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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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11 | Zero-Annotation Object Detection with Web Knowledge Transfer | [
"Qingyi Tao",
"Hao Yang",
"Jianfei Cai"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Qingyi_Tao_Zero-Annotation_Object_Detection_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Qingyi_Tao_Zero-Annotation_Object_Detection_ECCV_2018_paper.pdf | null | null | 1711.05954 | title_snapshot | Object detection is one of the major problems in computer vision, and has been extensively studied. Most of the existing detection works rely on labor-intensive supervision, such as ground truth bounding boxes of objects or at least image-level annotations. On the contrary, we propose an object detection method that do... | [
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12 | Fast Light Field Reconstruction With Deep Coarse-To-Fine Modeling of Spatial-Angular Clues | [
"Henry Wing Fung Yeung",
"Junhui Hou",
"Jie Chen",
"Yuk Ying Chung",
"Xiaoming Chen"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Henry_W._F._Yeung_Fast_Light_Field_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Henry_W._F._Yeung_Fast_Light_Field_ECCV_2018_paper.pdf | null | null | null | null | Densely-sampled light fields (LFs) are beneficial to many applications such as depth inference and post-capture refocusing. However, it is costly and challenging to capture them. In this paper, we propose a learning based algorithm to reconstruct a densely-sampled LF fast and accurately from a sparsely-sampled LF in on... | [
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13 | AGIL: Learning Attention from Human for Visuomotor Tasks | [
"Ruohan Zhang",
"Zhuode Liu",
"Luxin Zhang",
"Jake A. Whritner",
"Karl S. Muller",
"Mary M. Hayhoe",
"Dana H. Ballard"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ruohan_Zhang_AGIL_Learning_Attention_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ruohan_Zhang_AGIL_Learning_Attention_ECCV_2018_paper.pdf | null | null | 1806.03960 | title_snapshot | When intelligent agents learn visuomotor behaviors from human demonstrations, they may benefit from knowing where the human is allocating visual attention, which can be inferred from their gaze. A wealth of information regarding intelligent decision making is conveyed by human gaze allocation; hence, exploiting such in... | [
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14 | Physical Primitive Decomposition | [
"Zhijian Liu",
"William T. Freeman",
"Joshua B. Tenenbaum",
"Jiajun Wu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zhijian_Liu_Physical_Primitive_Decomposition_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zhijian_Liu_Physical_Primitive_Decomposition_ECCV_2018_paper.pdf | null | null | 1809.05070 | title_snapshot | Objects are made of parts, each with distinct geometry, physics, functionality, and affordances. Developing such a distributed, physical, interpretable representation of objects will facilitate intelligent agents to better explore and interact with the world. In this paper, we study physical primitive decomposition---u... | [
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15 | Deep Expander Networks: Efficient Deep Networks from Graph Theory | [
"Ameya Prabhu",
"Girish Varma",
"Anoop Namboodiri"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ameya_Prabhu_Deep_Expander_Networks_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ameya_Prabhu_Deep_Expander_Networks_ECCV_2018_paper.pdf | null | null | 1711.08757 | title_snapshot | Efficient CNN designs like ResNets and DenseNet were proposed to improve accuracy vs efficiency trade-offs. They essentially increased the connectivity, allowing efficient information flow across layers. Inspired by these techniques, we propose to model connections between filters of a CNN using graphs which are simult... | [
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16 | Real-Time MDNet | [
"Ilchae Jung",
"Jeany Son",
"Mooyeol Baek",
"Bohyung Han"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ilchae_Jung_Real-Time_MDNet_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ilchae_Jung_Real-Time_MDNet_ECCV_2018_paper.pdf | null | null | 1808.08834 | title_snapshot | We present a fast and accurate visual tracking algorithm based on the multi-domain convolutional neural network (MDNet). The proposed approach accelerates feature extraction procedure and learns more discriminative models for instance classification; it enhances representation quality of target and background by mainta... | [
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17 | The Mutex Watershed: Efficient, Parameter-Free Image Partitioning | [
"Steffen Wolf",
"Constantin Pape",
"Alberto Bailoni",
"Nasim Rahaman",
"Anna Kreshuk",
"Ullrich Kothe",
"FredA. Hamprecht"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Steffen_Wolf_The_Mutex_Watershed_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Steffen_Wolf_The_Mutex_Watershed_ECCV_2018_paper.pdf | null | null | null | null | Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments; or equivalently, the task of detecting closed contours in an image. Most prior work either requires seeds, one per segment; or a threshold; or formulates the task as an NP-hard signed graph partitioning pr... | [
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18 | MVSNet: Depth Inference for Unstructured Multi-view Stereo | [
"Yao Yao",
"Zixin Luo",
"Shiwei Li",
"Tian Fang",
"Long Quan"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yao_Yao_MVSNet_Depth_Inference_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yao_Yao_MVSNet_Depth_Inference_ECCV_2018_paper.pdf | null | null | 1804.02505 | title_snapshot | We present an end-to-end deep learning architecture for depth map inference from multi-view images. In the network, we first extract deep visual image features, and then build the 3D cost volume upon the reference camera frustum via the differentiable homography warping. Next, we apply 3D convolutions to regularize and... | [
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19 | Audio-Visual Event Localization in Unconstrained Videos | [
"Yapeng Tian",
"Jing Shi",
"Bochen Li",
"Zhiyao Duan",
"Chenliang Xu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yapeng_Tian_Audio-Visual_Event_Localization_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yapeng_Tian_Audio-Visual_Event_Localization_ECCV_2018_paper.pdf | null | null | 1803.08842 | title_snapshot | In this paper, we introduce a novel problem of audio-visual event localization in unconstrained videos. We define an audio-visual event as an event that is both visible and audible in a video segment. We collect an Audio-Visual Event (AVE) dataset to systemically investigate three temporal localization tasks: supervised... | [
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20 | Attend and Rectify: a gated attention mechanism for fine-grained recovery | [
"Pau Rodriguez",
"Josep M. Gonfaus",
"Guillem Cucurull",
"F. XavierRoca",
"Jordi Gonzalez"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Pau_Rodriguez_Lopez_Attend_and_Rectify_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Pau_Rodriguez_Lopez_Attend_and_Rectify_ECCV_2018_paper.pdf | null | null | 1807.07320 | title_snapshot | We propose a novel attention mechanism to enhance Convolutional Neural Networks for fine-grained recognition. It learns to attend to lower-level feature activations without requiring part annotations and uses these activations to update and rectify the output likelihood distribution. In contrast to other approaches, th... | [
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21 | PyramidBox: A Context-assisted Single Shot Face Detector | [
"Xu Tang",
"Daniel K. Du",
"Zeqiang He",
"Jingtuo Liu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xu_Tang_PyramidBox_A_Context-assisted_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xu_Tang_PyramidBox_A_Context-assisted_ECCV_2018_paper.pdf | null | null | 1803.07737 | title_snapshot | Face detection has been well studied for many years and one of remaining challenges is to detect small, blurred and partially occluded faces in uncontrolled environment. This paper proposes a novel context-assisted single shot face detector, named emph{PyramidBox} to handle the hard face detection problem. Observing th... | [
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22 | RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments | [
"Tobias Fischer",
"Hyung Jin Chang",
"Yiannis Demiris"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Tobias_Fischer_RT-GENE_Real-Time_Eye_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Tobias_Fischer_RT-GENE_Real-Time_Eye_ECCV_2018_paper.pdf | null | null | null | null | In this work, we consider the problem of robust gaze estimation in natural environments. Large camera-to-subject distances and high variations in head pose and eye gaze angles are common in such environments. This leads to two main shortfalls in state-of-the-art methods for gaze estimation: hindered ground truth gaze a... | [
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23 | Contemplating Visual Emotions: Understanding and Overcoming Dataset Bias | [
"Rameswar Panda",
"Jianming Zhang",
"Haoxiang Li",
"Joon-Young Lee",
"Xin Lu",
"Amit K. Roy-Chowdhury"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Rameswar_Panda_Contemplating_Visual_Emotions_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Rameswar_Panda_Contemplating_Visual_Emotions_ECCV_2018_paper.pdf | null | null | 1808.02212 | title_snapshot | While machine learning approaches to visual emotion recognition offer great promise, current methods consider training and testing models on small scale datasets covering limited visual emotion concepts. Our analysis identifies an important but long overlooked issue of existing visual emotion benchmarks in the form of ... | [
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24 | Highly-Economized Multi-View Binary Compression for Scalable Image Clustering | [
"Zheng Zhang",
"Li Liu",
"Jie Qin",
"Fan Zhu",
"Fumin Shen",
"Yong Xu",
"Ling Shao",
"Heng Tao Shen"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zheng_Zhang_Highly-Economized_Multi-View_Binary_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zheng_Zhang_Highly-Economized_Multi-View_Binary_ECCV_2018_paper.pdf | null | null | 1809.05992 | title_snapshot | How to economically cluster large-scale multi-view images is a long-standing problem in computer vision. To tackle this challenge, this paper introduces a novel approach named Highly-economized Scalable Image Clustering (HSIC) that radically surpasses conventional image clustering methods via binary compression. We int... | [
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25 | Deep Kalman Filtering Network for Video Compression Artifact Reduction | [
"Guo Lu",
"Wanli Ouyang",
"Dong Xu",
"Xiaoyun Zhang",
"Zhiyong Gao",
"Ming-Ting Sun"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Guo_Lu_Deep_Kalman_Filtering_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Guo_Lu_Deep_Kalman_Filtering_ECCV_2018_paper.pdf | null | null | null | null | When lossy video compression algorithms are applied, compression artifacts often appear in videos, making decoded videos unpleasant for human visual systems. In this paper, we model the video artifact reduction task as a Kalman filtering procedure and restore decoded frames through a deep Kalman filtering network. Diff... | [
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26 | DeepGUM: Learning Deep Robust Regression with a Gaussian-Uniform Mixture Model | [
"Stephane Lathuiliere",
"Pablo Mesejo",
"Xavier Alameda-Pineda",
"Radu Horaud"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Stephane_Lathuiliere_DeepGUM_Learning_Deep_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Stephane_Lathuiliere_DeepGUM_Learning_Deep_ECCV_2018_paper.pdf | null | null | 1808.09211 | title_snapshot | In this paper we address the problem of how to robustly train a ConvNet for regression, or deep robust regression. Traditionally, deep regression employ the L2 loss function, known to be sensitive to outliers, i.e. samples that either lie at an abnormal distance away from the majority of the training samples, or that c... | [
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27 | ISNN: Impact Sound Neural Network for Audio-Visual Object Classification | [
"Auston Sterling",
"Justin Wilson",
"Sam Lowe",
"Ming C. Lin"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Auston_Sterling_ISNN_-_Impact_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Auston_Sterling_ISNN_-_Impact_ECCV_2018_paper.pdf | null | null | null | null | 3D object geometry reconstruction remains a challenge when working with transparent, occluded, or highly reflective surfaces. While recent methods classify shape features using raw audio, we present a multimodal neural network optimized for estimating an object's geometry and material. Our networks use spectrograms of ... | [
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28 | Deep Cross-modality Adaptation via Semantics Preserving Adversarial Learning for Sketch-based 3D Shape Retrieval | [
"Jiaxin Chen",
"Yi Fang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Jiaxin_Chen_Deep_Cross-modality_Adaptation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Jiaxin_Chen_Deep_Cross-modality_Adaptation_ECCV_2018_paper.pdf | null | null | 1807.01806 | title_snapshot | Due to the large cross-modality discrepancy between 2D sketches and 3D shapes, retrieving 3D shapes by sketches is a significantly challenging task. To address this problem, we propose a novel framework to learn a discriminative deep cross-modality adaptation model in this paper. Specifically, we first separately adopt... | [
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29 | Learning to Blend Photos | [
"Wei-Chih Hung",
"Jianming Zhang",
"Xiaohui Shen",
"Zhe Lin",
"Joon-Young Lee",
"Ming-Hsuan Yang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Wei-Chih_Hung_Learning_to_Blend_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Wei-Chih_Hung_Learning_to_Blend_ECCV_2018_paper.pdf | null | null | null | null | Photo blending is a common technique to create aesthetically pleasing artworks by combining multiple photos. However, the process of photo blending is usually time-consuming, and care must be taken in the process of blending, filtering, positioning, and masking each of the source photos. To make photo blending accessib... | [
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30 | Second-order Democratic Aggregation | [
"Tsung-Yu Lin",
"Subhransu Maji",
"Piotr Koniusz"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Tsung-Yu_Lin_Second-order_Democratic_Aggregation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Tsung-Yu_Lin_Second-order_Democratic_Aggregation_ECCV_2018_paper.pdf | null | null | 1808.07503 | title_snapshot | Aggregated second-order features extracted from deep convolutional networks have been shown to be effective for texture generation, fine-grained recognition, material classification, and scene understanding. In this paper we study a class of orderless aggregation functions designed to minimize emph{interference} or equ... | [
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31 | Recurrent Fusion Network for Image captioning | [
"Wenhao Jiang",
"Lin Ma",
"Yu-Gang Jiang",
"Wei Liu",
"Tong Zhang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Wenhao_Jiang_Recurrent_Fusion_Network_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Wenhao_Jiang_Recurrent_Fusion_Network_ECCV_2018_paper.pdf | null | null | 1807.09986 | title_snapshot | Recently, much advance has been made in image captioning, and an encoder-decoder framework has been adopted by all the state-of-the-art models. Under this framework, an input image is encoded by a convolutional neural network (CNN) and then translated into natural language with a recurrent neural network (RNN). The exi... | [
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32 | Grounding Visual Explanations | [
"Lisa Anne Hendricks",
"Ronghang Hu",
"Trevor Darrell",
"Zeynep Akata"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Lisa_Anne_Hendricks_Grounding_Visual_Explanations_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Lisa_Anne_Hendricks_Grounding_Visual_Explanations_ECCV_2018_paper.pdf | null | null | 1807.09685 | title_snapshot | Existing visual explanation generating agents learn to fluently justify a class prediction. However, they may mention visual attributes which reflect a strong class prior, although the evidence may not actually be in the image. This is particularly concerning as ultimately such agents fail in building trust with human ... | [
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33 | A Dataset of Flash and Ambient Illumination Pairs from the Crowd | [
"Yagiz Aksoy",
"Changil Kim",
"Petr Kellnhofer",
"Sylvain Paris",
"Mohamed Elgharib",
"Marc Pollefeys",
"Wojciech Matusik"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yagiz_Aksoy_A_Dataset_of_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yagiz_Aksoy_A_Dataset_of_ECCV_2018_paper.pdf | null | null | null | null | Illumination is a critical element of photography and is essential for many computer vision tasks. Flash light is unique in the sense that it is a widely available tool for easily manipulating the scene illumination. We present a dataset of thousands of ambient and flash illumination pairs to enable studying flash phot... | [
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34 | Deep Continuous Fusion for Multi-Sensor 3D Object Detection | [
"Ming Liang",
"Bin Yang",
"Shenlong Wang",
"Raquel Urtasun"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ming_Liang_Deep_Continuous_Fusion_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ming_Liang_Deep_Continuous_Fusion_ECCV_2018_paper.pdf | null | null | 2012.10992 | title_snapshot | In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-end learnable architecture that exploits continuous convolutions to fuse image and LIDAR feature maps at different levels of resolution. Our p... | [
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35 | BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization | [
"Yue Wu",
"Wael Abd-Almageed",
"Prem Natarajan"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Rex_Yue_Wu_BusterNet_Detecting_Copy-Move_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Rex_Yue_Wu_BusterNet_Detecting_Copy-Move_ECCV_2018_paper.pdf | null | null | null | null | We introduce a novel deep neural architecture for image copy-move forgery detection (CMFD), code-named BusterNet. Unlike previous eorts, BusterNet is a pure, end-to-end trainable, deep neural network solution. It features a two-branch architecture followed by a fu- sion module. The two branches localize potential manip... | [
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36 | Parallel Feature Pyramid Network for Object Detection | [
"Seung-Wook Kim",
"Hyong-Keun Kook",
"Jee-Young Sun",
"Mun-Cheon Kang",
"Sung-Jea Ko"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Seung-Wook_Kim_Parallel_Feature_Pyramid_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Seung-Wook_Kim_Parallel_Feature_Pyramid_ECCV_2018_paper.pdf | null | null | null | null | Recently developed object detectors employ a convolutional neural network (CNN) by gradually increasing the number of feature layers with a pyramidal shape instead of using a featurized image pyramid. However, the different abstraction levels of the CNN feature layers often limit the detection performance, especially o... | [
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37 | Learning Region Features for Object Detection | [
"Jiayuan Gu",
"Han Hu",
"Liwei Wang",
"Yichen Wei",
"Jifeng Dai"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Jiayuan_Gu_Learning_Region_Features_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Jiayuan_Gu_Learning_Region_Features_ECCV_2018_paper.pdf | null | null | 1803.07066 | title_snapshot | While most steps in the modern object detection methods are learnable, the region feature extraction step remains largely hand-crafted, featured by RoI pooling methods. This work proposes a general viewpoint that unifies existing region feature extraction methods and a novel method that is end-to-end learnable. The pro... | [
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38 | AMC: AutoML for Model Compression and Acceleration on Mobile Devices | [
"Yihui He",
"Ji Lin",
"Zhijian Liu",
"Hanrui Wang",
"Li-Jia Li",
"Song Han"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yihui_He_AMC_Automated_Model_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yihui_He_AMC_Automated_Model_ECCV_2018_paper.pdf | null | null | 1802.03494 | title_snapshot | Model compression is an effective technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets. Conventional model compression techniques rely on hand-crafted features and require domain experts to explore the large design space trading off amo... | [
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39 | PSDF Fusion: Probabilistic Signed Distance Function for On-the-fly 3D Data Fusion and Scene Reconstruction | [
"Wei Dong",
"Qiuyuan Wang",
"Xin Wang",
"Hongbin Zha"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Wei_Dong_Probabilistic_Signed_Distance_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Wei_Dong_Probabilistic_Signed_Distance_ECCV_2018_paper.pdf | null | null | 1807.11034 | title_snapshot | We propose a novel 3D spatial representation for data fusion and scene reconstruction. Probabilistic Signed Distance Function (Probabilistic SDF, PSDF) is proposed to depict uncertainties in the 3D space. It is modeled by a joint distribution describing SDF value and its inlier probability, reflecting input data qualit... | [
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40 | Penalizing Top Performers: Conservative Loss for Semantic Segmentation Adaptation | [
"Xinge Zhu",
"Hui Zhou",
"Ceyuan Yang",
"Jianping Shi",
"Dahua Lin"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xinge_Zhu_Penalizing_Top_Performers_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xinge_Zhu_Penalizing_Top_Performers_ECCV_2018_paper.pdf | null | null | 1809.00903 | title_snapshot | Due to the expensive and time-consuming annotations (e.g., segmentation) for real-world images, recent works in computer vision resort to synthetic data. However, the performance on the real image often drops significantly because of the domain shift between the synthetic data and the real images. In this setting, doma... | [
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41 | Switchable Temporal Propagation Network | [
"Sifei Liu",
"Guangyu Zhong",
"Shalini De Mello",
"Jinwei Gu",
"Varun Jampani",
"Ming-Hsuan Yang",
"Jan Kautz"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Sifei_Liu_Switchable_Temporal_Propagation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Sifei_Liu_Switchable_Temporal_Propagation_ECCV_2018_paper.pdf | null | null | 1804.08758 | title_snapshot | Videos contain highly redundant information between frames. Such redundancy has been studied extensively in video compression and encoding but is less explored for more advanced video processing. In this paper, we propose a learnable unified framework for propagating a variety of visual properties of video images, incl... | [
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42 | Sampling Algebraic Varieties for Robust Camera Autocalibration | [
"Danda Pani Paudel",
"Luc Van Gool"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Danda_Pani_Paudel_Sampling_Algebraic_Varieties_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Danda_Pani_Paudel_Sampling_Algebraic_Varieties_ECCV_2018_paper.pdf | null | null | null | null | This paper addresses the problem of robustly autocalibrating a moving camera with constant intrinsics. The proposed calibration method uses the Branch-and-Bound (BnB) search paradigm to maximize the consensus of the polynomials. These polynomials are parameterized by the entries of, either the Dual Image of Absolute Co... | [
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43 | Image Reassembly Combining Deep Learning and Shortest Path Problem | [
"Marie-Morgane Paumard",
"David Picard",
"Hedi Tabia"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Marie-Morgane_Paumard_Image_Reassembly_Combining_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Marie-Morgane_Paumard_Image_Reassembly_Combining_ECCV_2018_paper.pdf | null | null | 1809.00898 | title_snapshot | This paper addresses the problem of reassembling images from disjointed fragments. More specifically, given an unordered set of fragments, we aim at reassembling one or several possibly incomplete images. The main contributions of this work are: 1) several deep neural architectures to predict the relative position of i... | [
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44 | Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out Codes | [
"Yang He",
"Bernt Schiele",
"Mario Fritz"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yang_He_Diverse_Conditional_Image_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yang_He_Diverse_Conditional_Image_ECCV_2018_paper.pdf | null | null | 1808.01121 | title_snapshot | Recent advances in Deep Learning and probabilistic modeling have let to strong improvements in generative models for images. On the one hand, GANs have contributed a highly effective adversarial learning procedure, but still suffer from stability issues. On the other hand, CVAE models provide a sound way of conditional... | [
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45 | Incremental Non-Rigid Structure-from-Motion with Unknown Focal Length | [
"Thomas Probst",
"Danda Pani Paudel",
"Ajad Chhatkuli",
"Luc Van Gool"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Thomas_Probst_Incremental_Non-Rigid_Structure-from-Motion_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Thomas_Probst_Incremental_Non-Rigid_Structure-from-Motion_ECCV_2018_paper.pdf | null | null | 1808.04181 | title_snapshot | The perspective camera and the isometric surface prior have recently gathered increased attention for Non-Rigid Structure-from-Motion (NRSfM). De- spite the recent progress, several challenges remain, particularly the computa- tional complexity and the unknown camera focal length. In this paper we present a method for ... | [
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46 | PS-FCN: A Flexible Learning Framework for Photometric Stereo | [
"Guanying Chen",
"Kai Han",
"Kwan-Yee K. Wong"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Guanying_Chen_PS-FCN_A_Flexible_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Guanying_Chen_PS-FCN_A_Flexible_ECCV_2018_paper.pdf | null | null | 1807.08696 | title_snapshot | This paper addresses the problem of photometric stereo for non-Lambertian surfaces. Existing approaches often adopt simplified reflectance models to make the problem more tractable, but this greatly hinders their applications on real-world objects. In this paper, we propose a deep fully convolutional network, called PS... | [
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47 | Instance-level Human Parsing via Part Grouping Network | [
"Ke Gong",
"Xiaodan Liang",
"Yicheng Li",
"Yimin Chen",
"Ming Yang",
"Liang Lin"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ke_Gong_Instance-level_Human_Parsing_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ke_Gong_Instance-level_Human_Parsing_ECCV_2018_paper.pdf | null | null | 1808.00157 | title_snapshot | Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing multiple instances in a single pass. Several related works all follow the ``parsing-by-detection" pipeline that heavily relies on separatel... | [
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48 | Normalized Blind Deconvolution | [
"Meiguang Jin",
"Stefan Roth",
"Paolo Favaro"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Meiguang_Jin_Normalized_Blind_Deconvolution_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Meiguang_Jin_Normalized_Blind_Deconvolution_ECCV_2018_paper.pdf | null | null | null | null | We introduce a family of novel approaches to single-image blind deconvolution, ie , the problem of recovering a sharp image and a blur kernel from a single blurry input. This problem is highly ill-posed, because infinite (image, blur) pairs produce the same blurry image. Most research effort has been devoted to the des... | [
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49 | Constrained Optimization Based Low-Rank Approximation of Deep Neural Networks | [
"Chong Li",
"C. J. Richard Shi"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Chong_Li_Constrained_Optimization_Based_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Chong_Li_Constrained_Optimization_Based_ECCV_2018_paper.pdf | null | null | null | null | We present COBLA---Constrained Optimization Based Low-rank Approximation---a systematic method of finding an optimal low-rank approximation of a trained convolutional neural network, subject to constraints in the number of multiply-accumulate (MAC) operations and the memory footprint. COBLA optimally allocates the cons... | [
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50 | Dense Pose Transfer | [
"Natalia Neverova",
"Riza Alp Guler",
"Iasonas Kokkinos"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Natalia_Neverova_Two_Stream__ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Natalia_Neverova_Two_Stream__ECCV_2018_paper.pdf | null | null | 1809.01995 | title_snapshot | In this work we integrate ideas from surface-based modeling with neural synthesis: we propose a combination of surface-based pose estimation and deep generative models that allows us to perform accurate pose transfer, i.e. synthesize a new image of a person based on a single image of that person and the image of a pose... | [
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51 | RCAA: Relational Context-Aware Agents for Person Search | [
"Xiaojun Chang",
"Po-Yao Huang",
"Yi-Dong Shen",
"Xiaodan Liang",
"Yi Yang",
"Alexander G. Hauptmann"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xiaojun_Chang_RCAA_Relational_Context-Aware_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xiaojun_Chang_RCAA_Relational_Context-Aware_ECCV_2018_paper.pdf | null | null | null | null | We aim to search for a target person from a gallery of whole scene images for which the annotations of pedestrian bounding boxes are unavailable. Previous approaches to this problem have relied on a pedestrian proposal net, which may generate redundant proposals and increase the computational burden. In this paper, we ... | [
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52 | Deep Discriminative Model for Video Classification | [
"Mohammad Tavakolian",
"Abdenour Hadid"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Mohammad_Tavakolian_Deep_Discriminative_Model_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Mohammad_Tavakolian_Deep_Discriminative_Model_ECCV_2018_paper.pdf | null | null | 1807.08259 | title_snapshot | This paper presents a new deep learning approach for video-based scene classification. We design a Heterogeneous Deep Discriminative Model (HDDM) whose parameters are initialized by performing an unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines (GRBM). In order to avoid the... | [
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53 | DeepKSPD: Learning Kernel-matrix-based SPD Representation for Fine-grained Image Recognition | [
"Melih Engin",
"Lei Wang",
"Luping Zhou",
"Xinwang Liu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Melih_Engin_DeepKSPD_Learning_Kernel-matrix-based_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Melih_Engin_DeepKSPD_Learning_Kernel-matrix-based_ECCV_2018_paper.pdf | null | null | 1711.04047 | title_snapshot | As a second-order pooled representation, covariance matrix has attracted much attention in visual recognition, and some pioneering works have recently integrated it into deep learning framework to jointly learn this matrix for fine-grained image recognition. A recent study shows that kernel matrix works considerably be... | [
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54 | Deep Pictorial Gaze Estimation | [
"Seonwook Park",
"Adrian Spurr",
"Otmar Hilliges"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Seonwook_Park_Deep_Pictorial_Gaze_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Seonwook_Park_Deep_Pictorial_Gaze_ECCV_2018_paper.pdf | null | null | 1807.10002 | title_snapshot | Estimating human gaze from natural eye images only is a challenging task. Gaze direction can be defined by the pupil- and the eyeball center where the latter is unobservable in 2D images. Hence, achieving highly accurate gaze estimates is an ill-posed problem. In this paper, we introduce a novel deep neural network arc... | [
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55 | CTAP: Complementary Temporal Action Proposal Generation | [
"Jiyang Gao",
"Kan Chen",
"Ram Nevatia"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Jiyang_Gao_CTAP_Complementary_Temporal_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Jiyang_Gao_CTAP_Complementary_Temporal_ECCV_2018_paper.pdf | null | null | 1807.04821 | title_snapshot | Temporal action proposal generation is an important task, akin to object proposals, temporal action proposals are intended to capture "clips" or temporal intervals in videos that are likely to contain an action. Previous methods can be divided to two groups: sliding window ranking and actionness score grouping. Sliding... | [
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56 | Neural Network Encapsulation | [
"Hongyang Li",
"Xiaoyang Guo",
"Bo DaiWanli Ouyang",
"Xiaogang Wang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Hongyang_Li_Neural_Network_Encapsulation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Hongyang_Li_Neural_Network_Encapsulation_ECCV_2018_paper.pdf | null | null | 1808.03749 | title_snapshot | A capsule is a collection of neurons which represents different variants of a pattern in the network. The routing scheme ensures only certain capsules who resemble lower counterparts in the higher layer should be activated. However, the computational complexity becomes an bottleneck for scaling up to larger networks, a... | [
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57 | Recovering 3D Planes from a Single Image via Convolutional Neural Networks | [
"Fengting Yang",
"Zihan Zhou"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Fengting_Yang_Recovering_3D_Planes_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Fengting_Yang_Recovering_3D_Planes_ECCV_2018_paper.pdf | null | null | null | null | In this paper, we study the problem of recovering 3D planar surfaces from a single image of man-made environment. We show that it is possible to directly train a deep neural network to achieve this goal. A novel plane structure-induced loss is proposed to train the network to simultaneously predict a plane segmentation... | [
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58 | Dist-GAN: An Improved GAN using Distance Constraints | [
"Ngoc-Trung Tran",
"Tuan-Anh Bui",
"Ngai-Man Cheung"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ngoc-Trung_Tran_Generative_Adversarial_Autoencoder_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ngoc-Trung_Tran_Generative_Adversarial_Autoencoder_ECCV_2018_paper.pdf | null | null | 1803.08887 | title_snapshot | We introduce effective training algorithms for Generative Adversarial Networks (GAN) to alleviate mode collapse and gradient vanishing. In our system, we constrain the generator by an Autoencoder (AE). We propose a formulation to consider the reconstructed samples from AE as "real'' samples for the discriminator. This ... | [
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59 | Retrospective Encoders for Video Summarization | [
"Ke Zhang",
"Kristen Grauman",
"Fei Sha"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ke_Zhang_Retrospective_Encoders_for_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ke_Zhang_Retrospective_Encoders_for_ECCV_2018_paper.pdf | null | null | null | null | Supervised learning techniques have shown substantial progress on video summarization. State-of-the-art approaches mostly regard the predicted summary and the human summary as two sequences (sets), and minimize discriminative losses that measure element-wise discrepancy. Such training objectives do not explicitly model... | [
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60 | Tracking Emerges by Colorizing Videos | [
"Carl Vondrick",
"Abhinav Shrivastava",
"Alireza Fathi",
"Sergio Guadarrama",
"Kevin Murphy"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Carl_Vondrick_Self-supervised_Tracking_by_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Carl_Vondrick_Self-supervised_Tracking_by_ECCV_2018_paper.pdf | null | null | 1806.09594 | title_snapshot | We use large amounts of unlabeled video to learn models for visual tracking without manual human supervision. We leverage the natural temporal coherency of color to create a model that learns to colorize gray-scale videos by copying colors from a reference frame. Quantitative and qualitative experiments suggest that th... | [
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61 | Task-Aware Image Downscaling | [
"Heewon Kim",
"Myungsub Choi",
"Bee Lim",
"Kyoung Mu Lee"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Heewon_Kim_Task-Aware_Image_Downscaling_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Heewon_Kim_Task-Aware_Image_Downscaling_ECCV_2018_paper.pdf | null | null | null | null | Image downscaling is one of the most classical problems in computer vision that aims to preserve the visual appearance of the original image when it is resized to a smaller scale. Upscaling a small image back to its original size is a difficult, ill-posed problem due to information loss that arises in the downscaling p... | [
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62 | Product Quantization Network for Fast Image Retrieval | [
"Tan Yu",
"Junsong Yuan",
"Chen Fang",
"Hailin Jin"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Tan_Yu_Product_Quantization_Network_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Tan_Yu_Product_Quantization_Network_ECCV_2018_paper.pdf | null | null | null | null | Product quantization has been widely used in fast image retrieval due to its effectiveness of coding high-dimensional visual features. By extending the hard assignment to soft assignment, we make it feasible to incorporate the product quantization as a layer of a convolutional neural network and propose our product qua... | [
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63 | Supervising the new with the old: learning SFM from SFM | [
"Maria Klodt",
"Andrea Vedaldi"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Maria_Klodt_Supervising_the_new_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Maria_Klodt_Supervising_the_new_ECCV_2018_paper.pdf | null | null | null | null | Recent work has demonstrated that it is possible to learn deep neural networks for monocular depth and ego-motion estimation from unlabelled video sequences, an interesting theoretical development with numerous advantages in applications. In this paper, we propose a number of improvements to these approaches. First, si... | [
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64 | Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline | [
"Zhenbo Xu",
"Wei Yang",
"Ajin Meng",
"Nanxue Lu",
"Huan Huang",
"Changchun Ying",
"Liusheng Huang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zhenbo_Xu_Towards_End-to-End_License_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zhenbo_Xu_Towards_End-to-End_License_ECCV_2018_paper.pdf | null | null | null | null | Most current license plate (LP) detection and recognition approaches are evaluated on a small and usually unrepresentative dataset since there are no publicly available large diverse datasets. In this paper, we introduce CCPD, a large and comprehensive LP dataset. All images are taken manually by workers of a roadside ... | [
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65 | Ask, Acquire, and Attack: Data-free UAP Generation using Class Impressions | [
"Konda Reddy Mopuri",
"Phani Krishna Uppala",
"R. Venkatesh Babu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Konda_Reddy_Mopuri_Ask_Acquire_and_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Konda_Reddy_Mopuri_Ask_Acquire_and_ECCV_2018_paper.pdf | null | null | 1808.01153 | title_snapshot | Deep learning models are susceptible to input specific noise, called adversarial perturbations. Moreover, there exist input-agnostic noise, called Universal Adversarial Perturbations (UAP) that can affect inference of the models over most input samples. Given a model, there exist broadly two approaches to craft UAPs: (... | [
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66 | Separating Reflection and Transmission Images in the Wild | [
"Patrick Wieschollek",
"Orazio Gallo",
"Jinwei Gu",
"Jan Kautz"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Patrick_Wieschollek_Separating_Reflection_and_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Patrick_Wieschollek_Separating_Reflection_and_ECCV_2018_paper.pdf | null | null | 1712.02099 | title_snapshot | The reflections caused by common semi-reflectors, such as glass windows, can impact the performance of computer vision algorithms. State-of-the-art methods can remove reflections on synthetic data and in controlled scenarios. However, they are based on strong assumptions and do not generalize well to real-world images.... | [
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67 | Hard-Aware Point-to-Set Deep Metric for Person Re-identification | [
"Rui Yu",
"Zhiyong Dou",
"Song Bai",
"Zhaoxiang Zhang",
"Yongchao Xu",
"Xiang Bai"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Rui_Yu_Hard-Aware_Point-to-Set_Deep_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Rui_Yu_Hard-Aware_Point-to-Set_Deep_ECCV_2018_paper.pdf | null | null | 1807.11206 | title_snapshot | Person re-identification (re-ID) is a highly challenging task due to large variations of pose, viewpoint, illumination, and occlusion. Deep metric learning provides a satisfactory solution to person re-ID by training a deep network under supervision of metric loss, e.g., triplet loss. However, the performance of deep m... | [
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68 | Cross-Modal and Hierarchical Modeling of Video and Text | [
"Bowen Zhang",
"Hexiang Hu",
"Fei Sha"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Bowen_Zhang_Cross-Modal_and_Hierarchical_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Bowen_Zhang_Cross-Modal_and_Hierarchical_ECCV_2018_paper.pdf | null | null | 1810.07212 | title_snapshot | Visual data and text data are composed of information at multiple granularities. A video can describe a complex scene that is composed of multiple clips or shots, where each depicts a semantically coherent event or action. Similarly, a paragraph may contain sentences with different topics, which collectively conveys a ... | [
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69 | StarMap for Category-Agnostic Keypoint and Viewpoint Estimation | [
"Xingyi Zhou",
"Arjun Karpur",
"Linjie Luo",
"Qixing Huang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xingyi_Zhou_Category-Agnostic_Semantic_Keypoint_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xingyi_Zhou_Category-Agnostic_Semantic_Keypoint_ECCV_2018_paper.pdf | null | null | 1803.09331 | title_snapshot | Semantic keypoints provide concise abstractions for a variety of visual understanding tasks. Existing methods define semantic keypoints separately for each category with a fixed number of semantic labels in fixed indices. As a result, this keypoint representation is in-feasible when objects have a varying number of par... | [
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70 | Improving DNN Robustness to Adversarial Attacks using Jacobian Regularization | [
"Daniel Jakubovitz",
"Raja Giryes"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Daniel_Jakubovitz_Improving_DNN_Robustness_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Daniel_Jakubovitz_Improving_DNN_Robustness_ECCV_2018_paper.pdf | null | null | 1803.08680 | title_snapshot | Deep neural networks have lately shown tremendous performance in various applications including vision and speech processing tasks. However, alongside their ability to perform these tasks with such high accuracy, it has been shown that they are highly susceptible to adversarial attacks: a small change in the input woul... | [
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71 | RelocNet: Continuous Metric Learning Relocalisation using Neural Nets | [
"Vassileios Balntas",
"Shuda Li",
"Victor Prisacariu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Vassileios_Balntas_RelocNet_Continous_Metric_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Vassileios_Balntas_RelocNet_Continous_Metric_ECCV_2018_paper.pdf | null | null | null | null | We propose a method of learning suitable convolutional representations for camera pose retrieval based on nearest neighbour matching and continuous metric learning-based feature descriptors. We introduce information from camera frusta overlaps between pairs of images to optimise our feature embedding network. Thus, the... | [
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72 | Mancs: A Multi-task Attentional Network with Curriculum Sampling for Person Re-identification | [
"Cheng Wang",
"Qian Zhang",
"Chang Huang",
"Wenyu Liu",
"Xinggang Wang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Cheng_Wang_Mancs_A_Multi-task_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Cheng_Wang_Mancs_A_Multi-task_ECCV_2018_paper.pdf | null | null | null | null | We propose a novel deep network called Mancs that solves the person re-identification problem from the following aspects: fully utilizing the attention mechanism for the person misalignment problem and properly sampling for the ranking loss to obtain more stable person representation. Technically, we contribute a novel... | [
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73 | Recurrent Tubelet Proposal and Recognition Networks for Action Detection | [
"Dong Li",
"Zhaofan Qiu",
"Qi Dai",
"Ting Yao",
"Tao Mei"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Dong_Li_Recurrent_Tubelet_Proposal_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Dong_Li_Recurrent_Tubelet_Proposal_ECCV_2018_paper.pdf | null | null | null | null | Detecting actions in videos is a challenging task as video is an information intensive media with complex variations. Existing approaches predominantly generate action proposals for each individual frame or fixed-length clip independently, while overlooking temporal context across them. Such temporal contextual relatio... | [
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74 | Estimating Depth from RGB and Sparse Sensing | [
"Zhao Chen",
"Vijay Badrinarayanan",
"Gilad Drozdov",
"Andrew Rabinovich"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zhao_Chen_Estimating_Depth_from_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zhao_Chen_Estimating_Depth_from_ECCV_2018_paper.pdf | null | null | 1804.02771 | title_snapshot | We present a deep model that can accurately produce dense depth maps given an RGB image with known depth at a very sparse set of pixels. The model works *simultaneously* for both indoor/outdoor scenes and produces state-of-the-art dense depth maps at nearly real-time speeds on both the NYUv2 and KITTI datasets. We surp... | [
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75 | Folded Recurrent Neural Networks for Future Video Prediction | [
"Marc Oliu",
"Javier Selva",
"Sergio Escalera"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Marc_Oliu_Folded_Recurrent_Neural_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Marc_Oliu_Folded_Recurrent_Neural_ECCV_2018_paper.pdf | null | null | 1712.00311 | title_snapshot | This work introduces double-mapping Gated Recurrent Units (dGRU), an extension of standard GRUs where the input is considered as a recurrent state. An extra set of logic gates is added to update the input given the output. Stacking multiple such layers results in a recurrent auto-encoder: the operators updating the out... | [
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76 | Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image | [
"Siyuan Huang",
"Siyuan Qi",
"Yixin Zhu",
"Yinxue Xiao",
"Yuanlu Xu",
"Song-Chun Zhu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Siyuan_Huang_Monocular_Scene_Parsing_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Siyuan_Huang_Monocular_Scene_Parsing_ECCV_2018_paper.pdf | null | null | 1808.02201 | title_snapshot | We propose a computational framework to jointly parse a single RGB image and reconstruct a holistic 3D configuration composed by a set of CAD models using a stochastic grammar model. Specifically, we introduce a Holistic Scene Grammar (HSG) to represent the 3D scene structure, which characterizes a joint distribution o... | [
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77 | Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation | [
"Zhenyu Zhang",
"Zhen Cui",
"Chunyan Xu",
"Zequn Jie",
"Xiang Li",
"Jian Yang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zhenyu_Zhang_Joint_Task-Recursive_Learning_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zhenyu_Zhang_Joint_Task-Recursive_Learning_ECCV_2018_paper.pdf | null | null | null | null | In this paper, we propose a novel joint Task-Recursive Learning (TRL) framework for the closing-loop semantic segmentation and monocular depth estimation tasks. TRL can recursively refine the results of both tasks through serialized task-level interactions. In order to mutually-boost for each other, we encapsulate the ... | [
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78 | A New Large Scale Dynamic Texture Dataset with Application to ConvNet Understanding | [
"Isma Hadji",
"Richard P. Wildes"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Isma_Hadji_A_New_Large_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Isma_Hadji_A_New_Large_ECCV_2018_paper.pdf | null | null | null | null | This paper introduces a new large scale dynamic texture dataset. The dataset is provided with two complementary organizations, one based on dynamics independent of spatial appearance and one based on spatial appearance independent of dynamics. With over 10,000 videos, the proposed Dynamic Texture DataBase (DTDB) is two... | [
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79 | Compositing-aware Image Search | [
"Hengshuang Zhao",
"Xiaohui Shen",
"Zhe Lin",
"Kalyan Sunkavalli",
"Brian Price",
"Jiaya Jia"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Hengshuang_Zhao_Compositing-aware_Image_Search_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Hengshuang_Zhao_Compositing-aware_Image_Search_ECCV_2018_paper.pdf | null | null | null | null | We present a new image search technique that, given a background image, returns compatible foreground objects for image compositing tasks. The compatibility of a foreground object and a background scene depends on various aspects such as semantics, surrounding context, geometry, style and color. However, existing image... | [
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80 | Extreme Network Compression via Filter Group Approximation | [
"Bo Peng",
"Wenming Tan",
"Zheyang Li",
"Shun Zhang",
"Di Xie",
"Shiliang Pu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Bo_Peng_Extreme_Network_Compression_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Bo_Peng_Extreme_Network_Compression_ECCV_2018_paper.pdf | null | null | 1807.11254 | title_snapshot | In this paper we propose a novel decomposition method based on filter group approximation, which can significantly reduce the redundancy of deep convolutional neural networks (CNNs) while maintaining the majority of feature representation. Unlike other low-rank decomposition algorithms which operate on spatial or chann... | [
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81 | Audio-Visual Scene Analysis with Self-Supervised Multisensory Features | [
"Andrew Owens",
"Alexei A. Efros"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Andrew_Owens_Audio-Visual_Scene_Analysis_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Andrew_Owens_Audio-Visual_Scene_Analysis_ECCV_2018_paper.pdf | null | null | 1804.03641 | title_snapshot | The thud of a bouncing ball, the onset of speech as lips open -- when visual and audio events occur together, it suggests that there might be a common, underlying event that produced both signals. In this paper, we argue that the visual and audio components of a video signal should be modeled jointly using a fused mult... | [
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82 | Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation | [
"Xin Wang",
"Wenhan Xiong",
"Hongmin Wang",
"William Yang Wang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xin_Wang_Look_Before_You_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xin_Wang_Look_Before_You_ECCV_2018_paper.pdf | null | null | 1803.07729 | title_snapshot | Existing research studies on vision and language grounding for robot navigation focus on improving model-free deep reinforcement learning (DRL) models in synthetic environments. However, model-free DRL models do not consider the dynamics in the real-world environments, and they often fail to generalize to new scenes. I... | [
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83 | Structure-from-Motion-Aware PatchMatch for Adaptive Optical Flow Estimation | [
"Daniel Maurer",
"Nico Marniok",
"Bastian Goldluecke",
"Andres Bruhn"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Daniel_Maurer_Structure-from-Motion-Aware_PatchMatch_for_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Daniel_Maurer_Structure-from-Motion-Aware_PatchMatch_for_ECCV_2018_paper.pdf | null | null | null | null | Many recent energy-based methods for optical flow estimation rely on a good initialization that is typically provided by some kind of feature matching. So far, however, these initial matching approaches are rather general: They do not incorporate any additional information that could help to improve the accuracy or the... | [
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84 | ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design | [
"Ningning Ma",
"Xiangyu Zhang",
"Hai-Tao Zheng",
"Jian Sun"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ningning_Light-weight_CNN_Architecture_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ningning_Light-weight_CNN_Architecture_ECCV_2018_paper.pdf | null | null | 1807.11164 | title_snapshot | Current network architecture design is mostly guided by the indirect metric of computation complexity, i.e., FLOPs. However, the direct metric, such as speed, also depends on the other factors such as memory access cost and platform characterics. Taking these factors into account, this work proposes practical guideline... | [
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85 | Attention-GAN for Object Transfiguration in Wild Images | [
"Xinyuan Chen",
"Chang Xu",
"Xiaokang Yang",
"Dacheng Tao"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xinyuan_Chen_Attention-GAN_for_Object_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xinyuan_Chen_Attention-GAN_for_Object_ECCV_2018_paper.pdf | null | null | 1803.06798 | title_snapshot | This paper studies the object transfiguration problem in wild images. The generative network in classical GANs for object transfiguration often undertakes a dual responsibility: to detect the objects of interests and to convert the object from source domain to another domain. In contrast, we decompose the generative ne... | [
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86 | Joint Representation and Truncated Inference Learning for Correlation Filter based Tracking | [
"Yingjie Yao",
"Xiaohe Wu",
"Lei Zhang",
"Shiguang Shan",
"Wangmeng Zuo"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yingjie_Yao_Joint_Representation_and_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yingjie_Yao_Joint_Representation_and_ECCV_2018_paper.pdf | null | null | 1807.11071 | title_snapshot | Correlation filter (CF) based trackers generally include two modules, i.e., feature representation and on-line model adaptation. In existing off-line deep learning models for CF trackers, the model adaptation usually is either abandoned or has closed-form solution to make it feasible to learn deep representation in an ... | [
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87 | Lambda Twist: An Accurate Fast Robust Perspective Three Point (P3P) Solver | [
"Mikael Persson",
"Klas Nordberg"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Mikael_Persson_Lambda_Twist_An_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Mikael_Persson_Lambda_Twist_An_ECCV_2018_paper.pdf | null | null | null | null | We present Lambda Twist; a novel P3P solver which is accurate, fast and robust. Current state-of-the-art P3P solvers find all roots to a quartic and discard geometrically invalid and duplicate solutions in a post-processing step. Instead of solving a quartic, the proposed P3P solver exploits the underlying elliptic equ... | [
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88 | StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction | [
"Sameh Khamis",
"Sean Fanello",
"Christoph Rhemann",
"Adarsh Kowdle",
"Julien Valentin",
"Shahram Izadi"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Sameh_Khamis_StereoNet_Guided_Hierarchical_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Sameh_Khamis_StereoNet_Guided_Hierarchical_ECCV_2018_paper.pdf | null | null | 1807.08865 | title_snapshot | This paper presents StereoNet, the first end-to-end deep architecture for real-time stereo matching that runs at 60 fps on an NVidia Titan X, producing high-quality, edge-preserved, quantization-free depth maps. A key insight of this paper is that the network achieves a sub-pixel matching precision than is a magnitude ... | [
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89 | Robust Optical Flow in Rainy Scenes | [
"Ruoteng Li",
"Robby T. Tan",
"Loong-Fah Cheong"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ruoteng_Li_Robust_Optical_Flow_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ruoteng_Li_Robust_Optical_Flow_ECCV_2018_paper.pdf | null | null | null | null | Optical flow estimation in rainy scenes is challenging due to degradation caused by rain streaks and rain accumulation, where the latter refers to the poor visibility of remote scenes due to intense rainfall. To resolve the problem, we introduce a residue channel, a single channel (gray) image that is free from rain, a... | [
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90 | Scale Aggregation Network for Accurate and Efficient Crowd Counting | [
"Xinkun Cao",
"Zhipeng Wang",
"Yanyun Zhao",
"Fei Su"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xinkun_Cao_Scale_Aggregation_Network_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xinkun_Cao_Scale_Aggregation_Network_ECCV_2018_paper.pdf | null | null | null | null | In this paper, we propose a novel encoder-decoder network, called extit{Scale Aggregation Network (SANet)}, for accurate and efficient crowd counting. The encoder extracts multi-scale features with scale aggregation modules and the decoder generates high-resolution density maps by using a set of transposed convolutions... | [
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91 | Deep Feature Factorization For Concept Discovery | [
"Edo Collins",
"Radhakrishna Achanta",
"Sabine Susstrunk"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Edo_Collins_Deep_Feature_Factorization_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Edo_Collins_Deep_Feature_Factorization_ECCV_2018_paper.pdf | null | null | 1806.10206 | title_snapshot | We propose Deep Feature Factorization (DFF), a method capable of localizing similar semantic concepts within an image or a set of images. We use DFF to gain insight into a deep convolutional neural network's learned features, where we detect hierarchical cluster structures in feature space. This is visualized as heat m... | [
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92 | Object-centered image stitching | [
"Charles Herrmann",
"Chen Wang",
"Richard Strong Bowen",
"Emil Keyder",
"Ramin Zabih"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Charles_Herrmann_Object-centered_image_stitching_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Charles_Herrmann_Object-centered_image_stitching_ECCV_2018_paper.pdf | null | null | 2011.11789 | title_snapshot | Image stitching is typically decomposed into three phases: registration, which aligns the source images with a common target image; seam finding, which determines for each target pixel the source image it should come from; and blending, which smooths transitions over the seams. As described in Szeliski’s tutorial on im... | [
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93 | A Style-Aware Content Loss for Real-time HD Style Transfer | [
"Artsiom Sanakoyeu",
"Dmytro Kotovenko",
"Sabine Lang",
"Bjorn Ommer"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Artsiom_Sanakoyeu_A_Style-aware_Content_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Artsiom_Sanakoyeu_A_Style-aware_Content_ECCV_2018_paper.pdf | null | null | 1807.10201 | title_snapshot | Recently style transfer has received a lot of attention. While much of this research has aimed at speeding up the processing, the approaches are still lacking from a principled, art historical standpoint: a style is more than just a single image or an artist, but previous work is limited to only a single instance of a ... | [
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94 | Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining | [
"Xia Li",
"Jianlong Wu",
"Zhouchen Lin",
"Hong Liu",
"Hongbin Zha"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xia_Li_Recurrent_Squeeze-and-Excitation_Context_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xia_Li_Recurrent_Squeeze-and-Excitation_Context_ECCV_2018_paper.pdf | null | null | 1807.05698 | title_snapshot | Rain streaks can severely degrade the visibility, which causes many current computer vision algorithms fail to work. So it is necessary to remove the rain from images. We propose a novel deep network architecture based on deep convolutional and recurrent neural networks for single image deraining. As contextual informa... | [
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95 | Acquisition of Localization Confidence for Accurate Object Detection | [
"Borui Jiang",
"Ruixuan Luo",
"Jiayuan Mao",
"Tete Xiao",
"Yuning Jiang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Borui_Jiang_Acquisition_of_Localization_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Borui_Jiang_Acquisition_of_Localization_ECCV_2018_paper.pdf | null | null | 1807.11590 | title_snapshot | Modern CNN-based object detectors rely on bounding box regression and non-maximum suppression to localize objects. While the probabilities for class labels naturally reflect classification confidence, localization confidence is absent. This makes properly localized bounding boxes degenerate during iterative regression ... | [
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96 | Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network | [
"Yao Feng",
"Fan Wu",
"Xiaohu Shao",
"Yanfeng Wang",
"Xi Zhou"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yao_Feng_Joint_3D_Face_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yao_Feng_Joint_3D_Face_ECCV_2018_paper.pdf | null | null | 1803.07835 | title_snapshot | We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment. To achieve this, we design a 2D representation called UV position map which records the 3D shape of a complete face in UV space, then train a simple Convolutional Neural Network to regress it from ... | [
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97 | Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground | [
"Deng-Ping Fan",
"Ming-Ming Cheng",
"Jiang-Jiang Liu",
"Shang-Hua Gao",
"Qibin Hou",
"Ali Borji"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Deng-Ping_Fan_Salient_Objects_in_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Deng-Ping_Fan_Salient_Objects_in_ECCV_2018_paper.pdf | null | null | 1803.06091 | title_snapshot | We provide a comprehensive evaluation of salient object detection (SOD) models. Our analysis identifies a serious design bias of existing SOD datasets which assumes that each image contains at least one clearly outstanding salient object in low clutter. The design bias has led to a saturated high performance for state-... | [
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98 | Multimodal Unsupervised Image-to-image Translation | [
"Xun Huang",
"Ming-Yu Liu",
"Serge Belongie",
"Jan Kautz"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xun_Huang_Multimodal_Unsupervised_Image-to-image_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xun_Huang_Multimodal_Unsupervised_Image-to-image_ECCV_2018_paper.pdf | null | null | 1804.04732 | title_snapshot | Unsupervised image-to-image translation is an important and challenging problem in computer vision. Given an image in the source domain, the goal is to learn the conditional distribution of corresponding images in the target domain, without seeing any pairs of corresponding images. While this conditional distribution i... | [
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99 | Diverse feature visualizations reveal invariances in early layers of deep neural networks | [
"Santiago A. Cadena",
"Marissa A. Weis",
"Leon A. Gatys",
"Matthias Bethge",
"Alexander S. Ecker"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Santiago_Cadena_Diverse_feature_visualizations_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Santiago_Cadena_Diverse_feature_visualizations_ECCV_2018_paper.pdf | null | null | 1807.10589 | title_snapshot | Visualizing features in deep neural networks (DNNs) can help understanding their computations. Many previous studies aimed to visualize the selectivity of individual units by finding meaningful images that maximize their activation. However, comparably little attention has been paid to visualizing to what image transfo... | [
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