_paper | Automatic Content-Aware Color and Tone Stylization | [
"Joon-Young Lee",
"Kalyan Sunkavalli",
"Zhe Lin",
"Xiaohui Shen",
"In So Kweon"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Lee_Automatic_Content-Aware_Color_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Lee_Automatic_Content-Aware_Color_CVPR_2016_paper.pdf | null | 1511.03748 | title_snapshot | @InProceedings{Lee_2016_CVPR,author = {Lee, Joon-Young and Sunkavalli, Kalyan and Lin, Zhe and Shen, Xiaohui and Kweon, In So},title = {Automatic Content-Aware Color and Tone Stylization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We introduce a new technique that automatically generates diverse, visually compelling stylizations for a photograph in an unsupervised manner. We achieve this by learning style ranking for a given input using a large photo collection and selecting a diverse subset of matching styles for final style transfer. We also p... |
Li_Combining_Markov_Random_CVPR_2016_paper | Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis | [
"Chuan Li",
"Michael Wand"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Li_Combining_Markov_Random_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Li_Combining_Markov_Random_CVPR_2016_paper.pdf | null | 1601.04589 | title_snapshot | @InProceedings{Li_2016_CVPR,author = {Li, Chuan and Wand, Michael},title = {Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | This paper studies a combination of generative Markov random field (MRF) models and discriminatively trained deep convolutional neural networks (dCNNs) for synthesizing 2D images. The generative MRF acts on higher-levels of a dCNN feature pyramid, controling the image layout at an abstract level. We apply the method to... |
Chen_DCAN_Deep_Contour-Aware_CVPR_2016_paper | DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation | [
"Hao Chen",
"Xiaojuan Qi",
"Lequan Yu",
"Pheng-Ann Heng"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Chen_DCAN_Deep_Contour-Aware_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Chen_DCAN_Deep_Contour-Aware_CVPR_2016_paper.pdf | null | 1604.02677 | title_snapshot | @InProceedings{Chen_2016_CVPR,author = {Chen, Hao and Qi, Xiaojuan and Yu, Lequan and Heng, Pheng-Ann},title = {DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | The morphology of glands has been used routinely by pathologists to assess the malignancy degree of adenocarcinomas. Accurate segmentation of glands from histology images is a crucial step to obtain reliable morphological statistics for quantitative diagnosis. In this paper, we proposed an efficient deep contour-aware ... |
Shin_Learning_to_Read_CVPR_2016_paper | Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation | [
"Hoo-Chang Shin",
"Kirk Roberts",
"Le Lu",
"Dina Demner-Fushman",
"Jianhua Yao",
"Ronald M. Summers"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Shin_Learning_to_Read_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Shin_Learning_to_Read_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Shin_Learning_to_Read_2016_CVPR_supplemental.pdf | 1603.08486 | title_snapshot | @InProceedings{Shin_2016_CVPR,author = {Shin, Hoo-Chang and Roberts, Kirk and Lu, Le and Demner-Fushman, Dina and Yao, Jianhua and Summers, Ronald M.},title = {Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation},booktitle = {Proceedings of the IEEE Conference on Computer Vision... | Despite the recent advances in automatically describing image contents, their applications have been mostly limited to image caption datasets containing natural images (e.g., Flickr 30k, MSCOCO). In this paper, we present a deep learning model to efficiently detect a disease from an image and annotate its contexts (e.g... |
Le_Conformal_Surface_Alignment_CVPR_2016_paper | Conformal Surface Alignment With Optimal Mobius Search | [
"Huu Le",
"Tat-Jun Chin",
"David Suter"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Le_Conformal_Surface_Alignment_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Le_Conformal_Surface_Alignment_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Le_Conformal_Surface_Alignment_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Le_2016_CVPR,author = {Le, Huu and Chin, Tat-Jun and Suter, David},title = {Conformal Surface Alignment With Optimal Mobius Search},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Deformations of surfaces with the same intrinsic shape can often be described accurately by a conformal model. A major focus of computational conformal geometry is the estimation of the conformal mapping that aligns a given pair of object surfaces. The uniformization theorem en- ables this task to be acccomplished in a... |
Hwang_Coupled_Harmonic_Bases_CVPR_2016_paper | Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks | [
"Seong Jae Hwang",
"Nagesh Adluru",
"Maxwell D. Collins",
"Sathya N. Ravi",
"Barbara B. Bendlin",
"Sterling C. Johnson",
"Vikas Singh"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Hwang_Coupled_Harmonic_Bases_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Hwang_Coupled_Harmonic_Bases_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Hwang_2016_CVPR,author = {Hwang, Seong Jae and Adluru, Nagesh and Collins, Maxwell D. and Ravi, Sathya N. and Bendlin, Barbara B. and Johnson, Sterling C. and Singh, Vikas},title = {Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks},booktitle = {Proceedings of the IEEE Conference... | There is a great deal of interest in using large scale brain imaging studies to understand how brain connectivity evolves over time for an individual and how it varies over different levels/quantiles of cognitive function. To do so, one typically performs so-called tractography procedures on diffusion MR brain images a... |
Shin_Automating_Carotid_Intima-Media_CVPR_2016_paper | Automating Carotid Intima-Media Thickness Video Interpretation With Convolutional Neural Networks | [
"Jae Shin",
"Nima Tajbakhsh",
"R. Todd Hurst",
"Christopher B. Kendall",
"Jianming Liang"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Shin_Automating_Carotid_Intima-Media_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Shin_Automating_Carotid_Intima-Media_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Shin_Automating_Carotid_Intima-Media_2016_CVPR_supplemental.pdf | 1706.00719 | title_snapshot | @InProceedings{Shin_2016_CVPR,author = {Shin, Jae and Tajbakhsh, Nima and Hurst, R. Todd and Kendall, Christopher B. and Liang, Jianming},title = {Automating Carotid Intima-Media Thickness Video Interpretation With Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pat... | Cardiovascular disease (CVD) is the leading cause of mortality yet largely preventable, but the key to prevention is to identify at risk individuals before adverse events. For predicting individual CVD risk, carotid intima-media thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable, offering seve... |
Pathak_Context_Encoders_Feature_CVPR_2016_paper | Context Encoders: Feature Learning by Inpainting | [
"Deepak Pathak",
"Philipp Krahenbuhl",
"Jeff Donahue",
"Trevor Darrell",
"Alexei A. Efros"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Pathak_Context_Encoders_Feature_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Pathak_Context_Encoders_Feature_CVPR_2016_paper.pdf | null | 1604.07379 | title_snapshot | @InProceedings{Pathak_2016_CVPR,author = {Pathak, Deepak and Krahenbuhl, Philipp and Donahue, Jeff and Darrell, Trevor and Efros, Alexei A.},title = {Context Encoders: Feature Learning by Inpainting},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year =... | We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context Encoders -- a convolutional neural network trained to generate the contents of an arbitrary image region conditioned on its surroundings. In order to succeed at this t... |
Lei_Comparative_Deep_Learning_CVPR_2016_paper | Comparative Deep Learning of Hybrid Representations for Image Recommendations | [
"Chenyi Lei",
"Dong Liu",
"Weiping Li",
"Zheng-Jun Zha",
"Houqiang Li"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Lei_Comparative_Deep_Learning_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Lei_Comparative_Deep_Learning_CVPR_2016_paper.pdf | null | 1604.01252 | title_snapshot | @InProceedings{Lei_2016_CVPR,author = {Lei, Chenyi and Liu, Dong and Li, Weiping and Zha, Zheng-Jun and Li, Houqiang},title = {Comparative Deep Learning of Hybrid Representations for Image Recommendations},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},... | In many image-related tasks, learning expressive and discriminative representations of images is essential, and deep learning has been studied for automating the learning of such representations. Some user-centric tasks, such as image recommendations, call for effective representations of not only images but also prefe... |
Lebedev_Fast_ConvNets_Using_CVPR_2016_paper | Fast ConvNets Using Group-Wise Brain Damage | [
"Vadim Lebedev",
"Victor Lempitsky"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Lebedev_Fast_ConvNets_Using_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Lebedev_Fast_ConvNets_Using_CVPR_2016_paper.pdf | null | 1506.02515 | title_snapshot | @InProceedings{Lebedev_2016_CVPR,author = {Lebedev, Vadim and Lempitsky, Victor},title = {Fast ConvNets Using Group-Wise Brain Damage},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We revisit the idea of brain damage, i.e. the pruning of the coefficients of a neural network, and suggest how brain damage can be modified and used to speedup convolutional layers in ConvNets. The approach uses the fact that many efficient implementations reduce generalized convolutions to matrix multiplications. The ... |
Hayder_Learning_to_Co-Generate_CVPR_2016_paper | Learning to Co-Generate Object Proposals With a Deep Structured Network | [
"Zeeshan Hayder",
"Xuming He",
"Mathieu Salzmann"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Hayder_Learning_to_Co-Generate_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Hayder_Learning_to_Co-Generate_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Hayder_Learning_to_Co-Generate_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Hayder_2016_CVPR,author = {Hayder, Zeeshan and He, Xuming and Salzmann, Mathieu},title = {Learning to Co-Generate Object Proposals With a Deep Structured Network},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Generating object proposals has become a key component of modern object detection pipelines. However, most existing methods generate the object candidates independently of each other. In this paper, we present an approach to co-generating object proposals in multiple images, thus leveraging the collective power of mult... |
Moosavi-Dezfooli_DeepFool_A_Simple_CVPR_2016_paper | DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks | [
"Seyed-Mohsen Moosavi-Dezfooli",
"Alhussein Fawzi",
"Pascal Frossard"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Moosavi-Dezfooli_DeepFool_A_Simple_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Moosavi-Dezfooli_DeepFool_A_Simple_CVPR_2016_paper.pdf | null | 1511.04599 | title_snapshot | @InProceedings{Moosavi-Dezfooli_2016_CVPR,author = {Moosavi-Dezfooli, Seyed-Mohsen and Fawzi, Alhussein and Frossard, Pascal},title = {DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},ye... | State-of-the-art deep neural networks have achieved impressive results on many image classification tasks. However, these same architectures have been shown to be unstable to small, well sought, perturbations of the images. Despite the importance of this phenomenon, no effective methods have been proposed to accurately... |
Murdock_Blockout_Dynamic_Model_CVPR_2016_paper | Blockout: Dynamic Model Selection for Hierarchical Deep Networks | [
"Calvin Murdock",
"Zhen Li",
"Howard Zhou",
"Tom Duerig"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Murdock_Blockout_Dynamic_Model_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Murdock_Blockout_Dynamic_Model_CVPR_2016_paper.pdf | null | 1512.05246 | title_snapshot | @InProceedings{Murdock_2016_CVPR,author = {Murdock, Calvin and Li, Zhen and Zhou, Howard and Duerig, Tom},title = {Blockout: Dynamic Model Selection for Hierarchical Deep Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Most deep architectures for image classification--even those that are trained to classify a large number of diverse categories--learn shared image representations with a single model. Intuitively, however, categories that are more similar should share more information than those that are very different. While hierarchi... |
Iandola_FireCaffe_Near-Linear_Acceleration_CVPR_2016_paper | FireCaffe: Near-Linear Acceleration of Deep Neural Network Training on Compute Clusters | [
"Forrest N. Iandola",
"Matthew W. Moskewicz",
"Khalid Ashraf",
"Kurt Keutzer"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Iandola_FireCaffe_Near-Linear_Acceleration_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Iandola_FireCaffe_Near-Linear_Acceleration_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Iandola_FireCaffe_Near-Linear_Acceleration_2016_CVPR_supplemental.pdf | 1511.00175 | title_snapshot | @InProceedings{Iandola_2016_CVPR,author = {Iandola, Forrest N. and Moskewicz, Matthew W. and Ashraf, Khalid and Keutzer, Kurt},title = {FireCaffe: Near-Linear Acceleration of Deep Neural Network Training on Compute Clusters},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVP... | Long training times for high-accuracy deep neural networks (DNNs) impede research into new DNN architectures and slow the development of high-accuracy DNNs. In this paper we present FireCaffe, which successfully scales deep neural network training across a cluster of GPUs. We also present a number of best practices to ... |
Rastegar_MDL-CW_A_Multimodal_CVPR_2016_paper | MDL-CW: A Multimodal Deep Learning Framework With Cross Weights | [
"Sarah Rastegar",
"Mahdieh Soleymani",
"Hamid R. Rabiee",
"Seyed Mohsen Shojaee"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Rastegar_MDL-CW_A_Multimodal_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Rastegar_MDL-CW_A_Multimodal_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Rastegar_2016_CVPR,author = {Rastegar, Sarah and Soleymani, Mahdieh and Rabiee, Hamid R. and Shojaee, Seyed Mohsen},title = {MDL-CW: A Multimodal Deep Learning Framework With Cross Weights},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},y... | Deep learning has received much attention as of the most powerful approaches for multimodal representation learning in recent years. An ideal model for multimodal data can reason about missing modalities using the available ones, and usually provides more information when multiple modalities are being considered. All t... |
Jacobsen_Structured_Receptive_Fields_CVPR_2016_paper | Structured Receptive Fields in CNNs | [
"Jorn-Henrik Jacobsen",
"Jan van Gemert",
"Zhongyu Lou",
"Arnold W. M. Smeulders"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Jacobsen_Structured_Receptive_Fields_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Jacobsen_Structured_Receptive_Fields_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Jacobsen_Structured_Receptive_Fields_2016_CVPR_supplemental.pdf | 1605.02971 | title_snapshot | @InProceedings{Jacobsen_2016_CVPR,author = {Jacobsen, Jorn-Henrik and van Gemert, Jan and Lou, Zhongyu and Smeulders, Arnold W. M.},title = {Structured Receptive Fields in CNNs},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Learning powerful feature representations with CNNs is hard when training data are limited. Pre-training is one way to overcome this, but it requires large datasets sufficiently similar to the target domain. Another option is to design priors into the model, which can range from tuned hyperparameters to fully engineere... |
Singh_First_Person_Action_CVPR_2016_paper | First Person Action Recognition Using Deep Learned Descriptors | [
"Suriya Singh",
"Chetan Arora",
"C. V. Jawahar"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Singh_First_Person_Action_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Singh_First_Person_Action_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Singh_2016_CVPR,author = {Singh, Suriya and Arora, Chetan and Jawahar, C. V.},title = {First Person Action Recognition Using Deep Learned Descriptors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We focus on the problem of wearer's action recognition in first person a.k.a. egocentric videos. This problem is more challenging than third person activity recognition due to unavailability of wearer's pose and sharp movements in the videos caused by the natural head motion of the wearer. Carefully crafted features ba... |
Yonetani_Recognizing_Micro-Actions_and_CVPR_2016_paper | Recognizing Micro-Actions and Reactions From Paired Egocentric Videos | [
"Ryo Yonetani",
"Kris M. Kitani",
"Yoichi Sato"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Yonetani_Recognizing_Micro-Actions_and_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Yonetani_Recognizing_Micro-Actions_and_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Yonetani_2016_CVPR,author = {Yonetani, Ryo and Kitani, Kris M. and Sato, Yoichi},title = {Recognizing Micro-Actions and Reactions From Paired Egocentric Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We aim to understand the dynamics of social interactions between two people by recognizing their actions and reactions using a head-mounted camera. Our work will impact several first-person vision tasks that need the detailed understanding of social interactions, such as automatic video summarization of group events an... |
Wang_Mining_3D_Key-Pose-Motifs_CVPR_2016_paper | Mining 3D Key-Pose-Motifs for Action Recognition | [
"Chunyu Wang",
"Yizhou Wang",
"Alan L. Yuille"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Wang_Mining_3D_Key-Pose-Motifs_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Wang_Mining_3D_Key-Pose-Motifs_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Wang_2016_CVPR,author = {Wang, Chunyu and Wang, Yizhou and Yuille, Alan L.},title = {Mining 3D Key-Pose-Motifs for Action Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Recognizing an action from a sequence of 3D skeletal poses is a challenging task. First, different actors may perform the same action in various performing styles. Second, the estimated poses are sometimes inaccurate due to sensory noises. These challenges can cause large variations between instances of the same class.... |
Soomro_Predicting_the_Where_CVPR_2016_paper | Predicting the Where and What of Actors and Actions Through Online Action Localization | [
"Khurram Soomro",
"Haroon Idrees",
"Mubarak Shah"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Soomro_Predicting_the_Where_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Soomro_Predicting_the_Where_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Soomro_2016_CVPR,author = {Soomro, Khurram and Idrees, Haroon and Shah, Mubarak},title = {Predicting the Where and What of Actors and Actions Through Online Action Localization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}... | This paper proposes a novel approach to tackle the challenging problem of 'online action localization' which entails predicting actions and their locations as they happen in a video. Typically, action localization or recognition is performed in an offline manner where all the frames in the video are processed together ... |
Wang_Actions__Transformations_CVPR_2016_paper | Actions ~ Transformations | [
"Xiaolong Wang",
"Ali Farhadi",
"Abhinav Gupta"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Wang_Actions__Transformations_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Wang_Actions__Transformations_CVPR_2016_paper.pdf | null | 1512.00795 | title_snapshot | @InProceedings{Wang_2016_CVPR,author = {Wang, Xiaolong and Farhadi, Ali and Gupta, Abhinav},title = {Actions ~ Transformations},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | What defines an action like "kicking ball"? We argue that the true meaning of an action lies in the change or transformation an action brings to the environment. In this paper, we propose a novel representation for actions by modeling an action as a transformation which changes the state of the environment before the a... |
Yoo_Visual_Path_Prediction_CVPR_2016_paper | Visual Path Prediction in Complex Scenes With Crowded Moving Objects | [
"YoungJoon Yoo",
"Kimin Yun",
"Sangdoo Yun",
"JongHee Hong",
"Hawook Jeong",
"Jin Young Choi"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Yoo_Visual_Path_Prediction_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Yoo_Visual_Path_Prediction_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Yoo_Visual_Path_Prediction_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Yoo_2016_CVPR,author = {Yoo, YoungJoon and Yun, Kimin and Yun, Sangdoo and Hong, JongHee and Jeong, Hawook and Choi, Jin Young},title = {Visual Path Prediction in Complex Scenes With Crowded Moving Objects},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}... | This paper proposes a novel path prediction algorithm for progressing one step further than the existing works focusing on single target path prediction. In this paper, we consider moving dynamics of co-occurring objects for path prediction in a scene that includes crowded moving objects. To solve this problem, we firs... |
Yeung_End-To-End_Learning_of_CVPR_2016_paper | End-To-End Learning of Action Detection From Frame Glimpses in Videos | [
"Serena Yeung",
"Olga Russakovsky",
"Greg Mori",
"Li Fei-Fei"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Yeung_End-To-End_Learning_of_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Yeung_End-To-End_Learning_of_CVPR_2016_paper.pdf | null | 1511.06984 | title_snapshot | @InProceedings{Yeung_2016_CVPR,author = {Yeung, Serena and Russakovsky, Olga and Mori, Greg and Fei-Fei, Li},title = {End-To-End Learning of Action Detection From Frame Glimpses in Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | In this work we introduce a fully end-to-end approach for action detection in videos that learns to directly predict the temporal bounds of actions. Our intuition is that the process of detecting actions is naturally one of observation and refinement: observing moments in video, and refining hypotheses about when an ac... |
Alfaro_Action_Recognition_in_CVPR_2016_paper | Action Recognition in Video Using Sparse Coding and Relative Features | [
"Anali Alfaro",
"Domingo Mery",
"Alvaro Soto"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Alfaro_Action_Recognition_in_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Alfaro_Action_Recognition_in_CVPR_2016_paper.pdf | null | 1605.03222 | title_snapshot | @InProceedings{Alfaro_2016_CVPR,author = {Alfaro, Anali and Mery, Domingo and Soto, Alvaro},title = {Action Recognition in Video Using Sparse Coding and Relative Features},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | This work presents an approach to category-based action recognition in video using sparse coding techniques. The proposed approach includes two main contributions: i) A new method to handle intra-class variations by decomposing each video into a reduced set of representative atomic action acts or key-sequences, and ii)... |
Wang_Improving_Human_Action_CVPR_2016_paper | Improving Human Action Recognition by Non-Action Classification | [
"Yang Wang",
"Minh Hoai"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Wang_Improving_Human_Action_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Wang_Improving_Human_Action_CVPR_2016_paper.pdf | null | 1604.06397 | title_snapshot | @InProceedings{Wang_2016_CVPR,author = {Wang, Yang and Hoai, Minh},title = {Improving Human Action Recognition by Non-Action Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | In this paper we consider the task of recognizing human actions in realistic video where human actions are dominated by irrelevant factors. We first study the benefits of removing non-action video segments, which are the ones that do not portray any human action. We then learn a non-action classifier and use it to down... |
Wang_Actionness_Estimation_Using_CVPR_2016_paper | Actionness Estimation Using Hybrid Fully Convolutional Networks | [
"Limin Wang",
"Yu Qiao",
"Xiaoou Tang",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Wang_Actionness_Estimation_Using_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Wang_Actionness_Estimation_Using_CVPR_2016_paper.pdf | null | 1604.07279 | title_snapshot | @InProceedings{Wang_2016_CVPR,author = {Wang, Limin and Qiao, Yu and Tang, Xiaoou and Van Gool, Luc},title = {Actionness Estimation Using Hybrid Fully Convolutional Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Actionness was introduced to quantify the likelihood of containing a generic action instance at a specific location. Accurate and efficient estimation of actionness is important in video analysis and may benefit other relevant tasks such as action recognition and action detection. This paper presents a new deep archite... |
Zhang_Real-Time_Action_Recognition_CVPR_2016_paper | Real-Time Action Recognition With Enhanced Motion Vector CNNs | [
"Bowen Zhang",
"Limin Wang",
"Zhe Wang",
"Yu Qiao",
"Hanli Wang"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Zhang_Real-Time_Action_Recognition_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_Real-Time_Action_Recognition_CVPR_2016_paper.pdf | null | 1604.07669 | title_snapshot | @InProceedings{Zhang_2016_CVPR,author = {Zhang, Bowen and Wang, Limin and Wang, Zhe and Qiao, Yu and Wang, Hanli},title = {Real-Time Action Recognition With Enhanced Motion Vector CNNs},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | The deep two-stream architecture exhibited excellent performance on video based action recognition. The most computationally expensive step in this approach comes from the calculation of optical flow which prevents it to be real-time. This paper accelerates this architecture by replacing optical flow with motion vector... |
Lee_Laplacian_Patch-Based_Image_CVPR_2016_paper | Laplacian Patch-Based Image Synthesis | [
"Joo Ho Lee",
"Inchang Choi",
"Min H. Kim"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Lee_Laplacian_Patch-Based_Image_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Lee_Laplacian_Patch-Based_Image_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Lee_Laplacian_Patch-Based_Image_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Lee_2016_CVPR,author = {Lee, Joo Ho and Choi, Inchang and Kim, Min H.},title = {Laplacian Patch-Based Image Synthesis},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Patch-based image synthesis has been enriched with global optimization on the image pyramid. Successively, the gradient-based synthesis has improved structural coherence and details. However, the gradient operator is directional and inconsistent and requires computing multiple operators. It also introduces a significan... |
Li_Rain_Streak_Removal_CVPR_2016_paper | Rain Streak Removal Using Layer Priors | [
"Yu Li",
"Robby T. Tan",
"Xiaojie Guo",
"Jiangbo Lu",
"Michael S. Brown"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Li_Rain_Streak_Removal_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Li_Rain_Streak_Removal_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Li_2016_CVPR,author = {Li, Yu and Tan, Robby T. and Guo, Xiaojie and Lu, Jiangbo and Brown, Michael S.},title = {Rain Streak Removal Using Layer Priors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | This paper addresses the problem of rain streak removal from a single image. Rain streaks impair visibility of an image and introduce undesirable interference that can severely affect the performance of computer vision algorithms. Rain streak removal can be formulated as a layer decomposition problem, with a rain str... |
Shibata_Gradient-Domain_Image_Reconstruction_CVPR_2016_paper | Gradient-Domain Image Reconstruction Framework With Intensity-Range and Base-Structure Constraints | [
"Takashi Shibata",
"Masayuki Tanaka",
"Masatoshi Okutomi"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Shibata_Gradient-Domain_Image_Reconstruction_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Shibata_Gradient-Domain_Image_Reconstruction_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Shibata_Gradient-Domain_Image_Reconstruction_2016_CVPR_supplemental.zip | null | null | @InProceedings{Shibata_2016_CVPR,author = {Shibata, Takashi and Tanaka, Masayuki and Okutomi, Masatoshi},title = {Gradient-Domain Image Reconstruction Framework With Intensity-Range and Base-Structure Constraints},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month =... | This paper presents a novel unified gradient-domain image reconstruction framework with intensity-range constraint and base-structure constraint. The existing method for manipulating base structures and detailed textures are classifiable into two major approaches: i) gradient-domain and ii) layer-decomposition. To gene... |
Wang_Removing_Clouds_and_CVPR_2016_paper | Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion | [
"Jialei Wang",
"Peder A. Olsen",
"Andrew R. Conn",
"Aurelie C. Lozano"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Wang_Removing_Clouds_and_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Wang_Removing_Clouds_and_CVPR_2016_paper.pdf | null | 1604.03915 | title_snapshot | @InProceedings{Wang_2016_CVPR,author = {Wang, Jialei and Olsen, Peder A. and Conn, Andrew R. and Lozano, Aurelie C.},title = {Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion},booktitle = {Proceedings of the IEEE Conference on Computer Vi... | We consider the problem of removing and replacing clouds in satellite image sequences, which has a wide range of applications in remote sensing. Our approach first detects and removes the cloud-contaminated part of the image sequences, then recovers the missing scenes from the clean parts by the proposed "TECROMAC" (TE... |
Wang_D3_Deep_Dual-Domain_CVPR_2016_paper | D3: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images | [
"Zhangyang Wang",
"Ding Liu",
"Shiyu Chang",
"Qing Ling",
"Yingzhen Yang",
"Thomas S. Huang"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Wang_D3_Deep_Dual-Domain_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Wang_D3_Deep_Dual-Domain_CVPR_2016_paper.pdf | null | 1601.04149 | title_judge | @InProceedings{Wang_2016_CVPR,author = {Wang, Zhangyang and Liu, Ding and Chang, Shiyu and Ling, Qing and Yang, Yingzhen and Huang, Thomas S.},title = {D3: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)... | In this paper, we design a Deep Dual-Domain (D3) based fast restoration model to remove artifacts of JPEG compressed images. It leverages the large learning capacity of deep networks, as well as the problem-specific expertise that was hardly incorporated in the past design of deep architectures. For the latter, we take... |
Rengarajan_From_Bows_to_CVPR_2016_paper | From Bows to Arrows: Rolling Shutter Rectification of Urban Scenes | [
"Vijay Rengarajan",
"Ambasamudram N. Rajagopalan",
"Rangarajan Aravind"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Rengarajan_From_Bows_to_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Rengarajan_From_Bows_to_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Rengarajan_From_Bows_to_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Rengarajan_2016_CVPR,author = {Rengarajan, Vijay and Rajagopalan, Ambasamudram N. and Aravind, Rangarajan},title = {From Bows to Arrows: Rolling Shutter Rectification of Urban Scenes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = ... | The rule of perspectivity that 'straight-lines-must-remain-straight' is easily inflected in CMOS cameras by distortions introduced by motion. Lines can be rendered as curves due to the row-wise exposure mechanism known as rolling shutter (RS). We solve the problem of correcting distortions arising from handheld cameras... |
Fu_A_Weighted_Variational_CVPR_2016_paper | A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation | [
"Xueyang Fu",
"Delu Zeng",
"Yue Huang",
"Xiao-Ping Zhang",
"Xinghao Ding"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Fu_A_Weighted_Variational_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Fu_A_Weighted_Variational_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Fu_2016_CVPR,author = {Fu, Xueyang and Zeng, Delu and Huang, Yue and Zhang, Xiao-Ping and Ding, Xinghao},title = {A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month... | We propose a weighted variational model to estimate both the reflectance and the illumination from an observed image. We show that, though it is widely adopted for ease of modeling, the log-transformed image for this task is not ideal. Based on the previous investigation of the logarithmic transformation, a new weighte... |
Lin_Visualizing_and_Understanding_CVPR_2016_paper | Visualizing and Understanding Deep Texture Representations | [
"Tsung-Yu Lin",
"Subhransu Maji"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Lin_Visualizing_and_Understanding_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Lin_Visualizing_and_Understanding_CVPR_2016_paper.pdf | null | 1511.05197 | title_snapshot | @InProceedings{Lin_2016_CVPR,author = {Lin, Tsung-Yu and Maji, Subhransu},title = {Visualizing and Understanding Deep Texture Representations},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | A number of recent approaches have used deep convolutional neural networks (CNNs) to build texture representations. Nevertheless, it is still unclear how these mod- els represent texture and invariances to categorical variations. This work conducts a systematic evaluation of recent CNN-based texture descriptors for rec... |
Pan_Robust_Kernel_Estimation_CVPR_2016_paper | Robust Kernel Estimation With Outliers Handling for Image Deblurring | [
"Jinshan Pan",
"Zhouchen Lin",
"Zhixun Su",
"Ming-Hsuan Yang"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Pan_Robust_Kernel_Estimation_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Pan_Robust_Kernel_Estimation_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Pan_Robust_Kernel_Estimation_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Pan_2016_CVPR,author = {Pan, Jinshan and Lin, Zhouchen and Su, Zhixun and Yang, Ming-Hsuan},title = {Robust Kernel Estimation With Outliers Handling for Image Deblurring},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Estimating blur kernels from real world images is a challenging problem as the linear image formation assumption does not hold when significant outliers, such as saturated pixels and non-Gaussian noise, are present. While some existing non-blind deblurring algorithms can deal with outliers to a certain extent, few blin... |
Zhang_Online_Collaborative_Learning_CVPR_2016_paper | Online Collaborative Learning for Open-Vocabulary Visual Classifiers | [
"Hanwang Zhang",
"Xindi Shang",
"Wenzhuo Yang",
"Huan Xu",
"Huanbo Luan",
"Tat-Seng Chua"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Zhang_Online_Collaborative_Learning_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_Online_Collaborative_Learning_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Zhang_2016_CVPR,author = {Zhang, Hanwang and Shang, Xindi and Yang, Wenzhuo and Xu, Huan and Luan, Huanbo and Chua, Tat-Seng},title = {Online Collaborative Learning for Open-Vocabulary Visual Classifiers},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},m... | We focus on learning open-vocabulary visual classifiers, which scale up to a large portion of natural language vocabulary (e.g., over tens of thousands of classes). In particular, the training data are large-scale weakly labeled Web images since it is difficult to acquire sufficient well-labeled data at this category s... |
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