CVPR
Collection
Accepted papers for CVPR (IEEE/CVF Conference on Computer Vision and Pattern Recognition), one dataset per year. • 14 items • Updated
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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