ECCV
Collection
Accepted papers for ECCV (European Conference on Computer Vision), one dataset per year. • 4 items • Updated
paper_id uint32 0 775 | title stringlengths 15 146 | authors listlengths 1 16 | ecva_url stringlengths 92 137 | pdf_url stringlengths 94 139 | supp_url stringclasses 0
values | doi stringclasses 0
values | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values | abstract large_stringlengths 474 1.99k | embedding listlengths 768 768 |
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0 | Modeling Varying Camera-IMU Time Offset in Optimization-Based Visual-Inertial Odometry | [
"Yonggen Ling",
"Linchao Bao",
"Zequn Jie",
"Fengming Zhu",
"Ziyang Li",
"Shanmin Tang",
"Yongsheng Liu",
"Wei Liu",
"Tong Zhang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yonggen_Ling_Modeling_Varying_Camera-IMU_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yonggen_Ling_Modeling_Varying_Camera-IMU_ECCV_2018_paper.pdf | null | null | 1810.05456 | title_snapshot | Combining cameras and inertial measurement units (IMUs) has been proven effective in motion tracking, as these two sensing modalities offer complementary characteristics that are suitable for fusion. While most works focus on global-shutter cameras and synchronized sensor measurements, consumer-grade devices are mostly... | [
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1 | Pose Partition Networks for Multi-Person Pose Estimation | [
"Xuecheng Nie",
"Jiashi Feng",
"Junliang Xing",
"Shuicheng Yan"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xuecheng_Nie_Pose_Partition_Networks_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xuecheng_Nie_Pose_Partition_Networks_ECCV_2018_paper.pdf | null | null | null | null | This paper proposes a novel Pose Partition Network (PPN) to address the challenging multi-person pose estimation problem. The proposed PPN is favorably featured by low complexity and high accuracy of joint detection and partition. In particular, PPN performs dense regressions from global joint candidates within a speci... | [
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2 | Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition | [
"Xiaohang Zhan",
"Ziwei Liu",
"Junjie Yan",
"Dahua Lin",
"Chen Change Loy"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xiaohang_Zhan_Consensus-Driven_Propagation_in_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xiaohang_Zhan_Consensus-Driven_Propagation_in_ECCV_2018_paper.pdf | null | null | 1809.01407 | title_snapshot | Face recognition has witnessed great progresses in recent years, mainly attributed to the high-capacity model designed and the abundant labeled data collected. However, it becomes more and more prohibitive to scale up the current million-level identity annotations. In this work, we show that unlabeled face data can be ... | [
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3 | Open-World Stereo Video Matching with Deep RNN | [
"Yiran Zhong",
"Hongdong Li",
"Yuchao Dai"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yiran_Zhong_Open-World_Stereo_Video_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yiran_Zhong_Open-World_Stereo_Video_ECCV_2018_paper.pdf | null | null | 1808.03959 | title_snapshot | In this paper, we propose a novel deep Recurrent Neural network (RNN) that takes a continuous (possibly previously unseen) stereo video as input, and directly predict a depth-map without of any pre-training process. The quality and accuracy of the obtained depth-map improves over time as new stereo frames being fed in.... | [
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4 | Deep Cross-Modal Projection Learning for Image-Text Matching | [
"Ying Zhang",
"Huchuan Lu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ying_Zhang_Deep_Cross-Modal_Projection_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ying_Zhang_Deep_Cross-Modal_Projection_ECCV_2018_paper.pdf | null | null | null | null | The key point of image-text matching is how to accurately measure the similarity between visual and textual inputs. Despite the great progress of associating the deep cross-modal embeddings with the bi-directional ranking loss, developing the strategies for mining useful triplets and selecting appropriate margins remai... | [
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5 | Gray-box Adversarial Training | [
"B. S. Vivek",
"Konda Reddy Mopuri",
"R. Venkatesh Babu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Vivek_B_S_Gray_box_adversarial_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Vivek_B_S_Gray_box_adversarial_ECCV_2018_paper.pdf | null | null | 1808.01753 | title_snapshot | Adversarial samples are perturbed inputs crafted to mislead the machine learning systems. A training mechanism, called adversarial training, which presents adversarial samples along with clean samples has been introduced to learn robust models. In order to scale adversarial training for large datasets, these perturbati... | [
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6 | Multi-Class Model Fitting by Energy Minimization and Mode-Seeking | [
"Daniel Barath",
"Jiri Matas"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Daniel_Barath_Multi-Class_Model_Fitting_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Daniel_Barath_Multi-Class_Model_Fitting_ECCV_2018_paper.pdf | null | null | 1706.00827 | title_snapshot | We propose a general formulation, called Multi-X, for multi-class multi-instance model fitting - the problem of interpreting the input data as a mixture of noisy observations originating from multiple instances of multiple classes. We extend the commonly used alpha-expansion-based technique with a new move in the label... | [
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7 | MRF Optimization with Separable Convex Prior on Partially Ordered Labels | [
"Csaba Domokos",
"Frank R. Schmidt",
"Daniel Cremers"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Csaba_Domokos_MRF_Optimization_with_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Csaba_Domokos_MRF_Optimization_with_ECCV_2018_paper.pdf | null | null | null | null | Solving a multi-labeling problem with a convex penalty can be achieved in polynomial time if the label set is totally ordered. In this paper we propose a generalization to partially ordered sets. To this end, we assume that the label set is the Cartesian product of totally ordered sets and the convex prior is separable... | [
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8 | VQA-E: Explaining, Elaborating, and Enhancing Your Answers for Visual Questions | [
"Qing Li",
"Qingyi Tao",
"Shafiq Joty",
"Jianfei Cai",
"Jiebo Luo"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Qing_Li_VQA-E_Explaining_Elaborating_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Qing_Li_VQA-E_Explaining_Elaborating_ECCV_2018_paper.pdf | null | null | 1803.07464 | title_snapshot | Most existing works in visual question answering (VQA) are dedicated to improving the accuracy of predicted answers, while disregarding the explanations. We argue that the explanation for an answer is of the same or even more importance compared with the answer itself, since it makes the question and answering process ... | [
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9 | Context Refinement for Object Detection | [
"Zhe Chen",
"Shaoli Huang",
"Dacheng Tao"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zhe_Chen_Context_Refinement_for_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zhe_Chen_Context_Refinement_for_ECCV_2018_paper.pdf | null | null | null | null | Current two-stage object detectors, which consists of a region proposal stage and a refinement stage, may produce unreliable results due to ill-localized proposed regions. To address this problem, we propose a context refinement algorithm that explores rich contextual information to better refine each proposed region. ... | [
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10 | Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network | [
"Xinjing Cheng",
"Peng Wang",
"Ruigang Yang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xinjing_Cheng_Depth_Estimation_via_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xinjing_Cheng_Depth_Estimation_via_ECCV_2018_paper.pdf | null | null | 1808.00150 | title_snapshot | Depth estimation from a single image is a fundamental problem in computer vision. In this paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) to learn the affinity matrix for depth prediction. Specifically, we adopt an efficient linear propagation model, where the propagation is pe... | [
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