ICCV
Collection
Accepted papers for ICCV (IEEE/CVF International Conference on Computer Vision), one dataset per year. • 7 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 | Ask Your Neurons: A Neural-Based Approach to Answering Questions About Images | [
"Mateusz Malinowski",
"Marcus Rohrbach",
"Mario Fritz"
] | https://openaccess.thecvf.com/content_iccv_2015/html/Malinowski_Ask_Your_Neurons_ICCV_2015_paper.html | https://openaccess.thecvf.com/content_iccv_2015/papers/Malinowski_Ask_Your_Neurons_ICCV_2015_paper.pdf | null | 1505.01121 | title_snapshot | @InProceedings{Malinowski_2015_ICCV,author = {Malinowski, Mateusz and Rohrbach, Marcus and Fritz, Mario},title = {Ask Your Neurons: A Neural-Based Approach to Answering Questions About Images},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2015}} | We address a question answering task on real-world images that is set up as a Visual Turing Test. By combining latest advances in image representation and natural language processing, we propose Neural-Image-QA, an end-to-end formulation to this problem for which all parts are trained jointly. In contrast to previous e... | [
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1 | Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing | [
"Hamid Izadinia",
"Fereshteh Sadeghi",
"Santosh K. Divvala",
"Hannaneh Hajishirzi",
"Yejin Choi",
"Ali Farhadi"
] | https://openaccess.thecvf.com/content_iccv_2015/html/Izadinia_Segment-Phrase_Table_for_ICCV_2015_paper.html | https://openaccess.thecvf.com/content_iccv_2015/papers/Izadinia_Segment-Phrase_Table_for_ICCV_2015_paper.pdf | null | 1509.08075 | title_snapshot | @InProceedings{Izadinia_2015_ICCV,author = {Izadinia, Hamid and Sadeghi, Fereshteh and Divvala, Santosh K. and Hajishirzi, Hannaneh and Choi, Yejin and Farhadi, Ali},title = {Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing},booktitle = {Proceedings of the IEEE International Conference... | We introduce Segment-Phrase Table (SPT), a large collection of bijective associations between textual phrases and their corresponding segmentations. Leveraging recent progress in object recognition and natural language semantics, we show how we can successfully build a high-quality segment-phrase table using minimal hu... | [
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2 | Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books | [
"Yukun Zhu",
"Ryan Kiros",
"Rich Zemel",
"Ruslan Salakhutdinov",
"Raquel Urtasun",
"Antonio Torralba",
"Sanja Fidler"
] | https://openaccess.thecvf.com/content_iccv_2015/html/Zhu_Aligning_Books_and_ICCV_2015_paper.html | https://openaccess.thecvf.com/content_iccv_2015/papers/Zhu_Aligning_Books_and_ICCV_2015_paper.pdf | null | 1506.06724 | title_snapshot | @InProceedings{Zhu_2015_ICCV,author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja},title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books},booktitle = {Proceedings of the IEEE I... | Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story. This paper aims to align books to their movie releases in order to provide rich descriptive explanat... | [
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3 | Learning Query and Image Similarities With Ranking Canonical Correlation Analysis | [
"Ting Yao",
"Tao Mei",
"Chong-Wah Ngo"
] | https://openaccess.thecvf.com/content_iccv_2015/html/Yao_Learning_Query_and_ICCV_2015_paper.html | https://openaccess.thecvf.com/content_iccv_2015/papers/Yao_Learning_Query_and_ICCV_2015_paper.pdf | null | null | null | @InProceedings{Yao_2015_ICCV,author = {Yao, Ting and Mei, Tao and Ngo, Chong-Wah},title = {Learning Query and Image Similarities With Ranking Canonical Correlation Analysis},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2015}} | One of the fundamental problems in image search is to learn the ranking functions, i.e., similarity between the query and image. The research on this topic has evolved through two paradigms: feature-based vector model and image ranker learning. The former relies on the image surrounding texts, while the latter learns a... | [
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4 | Learning to See by Moving | [
"Pulkit Agrawal",
"Joao Carreira",
"Jitendra Malik"
] | https://openaccess.thecvf.com/content_iccv_2015/html/Agrawal_Learning_to_See_ICCV_2015_paper.html | https://openaccess.thecvf.com/content_iccv_2015/papers/Agrawal_Learning_to_See_ICCV_2015_paper.pdf | null | 1505.01596 | title_snapshot | @InProceedings{Agrawal_2015_ICCV,author = {Agrawal, Pulkit and Carreira, Joao and Malik, Jitendra},title = {Learning to See by Moving},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2015}} | The current dominant paradigm for feature learning in computer vision relies on training neural networks for the task of object recognition using millions of hand labelled images. Is it also possible to learn features for a diverse set of visual tasks using any other form of supervision? In biology, living organisms de... | [
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5 | Object Detection Using Generalization and Efficiency Balanced Co-Occurrence Features | [
"Haoyu Ren",
"Ze-Nian Li"
] | https://openaccess.thecvf.com/content_iccv_2015/html/Ren_Object_Detection_Using_ICCV_2015_paper.html | https://openaccess.thecvf.com/content_iccv_2015/papers/Ren_Object_Detection_Using_ICCV_2015_paper.pdf | null | null | null | @InProceedings{Ren_2015_ICCV,author = {Ren, Haoyu and Li, Ze-Nian},title = {Object Detection Using Generalization and Efficiency Balanced Co-Occurrence Features},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2015}} | In this paper, we propose a high-accuracy object detector based on co-occurrence features. Firstly, we introduce three kinds of local co-occurrence features constructed by the traditional Haar, LBP, and HOG respectively. Then the boosted detectors are learned, where each weak classifier corresponds to a local image reg... | [
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6 | Mining And-Or Graphs for Graph Matching and Object Discovery | [
"Quanshi Zhang",
"Ying Nian Wu",
"Song-Chun Zhu"
] | https://openaccess.thecvf.com/content_iccv_2015/html/Zhang_Mining_And-Or_Graphs_ICCV_2015_paper.html | https://openaccess.thecvf.com/content_iccv_2015/papers/Zhang_Mining_And-Or_Graphs_ICCV_2015_paper.pdf | null | null | null | @InProceedings{Zhang_2015_ICCV,author = {Zhang, Quanshi and Wu, Ying Nian and Zhu, Song-Chun},title = {Mining And-Or Graphs for Graph Matching and Object Discovery},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2015}} | This paper reformulates the theory of graph mining on the technical basis of graph matching, and extends its scope of applications to computer vision. Given a set of attributed relational graphs (ARGs), we propose to use a hierarchical And-Or Graph (AoG) to model the pattern of maximal-size common subgraphs embedded in... | [
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7 | Pose Induction for Novel Object Categories | [
"Shubham Tulsiani",
"Joao Carreira",
"Jitendra Malik"
] | https://openaccess.thecvf.com/content_iccv_2015/html/Tulsiani_Pose_Induction_for_ICCV_2015_paper.html | https://openaccess.thecvf.com/content_iccv_2015/papers/Tulsiani_Pose_Induction_for_ICCV_2015_paper.pdf | null | 1505.00066 | title_snapshot | @InProceedings{Tulsiani_2015_ICCV,author = {Tulsiani, Shubham and Carreira, Joao and Malik, Jitendra},title = {Pose Induction for Novel Object Categories},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2015}} | We address the task of predicting pose for objects of unannotated object categories from a small seed set of annotated object classes. We present a generalized classifier that can reliably induce pose given a single instance of a novel category. In case of availability of a large collection of novel instances, our appr... | [
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8 | Dynamic Texture Recognition via Orthogonal Tensor Dictionary Learning | [
"Yuhui Quan",
"Yan Huang",
"Hui Ji"
] | https://openaccess.thecvf.com/content_iccv_2015/html/Quan_Dynamic_Texture_Recognition_ICCV_2015_paper.html | https://openaccess.thecvf.com/content_iccv_2015/papers/Quan_Dynamic_Texture_Recognition_ICCV_2015_paper.pdf | null | null | null | @InProceedings{Quan_2015_ICCV,author = {Quan, Yuhui and Huang, Yan and Ji, Hui},title = {Dynamic Texture Recognition via Orthogonal Tensor Dictionary Learning},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2015}} | Dynamic textures (DTs) are video sequences with stationary properties, which exhibit repetitive patterns over space and time. This paper aims at investigating the sparse coding based approach to characterizing local DT patterns for recognition. Owing to the high dimensionality of DT sequences, existing dictionary learn... | [
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9 | Convolutional Channel Features | [
"Bin Yang",
"Junjie Yan",
"Zhen Lei",
"Stan Z. Li"
] | https://openaccess.thecvf.com/content_iccv_2015/html/Yang_Convolutional_Channel_Features_ICCV_2015_paper.html | https://openaccess.thecvf.com/content_iccv_2015/papers/Yang_Convolutional_Channel_Features_ICCV_2015_paper.pdf | null | 1504.07339 | title_snapshot | @InProceedings{Yang_2015_ICCV,author = {Yang, Bin and Yan, Junjie and Lei, Zhen and Li, Stan Z.},title = {Convolutional Channel Features},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2015}} | Deep learning methods are powerful tools but often suffer from expensive computation and limited flexibility. An alternative is to combine light-weight models with deep representations. As successful cases exist in several visual problems, a unified framework is absent. In this paper, we revisit two widely used approac... | [
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10 | Local Convolutional Features With Unsupervised Training for Image Retrieval | [
"Mattis Paulin",
"Matthijs Douze",
"Zaid Harchaoui",
"Julien Mairal",
"Florent Perronin",
"Cordelia Schmid"
] | https://openaccess.thecvf.com/content_iccv_2015/html/Paulin_Local_Convolutional_Features_ICCV_2015_paper.html | https://openaccess.thecvf.com/content_iccv_2015/papers/Paulin_Local_Convolutional_Features_ICCV_2015_paper.pdf | null | null | null | @InProceedings{Paulin_2015_ICCV,author = {Paulin, Mattis and Douze, Matthijs and Harchaoui, Zaid and Mairal, Julien and Perronin, Florent and Schmid, Cordelia},title = {Local Convolutional Features With Unsupervised Training for Image Retrieval},booktitle = {Proceedings of the IEEE International Conference on Computer ... | Patch-level descriptors underlie several important computer vision tasks, such as stereo-matching or content-based image retrieval. We introduce a deep convolutional architecture that yields patch-level descriptors, as an alternative to the popular SIFT descriptor for image retrieval. The proposed family of descri... | [
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