paper_id uint32 | 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 | embedding list |
|---|---|---|---|---|---|---|---|---|---|---|
0 | Deep Compositional Captioning: Describing Novel Object Categories Without Paired Training Data | [
"Lisa Anne Hendricks",
"Subhashini Venugopalan",
"Marcus Rohrbach",
"Raymond Mooney",
"Kate Saenko",
"Trevor Darrell"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Hendricks_Deep_Compositional_Captioning_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Hendricks_Deep_Compositional_Captioning_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Hendricks_Deep_Compositional_Captioning_2016_CVPR_supplemental.pdf | 1511.05284 | title_snapshot | @InProceedings{Hendricks_2016_CVPR,author = {Hendricks, Lisa Anne and Venugopalan, Subhashini and Rohrbach, Marcus and Mooney, Raymond and Saenko, Kate and Darrell, Trevor},title = {Deep Compositional Captioning: Describing Novel Object Categories Without Paired Training Data},booktitle = {Proceedings of the IEEE Confe... | While recent deep neural network models have achieved promising results on the image captioning task, they rely largely on the availability of corpora with paired image and sentence captions to describe objects in context. In this work, we propose the Deep Compositional Captioner (DCC) to address the task of generating... | [
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1 | Generation and Comprehension of Unambiguous Object Descriptions | [
"Junhua Mao",
"Jonathan Huang",
"Alexander Toshev",
"Oana Camburu",
"Alan L. Yuille",
"Kevin Murphy"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Mao_Generation_and_Comprehension_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Mao_Generation_and_Comprehension_CVPR_2016_paper.pdf | null | 1511.02283 | title_snapshot | @InProceedings{Mao_2016_CVPR,author = {Mao, Junhua and Huang, Jonathan and Toshev, Alexander and Camburu, Oana and Yuille, Alan L. and Murphy, Kevin},title = {Generation and Comprehension of Unambiguous Object Descriptions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR... | We propose a method that can generate an unambiguous description (known as a referring expression) of a specific object or region in an image, and which can also comprehend or interpret such an expression to infer which object is being described. We show that our method outperforms previous methods that generate descri... | [
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2 | Stacked Attention Networks for Image Question Answering | [
"Zichao Yang",
"Xiaodong He",
"Jianfeng Gao",
"Li Deng",
"Alex Smola"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Yang_Stacked_Attention_Networks_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Yang_Stacked_Attention_Networks_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Yang_Stacked_Attention_Networks_2016_CVPR_supplemental.pdf | 1511.02274 | title_snapshot | @InProceedings{Yang_2016_CVPR,author = {Yang, Zichao and He, Xiaodong and Gao, Jianfeng and Deng, Li and Smola, Alex},title = {Stacked Attention Networks for Image Question Answering},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | This paper presents stacked attention networks (SANs)that learn to answer natural language questions from images. SANs use semantic representation of a question as query to search for the regions in an image that are related to the answer. We argue that image question answering (QA) often requires multiple steps of rea... | [
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3 | Image Question Answering Using Convolutional Neural Network With Dynamic Parameter Prediction | [
"Hyeonwoo Noh",
"Paul Hongsuck Seo",
"Bohyung Han"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Noh_Image_Question_Answering_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Noh_Image_Question_Answering_CVPR_2016_paper.pdf | null | 1511.05756 | title_snapshot | @InProceedings{Noh_2016_CVPR,author = {Noh, Hyeonwoo and Seo, Paul Hongsuck and Han, Bohyung},title = {Image Question Answering Using Convolutional Neural Network With Dynamic Parameter Prediction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {... | We tackle image question answering (ImageQA) problem by learning a convolutional neural network (CNN) with a dynamic parameter layer whose weights are determined adaptively based on questions. For the adaptive parameter prediction, we employ a separate parameter prediction network, which consists of gated recurrent uni... | [
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4 | Neural Module Networks | [
"Jacob Andreas",
"Marcus Rohrbach",
"Trevor Darrell",
"Dan Klein"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Andreas_Neural_Module_Networks_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Andreas_Neural_Module_Networks_CVPR_2016_paper.pdf | null | 1511.02799 | title_snapshot | @InProceedings{Andreas_2016_CVPR,author = {Andreas, Jacob and Rohrbach, Marcus and Darrell, Trevor and Klein, Dan},title = {Neural Module Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Visual question answering is fundamentally compositional in nature---a question like "where is the dog?" shares substructure with questions like "what color is the dog?" and "where is the cat?" This paper seeks to simultaneously exploit the representational capacity of deep networks and the compositional linguistic str... | [
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5 | Learning Deep Representations of Fine-Grained Visual Descriptions | [
"Scott Reed",
"Zeynep Akata",
"Honglak Lee",
"Bernt Schiele"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Reed_Learning_Deep_Representations_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Reed_Learning_Deep_Representations_CVPR_2016_paper.pdf | null | 1605.05395 | title_snapshot | @InProceedings{Reed_2016_CVPR,author = {Reed, Scott and Akata, Zeynep and Lee, Honglak and Schiele, Bernt},title = {Learning Deep Representations of Fine-Grained Visual Descriptions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | State-of-the-art methods for zero-shot visual recognition formulate learning as a joint embedding problem of images and side information. In these formulations the current best complement to visual features are attributes: manually-encoded vectors describing shared characteristics among categories. Despite good perform... | [
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6 | Multi-Cue Zero-Shot Learning With Strong Supervision | [
"Zeynep Akata",
"Mateusz Malinowski",
"Mario Fritz",
"Bernt Schiele"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Akata_Multi-Cue_Zero-Shot_Learning_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Akata_Multi-Cue_Zero-Shot_Learning_CVPR_2016_paper.pdf | null | 1603.08754 | title_snapshot | @InProceedings{Akata_2016_CVPR,author = {Akata, Zeynep and Malinowski, Mateusz and Fritz, Mario and Schiele, Bernt},title = {Multi-Cue Zero-Shot Learning With Strong Supervision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Scaling up visual category recognition to large numbers of classes remains challenging. A promising research direction is zero-shot learning, which does not require any training data to recognize new classes, but rather relies on some form of auxiliary information describing the new classes. Ultimately, this may allow ... | [
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7 | Latent Embeddings for Zero-Shot Classification | [
"Yongqin Xian",
"Zeynep Akata",
"Gaurav Sharma",
"Quynh Nguyen",
"Matthias Hein",
"Bernt Schiele"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Xian_Latent_Embeddings_for_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Xian_Latent_Embeddings_for_CVPR_2016_paper.pdf | null | 1603.08895 | title_snapshot | @InProceedings{Xian_2016_CVPR,author = {Xian, Yongqin and Akata, Zeynep and Sharma, Gaurav and Nguyen, Quynh and Hein, Matthias and Schiele, Bernt},title = {Latent Embeddings for Zero-Shot Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},y... | We present a novel latent embedding model for learning a compatibility function between image and class embeddings, in the context of zero-shot classification. The proposed method augments the state-of-the-art bilinear compatibility model by incorporating latent variables. Instead of learning a single bilinear map, it ... | [
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8 | One-Shot Learning of Scene Locations via Feature Trajectory Transfer | [
"Roland Kwitt",
"Sebastian Hegenbart",
"Marc Niethammer"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Kwitt_One-Shot_Learning_of_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Kwitt_One-Shot_Learning_of_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Kwitt_2016_CVPR,author = {Kwitt, Roland and Hegenbart, Sebastian and Niethammer, Marc},title = {One-Shot Learning of Scene Locations via Feature Trajectory Transfer},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | The appearance of (outdoor) scenes changes considerably with the strength of certain transient attributes, such as "rainy", "dark" or "sunny". Obviously, this also affects the representation of an image in feature space, e.g., as activations at a certain CNN layer, and consequently impacts scene recognition performance... | [
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9 | Learning Attributes Equals Multi-Source Domain Generalization | [
"Chuang Gan",
"Tianbao Yang",
"Boqing Gong"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Gan_Learning_Attributes_Equals_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Gan_Learning_Attributes_Equals_CVPR_2016_paper.pdf | null | 1605.00743 | title_snapshot | @InProceedings{Gan_2016_CVPR,author = {Gan, Chuang and Yang, Tianbao and Gong, Boqing},title = {Learning Attributes Equals Multi-Source Domain Generalization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Attributes possess appealing properties and benefit many computer vision problems, such as object recognition, learning with humans in the loop, and image retrieval. Whereas the existing work mainly pursues utilizing attributes for various computer vision problems, we contend that the most basic problem---how to accur... | [
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10 | Anticipating Visual Representations From Unlabeled Video | [
"Carl Vondrick",
"Hamed Pirsiavash",
"Antonio Torralba"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Vondrick_Anticipating_Visual_Representations_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Vondrick_Anticipating_Visual_Representations_CVPR_2016_paper.pdf | null | 1504.08023 | title_snapshot | @InProceedings{Vondrick_2016_CVPR,author = {Vondrick, Carl and Pirsiavash, Hamed and Torralba, Antonio},title = {Anticipating Visual Representations From Unlabeled Video},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Anticipating actions and objects before they start or appear is a difficult problem in computer vision with several real-world applications. This task is challenging partly because it requires leveraging extensive knowledge of the world that is difficult to write down. We believe that a promising resource for efficient... | [
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11 | Learning to Assign Orientations to Feature Points | [
"Kwang Moo Yi",
"Yannick Verdie",
"Pascal Fua",
"Vincent Lepetit"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Yi_Learning_to_Assign_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Yi_Learning_to_Assign_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Yi_Learning_to_Assign_2016_CVPR_supplemental.pdf | 1511.04273 | title_snapshot | @InProceedings{Yi_2016_CVPR,author = {Yi, Kwang Moo and Verdie, Yannick and Fua, Pascal and Lepetit, Vincent},title = {Learning to Assign Orientations to Feature Points},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We show how to train a Convolutional Neural Network to assign a canonical orientation to feature points given an image patch centered on the feature point. Our method improves feature point matching upon the state-of-the art and can be used in conjunction with any existing rotation sensitive descriptors. To avoid the... | [
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12 | Learning Dense Correspondence via 3D-Guided Cycle Consistency | [
"Tinghui Zhou",
"Philipp Krahenbuhl",
"Mathieu Aubry",
"Qixing Huang",
"Alexei A. Efros"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Zhou_Learning_Dense_Correspondence_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Zhou_Learning_Dense_Correspondence_CVPR_2016_paper.pdf | null | 1604.05383 | title_snapshot | @InProceedings{Zhou_2016_CVPR,author = {Zhou, Tinghui and Krahenbuhl, Philipp and Aubry, Mathieu and Huang, Qixing and Efros, Alexei A.},title = {Learning Dense Correspondence via 3D-Guided Cycle Consistency},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {Jun... | Discriminative deep learning approaches have shown impressive results for problems where human-labeled ground truth is plentiful, but what about tasks where labels are difficult or impossible to obtain? This paper tackles one such problem: establishing dense visual correspondence across different object instances. For ... | [
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13 | The Global Patch Collider | [
"Shenlong Wang",
"Sean Ryan Fanello",
"Christoph Rhemann",
"Shahram Izadi",
"Pushmeet Kohli"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Wang_The_Global_Patch_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Wang_The_Global_Patch_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Wang_2016_CVPR,author = {Wang, Shenlong and Fanello, Sean Ryan and Rhemann, Christoph and Izadi, Shahram and Kohli, Pushmeet},title = {The Global Patch Collider},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | This paper proposes a novel extremely efficient, fully-parallelizable, task-specific algorithm for the computation of global point-wise correspondences in images and videos. Our algorithm, the Global Patch Collider, is based on detecting unique collisions between image points using a collection of learned tree structur... | [
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14 | Joint Probabilistic Matching Using m-Best Solutions | [
"Seyed Hamid Rezatofighi",
"Anton Milan",
"Zhen Zhang",
"Qinfeng Shi",
"Anthony Dick",
"Ian Reid"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Rezatofighi_Joint_Probabilistic_Matching_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Rezatofighi_Joint_Probabilistic_Matching_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Rezatofighi_Joint_Probabilistic_Matching_2016_CVPR_supplemental.zip | null | null | @InProceedings{Rezatofighi_2016_CVPR,author = {Rezatofighi, Seyed Hamid and Milan, Anton and Zhang, Zhen and Shi, Qinfeng and Dick, Anthony and Reid, Ian},title = {Joint Probabilistic Matching Using m-Best Solutions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},mont... | Matching between two sets of objects is typically approached by finding the object pairs that collectively maximize the joint matching score. In this paper, we argue that this single solution does not necessarily lead to the optimal matching accuracy and that general one-to-one assignment problems can be improved by co... | [
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15 | Face Alignment Across Large Poses: A 3D Solution | [
"Xiangyu Zhu",
"Zhen Lei",
"Xiaoming Liu",
"Hailin Shi",
"Stan Z. Li"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Zhu_Face_Alignment_Across_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Zhu_Face_Alignment_Across_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Zhu_Face_Alignment_Across_2016_CVPR_supplemental.pdf | 1511.07212 | title_snapshot | @InProceedings{Zhu_2016_CVPR,author = {Zhu, Xiangyu and Lei, Zhen and Liu, Xiaoming and Shi, Hailin and Li, Stan Z.},title = {Face Alignment Across Large Poses: A 3D Solution},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in CV community. However, most algorithms are designed for faces in small to medium poses (below 45 degree), lacking the ability to align faces in large-pose up to 90 degree. The challeng... | [
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16 | Interactive Segmentation on RGBD Images via Cue Selection | [
"Jie Feng",
"Brian Price",
"Scott Cohen",
"Shih-Fu Chang"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Feng_Interactive_Segmentation_on_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Feng_Interactive_Segmentation_on_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Feng_2016_CVPR,author = {Feng, Jie and Price, Brian and Cohen, Scott and Chang, Shih-Fu},title = {Interactive Segmentation on RGBD Images via Cue Selection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Interactive image segmentation is an important problem in computer vision with many applications including image editing, object recognition and image retrieval. Most existing interactive segmentation methods only operate on color images. Until recently, very few works have been proposed to leverage depth information f... | [
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17 | Layered Scene Decomposition via the Occlusion-CRF | [
"Chen Liu",
"Pushmeet Kohli",
"Yasutaka Furukawa"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Liu_Layered_Scene_Decomposition_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Liu_Layered_Scene_Decomposition_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Liu_Layered_Scene_Decomposition_2016_CVPR_supplemental.zip | null | null | @InProceedings{Liu_2016_CVPR,author = {Liu, Chen and Kohli, Pushmeet and Furukawa, Yasutaka},title = {Layered Scene Decomposition via the Occlusion-CRF},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | This paper addresses the challenging problem of perceiving the hidden or occluded geometry of the scene depicted in any given RGBD image. Unlike other image labeling problems such as image segmentation where each pixel needs to be assigned a single label, layered decomposition requires us to assign multiple labels to p... | [
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18 | Affinity CNN: Learning Pixel-Centric Pairwise Relations for Figure/Ground Embedding | [
"Michael Maire",
"Takuya Narihira",
"Stella X. Yu"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Maire_Affinity_CNN_Learning_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Maire_Affinity_CNN_Learning_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Maire_Affinity_CNN_Learning_2016_CVPR_supplemental.pdf | 1512.02767 | title_snapshot | @InProceedings{Maire_2016_CVPR,author = {Maire, Michael and Narihira, Takuya and Yu, Stella X.},title = {Affinity CNN: Learning Pixel-Centric Pairwise Relations for Figure/Ground Embedding},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Spectral embedding provides a framework for solving perceptual organization problems, including image segmentation and figure/ground organization. From an affinity matrix describing pairwise relationships between pixels, it clusters pixels into regions, and, using a complex-valued extension, orders pixels according to... | [
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19 | Weakly Supervised Object Boundaries | [
"Anna Khoreva",
"Rodrigo Benenson",
"Mohamed Omran",
"Matthias Hein",
"Bernt Schiele"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Khoreva_Weakly_Supervised_Object_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Khoreva_Weakly_Supervised_Object_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Khoreva_Weakly_Supervised_Object_2016_CVPR_supplemental.pdf | 1511.07803 | title_snapshot | @InProceedings{Khoreva_2016_CVPR,author = {Khoreva, Anna and Benenson, Rodrigo and Omran, Mohamed and Hein, Matthias and Schiele, Bernt},title = {Weakly Supervised Object Boundaries},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | State-of-the-art learning based boundary detection methods require extensive training data. Since labelling object boundaries is one of the most expensive types of annotations, there is a need to relax the requirement to carefully annotate images to make both the training more affordable and to extend the amount of tra... | [
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20 | Object Contour Detection With a Fully Convolutional Encoder-Decoder Network | [
"Jimei Yang",
"Brian Price",
"Scott Cohen",
"Honglak Lee",
"Ming-Hsuan Yang"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Yang_Object_Contour_Detection_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Yang_Object_Contour_Detection_CVPR_2016_paper.pdf | null | 1603.04530 | title_snapshot | @InProceedings{Yang_2016_CVPR,author = {Yang, Jimei and Price, Brian and Cohen, Scott and Lee, Honglak and Yang, Ming-Hsuan},title = {Object Contour Detection With a Fully Convolutional Encoder-Decoder Network},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {J... | We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. Our network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate poly... | [
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21 | What Value Do Explicit High Level Concepts Have in Vision to Language Problems? | [
"Qi Wu",
"Chunhua Shen",
"Lingqiao Liu",
"Anthony Dick",
"Anton van den Hengel"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Wu_What_Value_Do_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Wu_What_Value_Do_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Wu_What_Value_Do_2016_CVPR_supplemental.pdf | 1506.01144 | title_snapshot | @InProceedings{Wu_2016_CVPR,author = {Wu, Qi and Shen, Chunhua and Liu, Lingqiao and Dick, Anthony and van den Hengel, Anton},title = {What Value Do Explicit High Level Concepts Have in Vision to Language Problems?},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month... | Much recent progress in Vision-to-Language (V2L) problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This approach does not explicitly represent high-level semantic concepts, but rather seeks to progress directly from image features to text. In ... | [
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22 | Fast Detection of Curved Edges at Low SNR | [
"Nati Ofir",
"Meirav Galun",
"Boaz Nadler",
"Ronen Basri"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Ofir_Fast_Detection_of_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Ofir_Fast_Detection_of_CVPR_2016_paper.pdf | null | 1505.06600 | title_snapshot | @InProceedings{Ofir_2016_CVPR,author = {Ofir, Nati and Galun, Meirav and Nadler, Boaz and Basri, Ronen},title = {Fast Detection of Curved Edges at Low SNR},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Detecting edges is a fundamental problem in computer vision with many applications, some involving very noisy images. While most edge detection methods are fast, they perform well only on relatively clean images. Unfortunately, sophisticated methods that are robust to high levels of noise are quite slow. In this paper ... | [
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23 | Object Skeleton Extraction in Natural Images by Fusing Scale-Associated Deep Side Outputs | [
"Wei Shen",
"Kai Zhao",
"Yuan Jiang",
"Yan Wang",
"Zhijiang Zhang",
"Xiang Bai"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Shen_Object_Skeleton_Extraction_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Shen_Object_Skeleton_Extraction_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Shen_Object_Skeleton_Extraction_2016_CVPR_supplemental.pdf | 1603.09446 | title_snapshot | @InProceedings{Shen_2016_CVPR,author = {Shen, Wei and Zhao, Kai and Jiang, Yuan and Wang, Yan and Zhang, Zhijiang and Bai, Xiang},title = {Object Skeleton Extraction in Natural Images by Fusing Scale-Associated Deep Side Outputs},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition... | Object skeleton is a useful cue for object detection, complementary to the object contour, as it provides a structural representation to describe the relationship among object parts. While object skeleton extraction in natural images is a very challenging problem, as it requires the extractor to be able to capture both... | [
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24 | Learning Relaxed Deep Supervision for Better Edge Detection | [
"Yu Liu",
"Michael S. Lew"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Liu_Learning_Relaxed_Deep_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Liu_Learning_Relaxed_Deep_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Liu_2016_CVPR,author = {Liu, Yu and Lew, Michael S.},title = {Learning Relaxed Deep Supervision for Better Edge Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We propose using relaxed deep supervision (RDS) within convolutional neural networks for edge detection. The conventional deep supervision utilizes the general ground-truth to guide intermediate predictions. Instead, we build hierarchical supervisory signals with additional relaxed labels to consider the diversities in... | [
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25 | Occlusion Boundary Detection via Deep Exploration of Context | [
"Huan Fu",
"Chaohui Wang",
"Dacheng Tao",
"Michael J. Black"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Fu_Occlusion_Boundary_Detection_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Fu_Occlusion_Boundary_Detection_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Fu_2016_CVPR,author = {Fu, Huan and Wang, Chaohui and Tao, Dacheng and Black, Michael J.},title = {Occlusion Boundary Detection via Deep Exploration of Context},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Occlusion boundaries contain rich perceptual information about the underlying scene structure. They also provide important cues in many visual perception tasks such as scene understanding, object recognition, and segmentation. In this paper, we improve occlusion boundary detection via enhanced exploration of contextual... | [
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26 | SemiContour: A Semi-Supervised Learning Approach for Contour Detection | [
"Zizhao Zhang",
"Fuyong Xing",
"Xiaoshuang Shi",
"Lin Yang"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Zhang_SemiContour_A_Semi-Supervised_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_SemiContour_A_Semi-Supervised_CVPR_2016_paper.pdf | null | 1605.04996 | title_snapshot | @InProceedings{Zhang_2016_CVPR,author = {Zhang, Zizhao and Xing, Fuyong and Shi, Xiaoshuang and Yang, Lin},title = {SemiContour: A Semi-Supervised Learning Approach for Contour Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Supervised contour detection methods usually require many labeled training images to obtain satisfactory performance. However, a large set of annotated data might be unavailable or extremely labor intensive. In this paper, we investigate the usage of semi-supervised learning (SSL) to obtain competitive detection accura... | [
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27 | Learning to Localize Little Landmarks | [
"Saurabh Singh",
"Derek Hoiem",
"David Forsyth"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Singh_Learning_to_Localize_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Singh_Learning_to_Localize_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Singh_2016_CVPR,author = {Singh, Saurabh and Hoiem, Derek and Forsyth, David},title = {Learning to Localize Little Landmarks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We interact everyday with tiny objects such as the door handle of a car or the light switch in a room. These little landmarks are barely visible and hard to localize in images. We describe a method to find such landmarks by finding a sequence of latent landmarks, each with a prediction model. Each latent landmark predi... | [
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28 | InterActive: Inter-Layer Activeness Propagation | [
"Lingxi Xie",
"Liang Zheng",
"Jingdong Wang",
"Alan L. Yuille",
"Qi Tian"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Xie_InterActive_Inter-Layer_Activeness_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Xie_InterActive_Inter-Layer_Activeness_CVPR_2016_paper.pdf | null | 1605.00052 | title_snapshot | @InProceedings{Xie_2016_CVPR,author = {Xie, Lingxi and Zheng, Liang and Wang, Jingdong and Yuille, Alan L. and Tian, Qi},title = {InterActive: Inter-Layer Activeness Propagation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | An increasing number of computer vision tasks can be tackled with deep features, which are the intermediate outputs of a pre-trained Convolutional Neural Network. Despite the astonishing performance, deep features extracted from low-level neurons are still below satisfaction, arguably because they cannot access the spa... | [
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29 | Exploit Bounding Box Annotations for Multi-Label Object Recognition | [
"Hao Yang",
"Joey Tianyi Zhou",
"Yu Zhang",
"Bin-Bin Gao",
"Jianxin Wu",
"Jianfei Cai"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Yang_Exploit_Bounding_Box_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Yang_Exploit_Bounding_Box_CVPR_2016_paper.pdf | null | 1504.05843 | title_snapshot | @InProceedings{Yang_2016_CVPR,author = {Yang, Hao and Zhou, Joey Tianyi and Zhang, Yu and Gao, Bin-Bin and Wu, Jianxin and Cai, Jianfei},title = {Exploit Bounding Box Annotations for Multi-Label Object Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month ... | Convolutional neural networks (CNNs) have shown great performance as general feature representations for object recognition applications. However, for multi-label images that contain multiple objects from different categories, scales and locations, global CNN features are not optimal. In this paper, we incorporate loca... | [
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30 | TI-Pooling: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks | [
"Dmitry Laptev",
"Nikolay Savinov",
"Joachim M. Buhmann",
"Marc Pollefeys"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Laptev_TI-Pooling_Transformation-Invariant_Pooling_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Laptev_TI-Pooling_Transformation-Invariant_Pooling_CVPR_2016_paper.pdf | null | 1604.06318 | title_snapshot | @InProceedings{Laptev_2016_CVPR,author = {Laptev, Dmitry and Savinov, Nikolay and Buhmann, Joachim M. and Pollefeys, Marc},title = {TI-Pooling: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recogniti... | In this paper we present a deep neural network topology that incorporates a simple to implement transformation-invariant pooling operator (TI-pooling). This operator is able to efficiently handle prior knowledge on nuisance variations in the data, such as rotation or scale changes. Most current methods usually make use... | [
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31 | Fashion Style in 128 Floats: Joint Ranking and Classification Using Weak Data for Feature Extraction | [
"Edgar Simo-Serra",
"Hiroshi Ishikawa"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Simo-Serra_Fashion_Style_in_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Simo-Serra_Fashion_Style_in_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Simo-Serra_Fashion_Style_in_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Simo-Serra_2016_CVPR,author = {Simo-Serra, Edgar and Ishikawa, Hiroshi},title = {Fashion Style in 128 Floats: Joint Ranking and Classification Using Weak Data for Feature Extraction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {... | We propose a novel approach for learning features from weakly-supervised data by joint ranking and classification. In order to exploit data with weak labels, we jointly train a feature extraction network with a ranking loss and a classification network with a cross-entropy loss. We obtain high-quality compact discrimin... | [
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32 | Equiangular Kernel Dictionary Learning With Applications to Dynamic Texture Analysis | [
"Yuhui Quan",
"Chenglong Bao",
"Hui Ji"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Quan_Equiangular_Kernel_Dictionary_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Quan_Equiangular_Kernel_Dictionary_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Quan_Equiangular_Kernel_Dictionary_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Quan_2016_CVPR,author = {Quan, Yuhui and Bao, Chenglong and Ji, Hui},title = {Equiangular Kernel Dictionary Learning With Applications to Dynamic Texture Analysis},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Most existing dictionary learning algorithms consider a linear sparse model, which often cannot effectively characterize the nonlinear properties present in many types of visual data, e.g. dynamic texture (DT). Such nonlinear properties can be exploited by the so-called kernel sparse coding. This paper proposed an eq... | [
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33 | Compact Bilinear Pooling | [
"Yang Gao",
"Oscar Beijbom",
"Ning Zhang",
"Trevor Darrell"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Gao_Compact_Bilinear_Pooling_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Gao_Compact_Bilinear_Pooling_CVPR_2016_paper.pdf | null | 1511.06062 | title_snapshot | @InProceedings{Gao_2016_CVPR,author = {Gao, Yang and Beijbom, Oscar and Zhang, Ning and Darrell, Trevor},title = {Compact Bilinear Pooling},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Bilinear models has been shown to achieve impressive performance on a wide range of visual tasks, such as semantic segmentation, fine grained recognition and face recognition. However, bilinear features are high dimensional, typically on the order of hundreds of thousands to a few million, which makes them impractical ... | [
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34 | Accumulated Stability Voting: A Robust Descriptor From Descriptors of Multiple Scales | [
"Tsun-Yi Yang",
"Yen-Yu Lin",
"Yung-Yu Chuang"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Yang_Accumulated_Stability_Voting_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Yang_Accumulated_Stability_Voting_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Yang_2016_CVPR,author = {Yang, Tsun-Yi and Lin, Yen-Yu and Chuang, Yung-Yu},title = {Accumulated Stability Voting: A Robust Descriptor From Descriptors of Multiple Scales},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | This paper proposes a novel local descriptor through accumulated stability voting (ASV). The stability of feature dimensions is measured by their differences across scales. To be more robust to noise, the stability is further quantized by thresholding. The principle of maximum entropy is utilized for determining the be... | [
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35 | CoMaL: Good Features to Match on Object Boundaries | [
"Swarna K. Ravindran",
"Anurag Mittal"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Ravindran_CoMaL_Good_Features_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Ravindran_CoMaL_Good_Features_CVPR_2016_paper.pdf | null | 1412.1957 | title_judge | @InProceedings{Ravindran_2016_CVPR,author = {Ravindran, Swarna K. and Mittal, Anurag},title = {CoMaL: Good Features to Match on Object Boundaries},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Traditional Feature Detectors and Trackers use information aggregation in 2D patches to detect and match discriminative patches. However, this information does not remain the same at object boundaries when there is object motion against a significantly varying background. In this paper, we propose a new approach for ... | [
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36 | Progressive Feature Matching With Alternate Descriptor Selection and Correspondence Enrichment | [
"Yuan-Ting Hu",
"Yen-Yu Lin"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Hu_Progressive_Feature_Matching_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Hu_Progressive_Feature_Matching_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Hu_2016_CVPR,author = {Hu, Yuan-Ting and Lin, Yen-Yu},title = {Progressive Feature Matching With Alternate Descriptor Selection and Correspondence Enrichment},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We address two difficulties in establishing an accurate system for image matching. First, image matching relies on the descriptor for feature extraction, but the optimal descriptor often varies from image to image, or even patch to patch. Second, conventional matching approaches carry out geometric checking on a small ... | [
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37 | A New Finsler Minimal Path Model With Curvature Penalization for Image Segmentation and Closed Contour Detection | [
"Da Chen",
"Jean-Marie Mirebeau",
"Laurent D. Cohen"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Chen_A_New_Finsler_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Chen_A_New_Finsler_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Chen_2016_CVPR,author = {Chen, Da and Mirebeau, Jean-Marie and Cohen, Laurent D.},title = {A New Finsler Minimal Path Model With Curvature Penalization for Image Segmentation and Closed Contour Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},m... | In this paper, we propose a new curvature penalized minimal path model for image segmentation via closed contour detection based on the weighted Euler elastica curves, firstly introduced to the field of computer vision in [22]. Our image segmentation method extracts a collection of curvature penalized minimal geodesics... | [
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38 | Scale-Aware Alignment of Hierarchical Image Segmentation | [
"Yuhua Chen",
"Dengxin Dai",
"Jordi Pont-Tuset",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Chen_Scale-Aware_Alignment_of_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Chen_Scale-Aware_Alignment_of_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Chen_2016_CVPR,author = {Chen, Yuhua and Dai, Dengxin and Pont-Tuset, Jordi and Van Gool, Luc},title = {Scale-Aware Alignment of Hierarchical Image Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Image segmentation is a key component in many computer vision systems, and it is recovering a prominent spot in the literature as methods improve and overcome their limitations. The outputs of most recent algorithms are in the form of a hierarchical segmentation, which provides segmentation at different scales in a sin... | [
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39 | Deep Interactive Object Selection | [
"Ning Xu",
"Brian Price",
"Scott Cohen",
"Jimei Yang",
"Thomas S. Huang"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Xu_Deep_Interactive_Object_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Xu_Deep_Interactive_Object_CVPR_2016_paper.pdf | null | 1603.04042 | title_snapshot | @InProceedings{Xu_2016_CVPR,author = {Xu, Ning and Price, Brian and Cohen, Scott and Yang, Jimei and Huang, Thomas S.},title = {Deep Interactive Object Selection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Interactive object selection is a very important research problem and has many applications. Previous algorithms require substantial user interactions to estimate the foreground and background distributions. In this paper, we present a novel deep-learning-based algorithm which has much better understanding of objectnes... | [
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40 | Pull the Plug? Predicting If Computers or Humans Should Segment Images | [
"Danna Gurari",
"Suyog Jain",
"Margrit Betke",
"Kristen Grauman"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Gurari_Pull_the_Plug_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Gurari_Pull_the_Plug_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Gurari_2016_CVPR,author = {Gurari, Danna and Jain, Suyog and Betke, Margrit and Grauman, Kristen},title = {Pull the Plug? Predicting If Computers or Humans Should Segment Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016... | Foreground object segmentation is a critical step for many image analysis tasks. While automated methods can produce high-quality results, their failures disappoint users in need of practical solutions. We propose a resource allocation framework for predicting how best to allocate a fixed budget of human annotation e... | [
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41 | In the Shadows, Shape Priors Shine: Using Occlusion to Improve Multi-Region Segmentation | [
"Yuka Kihara",
"Matvey Soloviev",
"Tsuhan Chen"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Kihara_In_the_Shadows_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Kihara_In_the_Shadows_CVPR_2016_paper.pdf | null | 1606.04590 | title_snapshot | @InProceedings{Kihara_2016_CVPR,author = {Kihara, Yuka and Soloviev, Matvey and Chen, Tsuhan},title = {In the Shadows, Shape Priors Shine: Using Occlusion to Improve Multi-Region Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}... | We present a new algorithm for multi-region segmentation of 2D images with objects that may partially occlude each other. Our algorithm is based on the observation that human performance on this task is based both on prior knowledge about plausible shapes and taking into account the presence of occluding objects whose ... | [
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42 | Convexity Shape Constraints for Image Segmentation | [
"Loic A. Royer",
"David L. Richmond",
"Carsten Rother",
"Bjoern Andres",
"Dagmar Kainmueller"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Royer_Convexity_Shape_Constraints_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Royer_Convexity_Shape_Constraints_CVPR_2016_paper.pdf | null | 1509.02122 | title_snapshot | @InProceedings{Royer_2016_CVPR,author = {Royer, Loic A. and Richmond, David L. and Rother, Carsten and Andres, Bjoern and Kainmueller, Dagmar},title = {Convexity Shape Constraints for Image Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},ye... | Segmenting an image into multiple components is a central task in computer vision. In many practical scenarios, prior knowledge about plausible components is available. Incorporating such prior knowledge into models and algorithms for image segmentation is highly desirable, yet can be non-trivial. In this work, we intr... | [
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43 | MCMC Shape Sampling for Image Segmentation With Nonparametric Shape Priors | [
"Ertunc Erdil",
"Sinan Yildirim",
"Mujdat Cetin",
"Tolga Tasdizen"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Erdil_MCMC_Shape_Sampling_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Erdil_MCMC_Shape_Sampling_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Erdil_MCMC_Shape_Sampling_2016_CVPR_supplemental.pdf | 1611.03749 | title_snapshot | @InProceedings{Erdil_2016_CVPR,author = {Erdil, Ertunc and Yildirim, Sinan and Cetin, Mujdat and Tasdizen, Tolga},title = {MCMC Shape Sampling for Image Segmentation With Nonparametric Shape Priors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = ... | Segmenting images of low quality or with missing data is a challenging problem. Integrating statistical prior information about the shapes to be segmented can improve the segmentation results significantly. Most shape-based segmentation algorithms optimize an energy functional and find a point estimate for the object t... | [
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44 | From Noise Modeling to Blind Image Denoising | [
"Fengyuan Zhu",
"Guangyong Chen",
"Pheng-Ann Heng"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Zhu_From_Noise_Modeling_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Zhu_From_Noise_Modeling_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Zhu_2016_CVPR,author = {Zhu, Fengyuan and Chen, Guangyong and Heng, Pheng-Ann},title = {From Noise Modeling to Blind Image Denoising},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Traditional image denoising algorithms always assume the noise to be homogeneous white Gaussian distributed. However, the noise on real images can be much more complex empirically. This paper addresses this problem and proposes a novel blind image denoising algorithm which can cope with real-world noisy images even whe... | [
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45 | Efficient and Robust Color Consistency for Community Photo Collections | [
"Jaesik Park",
"Yu-Wing Tai",
"Sudipta N. Sinha",
"In So Kweon"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Park_Efficient_and_Robust_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Park_Efficient_and_Robust_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Park_Efficient_and_Robust_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Park_2016_CVPR,author = {Park, Jaesik and Tai, Yu-Wing and Sinha, Sudipta N. and Kweon, In So},title = {Efficient and Robust Color Consistency for Community Photo Collections},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We present an efficient technique to optimize color consistency of a collection of images depicting a common scene. Our method first recovers sparse pixel correspondences in the input images and stacks them into a matrix with many missing entries. We show that this matrix satisfies a rank two constraint under a simple ... | [
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46 | Needle-Match: Reliable Patch Matching Under High Uncertainty | [
"Or Lotan",
"Michal Irani"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Lotan_Needle-Match_Reliable_Patch_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Lotan_Needle-Match_Reliable_Patch_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Lotan_2016_CVPR,author = {Lotan, Or and Irani, Michal},title = {Needle-Match: Reliable Patch Matching Under High Uncertainty},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Reliable patch-matching forms the basis for many algorithms (super-resolution, denoising, inpainting, etc.) However, when the image quality deteriorates (by noise, blur or geometric distortions), the reliability of patch-matching deteriorates as well. Matched patches in the degraded image, do not necessarily imply sim... | [
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47 | ReconNet: Non-Iterative Reconstruction of Images From Compressively Sensed Measurements | [
"Kuldeep Kulkarni",
"Suhas Lohit",
"Pavan Turaga",
"Ronan Kerviche",
"Amit Ashok"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Kulkarni_ReconNet_Non-Iterative_Reconstruction_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Kulkarni_ReconNet_Non-Iterative_Reconstruction_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Kulkarni_ReconNet_Non-Iterative_Reconstruction_2016_CVPR_supplemental.pdf | 1601.06892 | title_judge | @InProceedings{Kulkarni_2016_CVPR,author = {Kulkarni, Kuldeep and Lohit, Suhas and Turaga, Pavan and Kerviche, Ronan and Ashok, Amit},title = {ReconNet: Non-Iterative Reconstruction of Images From Compressively Sensed Measurements},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recogniti... | The goal of this paper is to present a non-iterative and more importantly an extremely fast algorithm to reconstruct images from compressively sensed (CS) random measurements. To this end, we propose a novel convolutional neural network (CNN) architecture which takes in CS measurements of an image as input and outputs... | [
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48 | Soft-Segmentation Guided Object Motion Deblurring | [
"Jinshan Pan",
"Zhe Hu",
"Zhixun Su",
"Hsin-Ying Lee",
"Ming-Hsuan Yang"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Pan_Soft-Segmentation_Guided_Object_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Pan_Soft-Segmentation_Guided_Object_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Pan_2016_CVPR,author = {Pan, Jinshan and Hu, Zhe and Su, Zhixun and Lee, Hsin-Ying and Yang, Ming-Hsuan},title = {Soft-Segmentation Guided Object Motion Deblurring},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Object motion blur is a challenging problem as the foreground and the background in the scenes undergo different types of image degradation due to movements in various directions and speed. Most object motion deblurring methods address this problem by segmenting blurred images into regions where different kernels are e... | [
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49 | Two Illuminant Estimation and User Correction Preference | [
"Dongliang Cheng",
"Abdelrahman Abdelhamed",
"Brian Price",
"Scott Cohen",
"Michael S. Brown"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Cheng_Two_Illuminant_Estimation_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Cheng_Two_Illuminant_Estimation_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Cheng_2016_CVPR,author = {Cheng, Dongliang and Abdelhamed, Abdelrahman and Price, Brian and Cohen, Scott and Brown, Michael S.},title = {Two Illuminant Estimation and User Correction Preference},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {Ju... | This paper examines the problem of white-balance correction when a scene contains two illuminations. This is a two step process: 1) estimate the two illuminants; and 2) correct the image. Existing methods attempt to estimate a spatially varying illumination map, however, results are error prone and the resulting ill... | [
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50 | Deep Contrast Learning for Salient Object Detection | [
"Guanbin Li",
"Yizhou Yu"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Li_Deep_Contrast_Learning_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Li_Deep_Contrast_Learning_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Li_Deep_Contrast_Learning_2016_CVPR_supplemental.pdf | 1603.01976 | title_snapshot | @InProceedings{Li_2016_CVPR,author = {Li, Guanbin and Yu, Yizhou},title = {Deep Contrast Learning for Salient Object Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Salient object detection has recently witnessed substantial progress due to powerful features extracted using deep convolutional neural networks (CNNs). However, existing CNN-based methods operate at the patch level instead of the pixel level. Resulting saliency maps are typically blurry, especially near the boundary o... | [
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51 | Multiview Image Completion With Space Structure Propagation | [
"Seung-Hwan Baek",
"Inchang Choi",
"Min H. Kim"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Baek_Multiview_Image_Completion_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Baek_Multiview_Image_Completion_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Baek_Multiview_Image_Completion_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Baek_2016_CVPR,author = {Baek, Seung-Hwan and Choi, Inchang and Kim, Min H.},title = {Multiview Image Completion With Space Structure Propagation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We present a multiview image completion method that provides geometric consistency among different views by propagating space structures. Since a user specifies the region to be completed in one of multiview photographs casually taken in a scene, the proposed method enables us to complete the set of photographs with ge... | [
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52 | Composition-Preserving Deep Photo Aesthetics Assessment | [
"Long Mai",
"Hailin Jin",
"Feng Liu"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Mai_Composition-Preserving_Deep_Photo_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Mai_Composition-Preserving_Deep_Photo_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Mai_2016_CVPR,author = {Mai, Long and Jin, Hailin and Liu, Feng},title = {Composition-Preserving Deep Photo Aesthetics Assessment},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Photo aesthetics assessment is challenging. Deep convolutional neural network (ConvNet) methods have recently shown promising results for aesthetics assessment. The performance of these deep ConvNet methods, however, is often compromised by the constraint that the neural network only takes the fixed-size input. To acco... | [
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53 | Automatic Image Cropping : A Computational Complexity Study | [
"Jiansheng Chen",
"Gaocheng Bai",
"Shaoheng Liang",
"Zhengqin Li"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Chen_Automatic_Image_Cropping_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Chen_Automatic_Image_Cropping_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Chen_2016_CVPR,author = {Chen, Jiansheng and Bai, Gaocheng and Liang, Shaoheng and Li, Zhengqin},title = {Automatic Image Cropping : A Computational Complexity Study},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Attention based automatic image cropping aims at preserving the most visually important region in an image. A common task in this kind of method is to search for the smallest rectangle inside which the summed attention is maximized. We demonstrate that under appropriate formulations, this task can be achieved using eff... | [
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54 | A Deeper Look at Saliency: Feature Contrast, Semantics, and Beyond | [
"Neil D. B. Bruce",
"Christopher Catton",
"Sasa Janjic"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Bruce_A_Deeper_Look_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Bruce_A_Deeper_Look_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Bruce_2016_CVPR,author = {Bruce, Neil D. B. and Catton, Christopher and Janjic, Sasa},title = {A Deeper Look at Saliency: Feature Contrast, Semantics, and Beyond},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | In this paper we consider the problem of visual saliency modeling, including both human gaze prediction and salient object segmentation. The overarching goal of the paper is to identify high level considerations relevant to deriving more sophisticated visual saliency models. A deep learning model based on fully convolu... | [
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55 | Spatially Binned ROC: A Comprehensive Saliency Metric | [
"Calden Wloka",
"John Tsotsos"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Wloka_Spatially_Binned_ROC_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Wloka_Spatially_Binned_ROC_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Wloka_2016_CVPR,author = {Wloka, Calden and Tsotsos, John},title = {Spatially Binned ROC: A Comprehensive Saliency Metric},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | A recent trend in saliency algorithm development is large-scale benchmarking and algorithm ranking with ground truth provided by datasets of human fixations. In order to accommodate the strong bias humans have toward central fixations, it is common to replace traditional ROC metrics with a shuffled ROC metric which use... | [
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56 | GraB: Visual Saliency via Novel Graph Model and Background Priors | [
"Qiaosong Wang",
"Wen Zheng",
"Robinson Piramuthu"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Wang_GraB_Visual_Saliency_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Wang_GraB_Visual_Saliency_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Wang_2016_CVPR,author = {Wang, Qiaosong and Zheng, Wen and Piramuthu, Robinson},title = {GraB: Visual Saliency via Novel Graph Model and Background Priors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We propose an unsupervised bottom-up saliency detection approach by exploiting novel graph structure and background priors. The input image is represented as an undirected graph with superpixels as nodes. Feature vectors are extracted from each node to cover regional color, contrast and texture information. A novel gra... | [
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57 | Predicting When Saliency Maps Are Accurate and Eye Fixations Consistent | [
"Anna Volokitin",
"Michael Gygli",
"Xavier Boix"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Volokitin_Predicting_When_Saliency_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Volokitin_Predicting_When_Saliency_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Volokitin_2016_CVPR,author = {Volokitin, Anna and Gygli, Michael and Boix, Xavier},title = {Predicting When Saliency Maps Are Accurate and Eye Fixations Consistent},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Many computational models of visual attention use image features and machine learning techniques to predict eye fixation locations as saliency maps. Recently, the success of Deep Convolutional Neural Networks (DCNNs) for object recognition has opened a new avenue for computational models of visual attention due to the ... | [
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58 | Split and Match: Example-Based Adaptive Patch Sampling for Unsupervised Style Transfer | [
"Oriel Frigo",
"Neus Sabater",
"Julie Delon",
"Pierre Hellier"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Frigo_Split_and_Match_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Frigo_Split_and_Match_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Frigo_2016_CVPR,author = {Frigo, Oriel and Sabater, Neus and Delon, Julie and Hellier, Pierre},title = {Split and Match: Example-Based Adaptive Patch Sampling for Unsupervised Style Transfer},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June}... | This paper presents a novel unsupervised method to transfer the style of an example image to a source image. The complex notion of image style is here considered as a local texture transfer, eventually coupled with a global color transfer. For the local texture transfer, we propose a new method based on an adaptive pat... | [
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59 | Detection and Accurate Localization of Circular Fiducials Under Highly Challenging Conditions | [
"Lilian Calvet",
"Pierre Gurdjos",
"Carsten Griwodz",
"Simone Gasparini"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Calvet_Detection_and_Accurate_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Calvet_Detection_and_Accurate_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Calvet_2016_CVPR,author = {Calvet, Lilian and Gurdjos, Pierre and Griwodz, Carsten and Gasparini, Simone},title = {Detection and Accurate Localization of Circular Fiducials Under Highly Challenging Conditions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVP... | Using fiducial markers ensures reliable detection and identification of planar features in images. Fiducials are used in a wide range of applications, especially when a reliable visual reference is needed, e.g., to track the camera in cluttered or textureless environments. A marker designed for such applications must b... | [
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60 | Scene Recognition With CNNs: Objects, Scales and Dataset Bias | [
"Luis Herranz",
"Shuqiang Jiang",
"Xiangyang Li"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Herranz_Scene_Recognition_With_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Herranz_Scene_Recognition_With_CVPR_2016_paper.pdf | null | 1801.06867 | title_snapshot | @InProceedings{Herranz_2016_CVPR,author = {Herranz, Luis and Jiang, Shuqiang and Li, Xiangyang},title = {Scene Recognition With CNNs: Objects, Scales and Dataset Bias},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Since scenes are composed in part of objects, accurate recognition of scenes requires knowledge about both scenes and objects. In this paper we address two related problems: 1) scale induced dataset bias in multi-scale convolutional neural network (CNN) architectures, and 2) how to combine effectively scene-centric and... | [
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61 | Learning Action Maps of Large Environments via First-Person Vision | [
"Nicholas Rhinehart",
"Kris M. Kitani"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Rhinehart_Learning_Action_Maps_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Rhinehart_Learning_Action_Maps_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Rhinehart_Learning_Action_Maps_2016_CVPR_supplemental.zip | 1605.01679 | title_snapshot | @InProceedings{Rhinehart_2016_CVPR,author = {Rhinehart, Nicholas and Kitani, Kris M.},title = {Learning Action Maps of Large Environments via First-Person Vision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | When people observe and interact with physical spaces, they are able to associate functionality to regions in the environment. Our goal is to automate functional understanding of large spaces by leveraging activity demonstrations recorded from an ego-centric viewpoint. The method we describe enables functionality estim... | [
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62 | Single-Image Crowd Counting via Multi-Column Convolutional Neural Network | [
"Yingying Zhang",
"Desen Zhou",
"Siqin Chen",
"Shenghua Gao",
"Yi Ma"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Zhang_Single-Image_Crowd_Counting_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_Single-Image_Crowd_Counting_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Zhang_2016_CVPR,author = {Zhang, Yingying and Zhou, Desen and Chen, Siqin and Gao, Shenghua and Ma, Yi},title = {Single-Image Crowd Counting via Multi-Column Convolutional Neural Network},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},yea... | This paper aims to develop a method that can accurately estimate the crowd count from an individual image with arbitrary crowd density and arbitrary perspective. To this end,we have proposed a simple but effective Multi-column Convolutional Neural Network (MCNN) architecture to map the image to its crowd density map. T... | [
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63 | Shallow and Deep Convolutional Networks for Saliency Prediction | [
"Junting Pan",
"Elisa Sayrol",
"Xavier Giro-i-Nieto",
"Kevin McGuinness",
"Noel E. O'Connor"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Pan_Shallow_and_Deep_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Pan_Shallow_and_Deep_CVPR_2016_paper.pdf | null | 1603.00845 | title_snapshot | @InProceedings{Pan_2016_CVPR,author = {Pan, Junting and Sayrol, Elisa and Giro-i-Nieto, Xavier and McGuinness, Kevin and O'Connor, Noel E.},title = {Shallow and Deep Convolutional Networks for Saliency Prediction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month =... | The prediction of salient areas in images has been traditionally addressed with hand-crafted features based on neuroscience principles. This paper, however, addresses the problem with a completely data-driven approach by training a convolutional neural network (convnet). The learning process is formulated as a minimiza... | [
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64 | Sample and Filter: Nonparametric Scene Parsing via Efficient Filtering | [
"Mohammad Najafi",
"Sarah Taghavi Namin",
"Mathieu Salzmann",
"Lars Petersson"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Najafi_Sample_and_Filter_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Najafi_Sample_and_Filter_CVPR_2016_paper.pdf | null | 1511.04960 | title_snapshot | @InProceedings{Najafi_2016_CVPR,author = {Najafi, Mohammad and Namin, Sarah Taghavi and Salzmann, Mathieu and Petersson, Lars},title = {Sample and Filter: Nonparametric Scene Parsing via Efficient Filtering},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June... | Scene parsing has attracted a lot of attention in computer vision. While parametric models have proven effective for this task, they cannot easily incorporate new training data. By contrast, nonparametric approaches, which bypass any learning phase and directly transfer the labels from the training data to the query im... | [
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65 | DeLay: Robust Spatial Layout Estimation for Cluttered Indoor Scenes | [
"Saumitro Dasgupta",
"Kuan Fang",
"Kevin Chen",
"Silvio Savarese"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Dasgupta_DeLay_Robust_Spatial_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Dasgupta_DeLay_Robust_Spatial_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Dasgupta_2016_CVPR,author = {Dasgupta, Saumitro and Fang, Kuan and Chen, Kevin and Savarese, Silvio},title = {DeLay: Robust Spatial Layout Estimation for Cluttered Indoor Scenes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016... | We consider the problem of estimating the spatial layout of an indoor scene from a monocular RGB image, modeled as the projection of a 3D cuboid. Existing solutions to this problem often rely strongly on hand-engineered features and vanishing point detection, which are prone to failure in the presence of clutter. In th... | [
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66 | A Text Detection System for Natural Scenes With Convolutional Feature Learning and Cascaded Classification | [
"Siyu Zhu",
"Richard Zanibbi"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Zhu_A_Text_Detection_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Zhu_A_Text_Detection_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Zhu_2016_CVPR,author = {Zhu, Siyu and Zanibbi, Richard},title = {A Text Detection System for Natural Scenes With Convolutional Feature Learning and Cascaded Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We propose a system that finds text in natural scenes using a variety of cues. Our novel data-driven method incorporates coarse-to-fine detection of character pixels using convolutional features (Text-Conv), followed by extracting connected components (CCs) from characters using edge and color features, and finally per... | [
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67 | Reversible Recursive Instance-Level Object Segmentation | [
"Xiaodan Liang",
"Yunchao Wei",
"Xiaohui Shen",
"Zequn Jie",
"Jiashi Feng",
"Liang Lin",
"Shuicheng Yan"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Liang_Reversible_Recursive_Instance-Level_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Liang_Reversible_Recursive_Instance-Level_CVPR_2016_paper.pdf | null | 1511.04517 | title_snapshot | @InProceedings{Liang_2016_CVPR,author = {Liang, Xiaodan and Wei, Yunchao and Shen, Xiaohui and Jie, Zequn and Feng, Jiashi and Lin, Liang and Yan, Shuicheng},title = {Reversible Recursive Instance-Level Object Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR... | In this work, we propose a novel Reversible Recursive Instance-level Object Segmentation (R2-IOS) framework to address the challenging instance-level object segmentation task. R2-IOS consists of a reversible proposal refinement sub-network that predicts bounding box offsets for refining the object proposal locations, a... | [
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68 | Coherent Parametric Contours for Interactive Video Object Segmentation | [
"Yao Lu",
"Xue Bai",
"Linda Shapiro",
"Jue Wang"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Lu_Coherent_Parametric_Contours_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Lu_Coherent_Parametric_Contours_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Lu_2016_CVPR,author = {Lu, Yao and Bai, Xue and Shapiro, Linda and Wang, Jue},title = {Coherent Parametric Contours for Interactive Video Object Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Interactive video segmentation systems aim at producing sub-pixel-level object boundaries for visual effect applications. Recent approaches mainly focus on using sparse user input (i.e. scribbles) for efficient segmentation; however, the quality of the final object boundaries is not satisfactory for the following reaso... | [
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69 | Manifold SLIC: A Fast Method to Compute Content-Sensitive Superpixels | [
"Yong-Jin Liu",
"Cheng-Chi Yu",
"Min-Jing Yu",
"Ying He"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Liu_Manifold_SLIC_A_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Liu_Manifold_SLIC_A_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Liu_Manifold_SLIC_A_2016_CVPR_supplemental.pdf | null | null | @InProceedings{Liu_2016_CVPR,author = {Liu, Yong-Jin and Yu, Cheng-Chi and Yu, Min-Jing and He, Ying},title = {Manifold SLIC: A Fast Method to Compute Content-Sensitive Superpixels},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Superpixels are perceptually meaningful atomic regions that can effectively capture image features. Among various methods for computing uniform superpixels, simple linear iterative clustering (SLIC) is popular due to its simplicity and high performance. In this paper, we extend SLIC to compute content-sensitive superpi... | [
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70 | Deep Saliency With Encoded Low Level Distance Map and High Level Features | [
"Gayoung Lee",
"Yu-Wing Tai",
"Junmo Kim"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Lee_Deep_Saliency_With_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Lee_Deep_Saliency_With_CVPR_2016_paper.pdf | null | 1604.05495 | title_snapshot | @InProceedings{Lee_2016_CVPR,author = {Lee, Gayoung and Tai, Yu-Wing and Kim, Junmo},title = {Deep Saliency With Encoded Low Level Distance Map and High Level Features},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Recent advances in saliency detection have utilized deep learning to obtain high level features to detect salient regions in a scene. They have demonstrated superior results over previous works that utilize hand-crafted low level features for saliency detection. In this paper, we demonstrate that the hand-crafted featu... | [
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71 | Instance-Level Segmentation for Autonomous Driving With Deep Densely Connected MRFs | [
"Ziyu Zhang",
"Sanja Fidler",
"Raquel Urtasun"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Zhang_Instance-Level_Segmentation_for_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_Instance-Level_Segmentation_for_CVPR_2016_paper.pdf | null | 1512.06735 | title_snapshot | @InProceedings{Zhang_2016_CVPR,author = {Zhang, Ziyu and Fidler, Sanja and Urtasun, Raquel},title = {Instance-Level Segmentation for Autonomous Driving With Deep Densely Connected MRFs},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Our aim is to provide a pixel-wise instance-level labeling of a monocular image in the context of autonomous driving. We build on recent work [Zhang et al., ICCV15] that trained a convolutional neural net to predict instance labeling in local image patches, extracted exhaustively in a stride from an image. A simple Ma... | [
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72 | DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection | [
"Nian Liu",
"Junwei Han"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Liu_DHSNet_Deep_Hierarchical_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Liu_DHSNet_Deep_Hierarchical_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Liu_2016_CVPR,author = {Liu, Nian and Han, Junwei},title = {DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Traditional1 salient object detection models often use hand-crafted features to formulate contrast and various prior knowledge, and then combine them artificially. In this work, we propose a novel end-to-end deep hierarchical saliency network (DHSNet) based on convolutional neural networks for detecting salient objects... | [
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73 | Object Co-Segmentation via Graph Optimized-Flexible Manifold Ranking | [
"Rong Quan",
"Junwei Han",
"Dingwen Zhang",
"Feiping Nie"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Quan_Object_Co-Segmentation_via_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Quan_Object_Co-Segmentation_via_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Quan_2016_CVPR,author = {Quan, Rong and Han, Junwei and Zhang, Dingwen and Nie, Feiping},title = {Object Co-Segmentation via Graph Optimized-Flexible Manifold Ranking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Aiming at automatically discovering the common objects contained in a set of relevant images and segmenting them as foreground simultaneously, object co-segmentation has become an active research topic in recent years. Although a number of approaches have been proposed to address this problem, many of them are designed... | [
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74 | Primary Object Segmentation in Videos via Alternate Convex Optimization of Foreground and Background Distributions | [
"Won-Dong Jang",
"Chulwoo Lee",
"Chang-Su Kim"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Jang_Primary_Object_Segmentation_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Jang_Primary_Object_Segmentation_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Jang_2016_CVPR,author = {Jang, Won-Dong and Lee, Chulwoo and Kim, Chang-Su},title = {Primary Object Segmentation in Videos via Alternate Convex Optimization of Foreground and Background Distributions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month... | An unsupervised video object segmentation algorithm, which discovers a primary object in a video sequence automatically, is proposed in this work. We introduce three energies in terms of foreground and background probability distributions: Markov, spatiotemporal, and antagonistic energies. Then, we minimize a hybrid of... | [
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75 | Automatic Fence Segmentation in Videos of Dynamic Scenes | [
"Renjiao Yi",
"Jue Wang",
"Ping Tan"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Yi_Automatic_Fence_Segmentation_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Yi_Automatic_Fence_Segmentation_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Yi_2016_CVPR,author = {Yi, Renjiao and Wang, Jue and Tan, Ping},title = {Automatic Fence Segmentation in Videos of Dynamic Scenes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We present a fully automatic approach to detect and segment fence-like occluders from a video clip. Unlike previous approaches that usually assume either static scenes or cameras, our method is capable of handling both dynamic scenes and moving cameras. Under a bottom-up framework, it first clusters pixels into coheren... | [
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76 | Discovering the Physical Parts of an Articulated Object Class From Multiple Videos | [
"Luca Del Pero",
"Susanna Ricco",
"Rahul Sukthankar",
"Vittorio Ferrari"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Del_Pero_Discovering_the_Physical_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Del_Pero_Discovering_the_Physical_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Pero_2016_CVPR,author = {Del Pero, Luca and Ricco, Susanna and Sukthankar, Rahul and Ferrari, Vittorio},title = {Discovering the Physical Parts of an Articulated Object Class From Multiple Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {... | We propose a motion-based method to discover the physical parts of an articulated object class (e.g. head/torso/leg of a horse) from multiple videos. The key is to find object regions that exhibit consistent motion relative to the rest of the object, across multiple videos. We can then learn a location model for the pa... | [
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77 | A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation | [
"Federico Perazzi",
"Jordi Pont-Tuset",
"Brian McWilliams",
"Luc Van Gool",
"Markus Gross",
"Alexander Sorkine-Hornung"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Perazzi_A_Benchmark_Dataset_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Perazzi_A_Benchmark_Dataset_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Perazzi_2016_CVPR,author = {Perazzi, Federico and Pont-Tuset, Jordi and McWilliams, Brian and Van Gool, Luc and Gross, Markus and Sorkine-Hornung, Alexander},title = {A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer ... | Over the years, datasets and benchmarks have proven their fundamental importance in computer vision research, enabling targeted progress and objective comparisons in many fields. At the same time, legacy datasets may impend the evolution of a field due to saturated algorithm performance and the lack of contemporary, hi... | [
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78 | Learning Temporal Regularity in Video Sequences | [
"Mahmudul Hasan",
"Jonghyun Choi",
"Jan Neumann",
"Amit K. Roy-Chowdhury",
"Larry S. Davis"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Hasan_Learning_Temporal_Regularity_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Hasan_Learning_Temporal_Regularity_CVPR_2016_paper.pdf | null | 1604.04574 | title_snapshot | @InProceedings{Hasan_2016_CVPR,author = {Hasan, Mahmudul and Choi, Jonghyun and Neumann, Jan and Roy-Chowdhury, Amit K. and Davis, Larry S.},title = {Learning Temporal Regularity in Video Sequences},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = ... | Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of `meaningfulness' as well as clutters in the scene. We approach this problem by learning a generative model for regular motion patterns (termed as regularity) using multiple sources with very limited supervi... | [
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79 | Bilateral Space Video Segmentation | [
"Nicolas Maerki",
"Federico Perazzi",
"Oliver Wang",
"Alexander Sorkine-Hornung"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Maerki_Bilateral_Space_Video_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Maerki_Bilateral_Space_Video_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Maerki_2016_CVPR,author = {Maerki, Nicolas and Perazzi, Federico and Wang, Oliver and Sorkine-Hornung, Alexander},title = {Bilateral Space Video Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | In this work, we propose a novel approach to video segmentation that operates in bilateral space. We design a new energy on the vertices of a regularly sampled spatio-temporal bilateral grid, which can be solved efficiently using a standard graph cut label assignment. Using a bilateral formulation, the energy that we m... | [
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80 | ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering | [
"Zhang Zhang",
"Kaiqi Huang",
"Tieniu Tan",
"Peipei Yang",
"Jun Li"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Zhang_ReD-SFA_Relation_Discovery_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_ReD-SFA_Relation_Discovery_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Zhang_2016_CVPR,author = {Zhang, Zhang and Huang, Kaiqi and Tan, Tieniu and Yang, Peipei and Li, Jun},title = {ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {Jun... | For spectral embedding/clustering, it is still an open problem on how to construct an relation graph to reflect the intrinsic structures in data. In this paper, we proposed an approach, named Relation Discovery based Slow Feature Analysis (ReD-SFA), for feature learning and graph construction simultaneously. Given an i... | [
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81 | Training Region-Based Object Detectors With Online Hard Example Mining | [
"Abhinav Shrivastava",
"Abhinav Gupta",
"Ross Girshick"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Shrivastava_Training_Region-Based_Object_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Shrivastava_Training_Region-Based_Object_CVPR_2016_paper.pdf | null | 1604.03540 | title_snapshot | @InProceedings{Shrivastava_2016_CVPR,author = {Shrivastava, Abhinav and Gupta, Abhinav and Girshick, Ross},title = {Training Region-Based Object Detectors With Online Hard Example Mining},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | The field of object detection has made significant advances riding on the wave of region-based ConvNets, but their training procedure still includes many heuristics and hyperparameters that are costly to tune. We present a simple yet surprisingly effective online hard example mining (OHEM) algorithm for training region... | [
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82 | Deep Residual Learning for Image Recognition | [
"Kaiming He",
"Xiangyu Zhang",
"Shaoqing Ren",
"Jian Sun"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/He_Deep_Residual_Learning_2016_CVPR_supplemental.pdf | 1512.03385 | title_snapshot | @InProceedings{He_2016_CVPR,author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},title = {Deep Residual Learning for Image Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced... | [
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83 | You Only Look Once: Unified, Real-Time Object Detection | [
"Joseph Redmon",
"Santosh Divvala",
"Ross Girshick",
"Ali Farhadi"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Redmon_You_Only_Look_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Redmon_You_Only_Look_CVPR_2016_paper.pdf | null | 1506.02640 | title_snapshot | @InProceedings{Redmon_2016_CVPR,author = {Redmon, Joseph and Divvala, Santosh and Girshick, Ross and Farhadi, Ali},title = {You Only Look Once: Unified, Real-Time Object Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class ... | [
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84 | LocNet: Improving Localization Accuracy for Object Detection | [
"Spyros Gidaris",
"Nikos Komodakis"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Gidaris_LocNet_Improving_Localization_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Gidaris_LocNet_Improving_Localization_CVPR_2016_paper.pdf | null | 1511.07763 | title_snapshot | @InProceedings{Gidaris_2016_CVPR,author = {Gidaris, Spyros and Komodakis, Nikos},title = {LocNet: Improving Localization Accuracy for Object Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We propose a novel object localization methodology with the purpose of boosting the localization accuracy of state-of-the-art object detection systems. Our model, given a search region, aims at returning the bounding box of an object of interest inside this region. To accomplish its goal, it relies on assigning conditi... | [
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85 | Sketch Me That Shoe | [
"Qian Yu",
"Feng Liu",
"Yi-Zhe Song",
"Tao Xiang",
"Timothy M. Hospedales",
"Chen-Change Loy"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Yu_Sketch_Me_That_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Yu_Sketch_Me_That_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Yu_2016_CVPR,author = {Yu, Qian and Liu, Feng and Song, Yi-Zhe and Xiang, Tao and Hospedales, Timothy M. and Loy, Chen-Change},title = {Sketch Me That Shoe},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We investigate the problem of fine-grained sketch-based image retrieval (SBIR), where free-hand human sketches are used as queries to perform instance-level retrieval of images. This is an extremely challenging task because (i) visual comparisons not only need to be fine-grained but also executed cross-domain, (ii) fre... | [
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86 | Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images | [
"Shuran Song",
"Jianxiong Xiao"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Song_Deep_Sliding_Shapes_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Song_Deep_Sliding_Shapes_CVPR_2016_paper.pdf | null | 1511.02300 | title_snapshot | @InProceedings{Song_2016_CVPR,author = {Song, Shuran and Xiao, Jianxiong},title = {Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We focus on the task of amodal 3D object detection in RGB-D images, which aims to produce a 3D bounding box of an object in metric form at its full extent. We introduce Deep Sliding Shapes, a 3D ConvNet formulation that takes a 3D volumetric scene from a RGB-D image as input and outputs 3D object bounding boxes. In our... | [
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87 | Object Detection From Video Tubelets With Convolutional Neural Networks | [
"Kai Kang",
"Wanli Ouyang",
"Hongsheng Li",
"Xiaogang Wang"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Kang_Object_Detection_From_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Kang_Object_Detection_From_CVPR_2016_paper.pdf | null | 1604.04053 | title_snapshot | @InProceedings{Kang_2016_CVPR,author = {Kang, Kai and Ouyang, Wanli and Li, Hongsheng and Wang, Xiaogang},title = {Object Detection From Video Tubelets With Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Deep Convolution Neural Networks (CNNs) have shown impressive performance in various vision tasks such as image classification, object detection and semantic segmentation. For object detection, particularly in still images, the performance has been significantly increased last year thanks to powerful deep networks (e.g... | [
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88 | Learning With Side Information Through Modality Hallucination | [
"Judy Hoffman",
"Saurabh Gupta",
"Trevor Darrell"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Hoffman_Learning_With_Side_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Hoffman_Learning_With_Side_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Hoffman_2016_CVPR,author = {Hoffman, Judy and Gupta, Saurabh and Darrell, Trevor},title = {Learning With Side Information Through Modality Hallucination},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We present a modality hallucination architecture for training an RGB object detection model which incorporates depth side information at training time. Our convolutional hallucination network learns a new and complementary RGB image representation which is taught to mimic convolutional mid-level features from a depth n... | [
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89 | Object-Proposal Evaluation Protocol is 'Gameable' | [
"Neelima Chavali",
"Harsh Agrawal",
"Aroma Mahendru",
"Dhruv Batra"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Chavali_Object-Proposal_Evaluation_Protocol_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Chavali_Object-Proposal_Evaluation_Protocol_CVPR_2016_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Chavali_Object-Proposal_Evaluation_Protocol_2016_CVPR_supplemental.pdf | 1505.05836 | title_snapshot | @InProceedings{Chavali_2016_CVPR,author = {Chavali, Neelima and Agrawal, Harsh and Mahendru, Aroma and Batra, Dhruv},title = {Object-Proposal Evaluation Protocol is 'Gameable'},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Object proposals have quickly become the de-facto pre-processing step in a number of vision pipelines (for object detection, object discovery, and other tasks). Their performance is usually evaluated on partially annotated datasets. In this paper, we argue that the choice of using a partially annotated dataset for eval... | [
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90 | HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection | [
"Tao Kong",
"Anbang Yao",
"Yurong Chen",
"Fuchun Sun"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Kong_HyperNet_Towards_Accurate_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Kong_HyperNet_Towards_Accurate_CVPR_2016_paper.pdf | null | 1604.00600 | title_snapshot | @InProceedings{Kong_2016_CVPR,author = {Kong, Tao and Yao, Anbang and Chen, Yurong and Sun, Fuchun},title = {HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Almost all of the current top-performing object detection networks employ region proposals to guide the search for object instances. State-of-the-art region proposal methods usually need several thousand proposals to get high recall, thus hurting the detection efficiency. Although the latest Region Proposal Network met... | [
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91 | We Don't Need No Bounding-Boxes: Training Object Class Detectors Using Only Human Verification | [
"Dim P. Papadopoulos",
"Jasper R. R. Uijlings",
"Frank Keller",
"Vittorio Ferrari"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Papadopoulos_We_Dont_Need_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Papadopoulos_We_Dont_Need_CVPR_2016_paper.pdf | null | 1602.08405 | title_snapshot | @InProceedings{Papadopoulos_2016_CVPR,author = {Papadopoulos, Dim P. and Uijlings, Jasper R. R. and Keller, Frank and Ferrari, Vittorio},title = {We Don't Need No Bounding-Boxes: Training Object Class Detectors Using Only Human Verification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern... | Training object class detectors typically requires a large set of images in which objects are annotated by bounding-boxes. However, manually drawing bounding-boxes is very time consuming. We propose a new scheme for training object detectors which only requires annotators to verify bounding-boxes produced automatically... | [
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92 | Factors in Finetuning Deep Model for Object Detection With Long-Tail Distribution | [
"Wanli Ouyang",
"Xiaogang Wang",
"Cong Zhang",
"Xiaokang Yang"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Ouyang_Factors_in_Finetuning_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Ouyang_Factors_in_Finetuning_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Ouyang_2016_CVPR,author = {Ouyang, Wanli and Wang, Xiaogang and Zhang, Cong and Yang, Xiaokang},title = {Factors in Finetuning Deep Model for Object Detection With Long-Tail Distribution},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},yea... | Finetuning from a pretrained deep model is found to yield state-of-the-art performance for many vision tasks. This paper investigates many factors that influence the performance in finetuning for object detection. There is a long-tailed distribution of sample numbers for classes in object detection. Our analysis and ... | [
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93 | Information-Driven Adaptive Structured-Light Scanners | [
"Guy Rosman",
"Daniela Rus",
"John W. Fisher III"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Rosman_Information-Driven_Adaptive_Structured-Light_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Rosman_Information-Driven_Adaptive_Structured-Light_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Rosman_2016_CVPR,author = {Rosman, Guy and Rus, Daniela and , III, John W. Fisher},title = {Information-Driven Adaptive Structured-Light Scanners},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Sensor planning and active sensing, long studied in robotics, adapt sensor positioning and operation mode in order to maximize information gain. While these concepts are often used to reason about 3D sensors, these are usually treated as a predefined, black-box, component. In this paper we show how the same principles ... | [
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94 | Simultaneous Optical Flow and Intensity Estimation From an Event Camera | [
"Patrick Bardow",
"Andrew J. Davison",
"Stefan Leutenegger"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Bardow_Simultaneous_Optical_Flow_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Bardow_Simultaneous_Optical_Flow_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Bardow_2016_CVPR,author = {Bardow, Patrick and Davison, Andrew J. and Leutenegger, Stefan},title = {Simultaneous Optical Flow and Intensity Estimation From an Event Camera},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | Event cameras are bio-inspired vision sensors which mimic retinas to measure per-pixel intensity change rather than outputting an actual intensity image. This proposed paradigm shift away from traditional frame cameras offers significant potential advantages: namely avoiding high data rates, dynamic range limitations a... | [
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95 | Macroscopic Interferometry: Rethinking Depth Estimation With Frequency-Domain Time-Of-Flight | [
"Achuta Kadambi",
"Jamie Schiel",
"Ramesh Raskar"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Kadambi_Macroscopic_Interferometry_Rethinking_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Kadambi_Macroscopic_Interferometry_Rethinking_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Kadambi_2016_CVPR,author = {Kadambi, Achuta and Schiel, Jamie and Raskar, Ramesh},title = {Macroscopic Interferometry: Rethinking Depth Estimation With Frequency-Domain Time-Of-Flight},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year =... | A form of meter-scale, macroscopic interferometry is proposed using conventional time-of-flight (ToF) sensors. Today, ToF sensors use phase-based sampling, where the phase delay between emitted and received, high-frequency signals encodes distance. This paper examines an alternative ToF architecture, inspired by micron... | [
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96 | ASP Vision: Optically Computing the First Layer of Convolutional Neural Networks Using Angle Sensitive Pixels | [
"Huaijin G. Chen",
"Suren Jayasuriya",
"Jiyue Yang",
"Judy Stephen",
"Sriram Sivaramakrishnan",
"Ashok Veeraraghavan",
"Alyosha Molnar"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Chen_ASP_Vision_Optically_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Chen_ASP_Vision_Optically_CVPR_2016_paper.pdf | null | 1605.03621 | title_snapshot | @InProceedings{Chen_2016_CVPR,author = {Chen, Huaijin G. and Jayasuriya, Suren and Yang, Jiyue and Stephen, Judy and Sivaramakrishnan, Sriram and Veeraraghavan, Ashok and Molnar, Alyosha},title = {ASP Vision: Optically Computing the First Layer of Convolutional Neural Networks Using Angle Sensitive Pixels},booktitle = ... | Deep learning using convolutional neural networks (CNNs) is quickly becoming the state-of-the-art for challenging computer vision applications. However, deep learning's power consumption and bandwidth requirements currently limit its application in embedded and mobile systems with tight energy budgets. In this paper, w... | [
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97 | Computational Imaging for VLBI Image Reconstruction | [
"Katherine L. Bouman",
"Michael D. Johnson",
"Daniel Zoran",
"Vincent L. Fish",
"Sheperd S. Doeleman",
"William T. Freeman"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Bouman_Computational_Imaging_for_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Bouman_Computational_Imaging_for_CVPR_2016_paper.pdf | null | 1512.01413 | title_snapshot | @InProceedings{Bouman_2016_CVPR,author = {Bouman, Katherine L. and Johnson, Michael D. and Zoran, Daniel and Fish, Vincent L. and Doeleman, Sheperd S. and Freeman, William T.},title = {Computational Imaging for VLBI Image Reconstruction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Rec... | Very long baseline interferometry (VLBI) is a technique for imaging celestial radio emissions by simultaneously observing a source from telescopes distributed across Earth. The challenges in reconstructing images from fine angular resolution VLBI data are immense. The data is extremely sparse and noisy, thus requiring ... | [
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98 | You Lead, We Exceed: Labor-Free Video Concept Learning by Jointly Exploiting Web Videos and Images | [
"Chuang Gan",
"Ting Yao",
"Kuiyuan Yang",
"Yi Yang",
"Tao Mei"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Gan_You_Lead_We_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Gan_You_Lead_We_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Gan_2016_CVPR,author = {Gan, Chuang and Yao, Ting and Yang, Kuiyuan and Yang, Yi and Mei, Tao},title = {You Lead, We Exceed: Labor-Free Video Concept Learning by Jointly Exploiting Web Videos and Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},mo... | Video concept learning often requires a large set of training samples. In practice, however, acquiring noise-free training labels with sufficient positive examples is very expensive. A plausible solution for training data collection is by sampling from the vast quantities of images and videos on the Web. Such a solutio... | [
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99 | Track and Segment: An Iterative Unsupervised Approach for Video Object Proposals | [
"Fanyi Xiao",
"Yong Jae Lee"
] | https://openaccess.thecvf.com/content_cvpr_2016/html/Xiao_Track_and_Segment_CVPR_2016_paper.html | https://openaccess.thecvf.com/content_cvpr_2016/papers/Xiao_Track_and_Segment_CVPR_2016_paper.pdf | null | null | null | @InProceedings{Xiao_2016_CVPR,author = {Xiao, Fanyi and Lee, Yong Jae},title = {Track and Segment: An Iterative Unsupervised Approach for Video Object Proposals},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2016}} | We present an unsupervised approach that generates a diverse, ranked set of bounding box and segmentation video object proposals---spatio-temporal tubes that localize the foreground objects---in an unannotated video. In contrast to previous unsupervised methods that either track regions initialized in an arbitrary fra... | [
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-... |
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