【1】 Supervised and Unsupervised Detections for Multiple Object Tracking in Traffic Scenes: A Comparative Study 交通场景中多目标跟踪的有监督和无监督检测的比较研究 作者:Hui-Lee Ooi, Nicolas Saunier 备注:Accepted for ICIAR 2020 链接:https://arxiv.org/abs/2003.13644
【2】 Squeezed Deep 6DoF Object Detection Using Knowledge Distillation 利用知识精馏的压缩深6DoF目标检测 作者:Heitor Felix, Cleber Zanchettin 备注:This paper was accepted by IJCNN 2020 and will have few changes from the version that will be published 链接:https://arxiv.org/abs/2003.13586
【3】 SiTGRU: Single-Tunnelled Gated Recurrent Unit for Abnormality Detection SiTGRU:用于异常检测的单隧道门控循环单元 作者:Habtamu Fanta, Lizhuang Ma 链接:https://arxiv.org/abs/2003.13528
【4】 A Comparison of Data Augmentation Techniques in Training Deep Neural Networks for Satellite Image Classification 用于卫星图像分类的深度神经网络训练中数据增强技术的比较 作者:Mohamed Abdelhack 链接:https://arxiv.org/abs/2003.13502
【5】 Computer Aided Detection for Pulmonary Embolism Challenge (CAD-PE) 肺栓塞挑战的计算机辅助检测(CAD-PE) 作者:Germán González, Maria J. Ledesma-Carbayo 链接:https://arxiv.org/abs/2003.13440
【6】 Learning Memory-guided Normality for Anomaly Detection 用于异常检测的学习记忆引导的正规性 作者:Hyunjong Park, Bumsub Ham 备注:Accepted to CVPR 2020 链接:https://arxiv.org/abs/2003.13228
【7】 Cross-Domain Document Object Detection: Benchmark Suite and Method 跨域文档对象检测:Benchmark Suite和Method 作者:Kai Li, Yun Fu 备注:To appear in CVPR 2020 链接:https://arxiv.org/abs/2003.13197
【8】 Detection of 3D Bounding Boxes of Vehicles Using Perspective Transformation for Accurate Speed Measurement 基于透视变换精确测速的车辆三维包围盒检测 作者:Viktor Kocur, Milan Ftáčnik 链接:https://arxiv.org/abs/2003.13137
【9】 Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification Attentive CutMix:一种用于深度学习图像分类的增强数据增强方法 作者:Devesh Walawalkar, Marios Savvides 备注:Accepted as conference paper in ICASSP 2020 链接:https://arxiv.org/abs/2003.13048
【10】 Noise Modeling, Synthesis and Classification for Generic Object Anti-Spoofing 通用对象防欺骗的噪声建模、合成和分类 作者:Joel Stehouwer, Xiaoming Liu 备注:In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020 链接:https://arxiv.org/abs/2003.13043
【11】 Adaptive Object Detection with Dual Multi-Label Prediction 基于双多标签预测的自适应目标检测 作者:Zhen Zhao, Jieping Ye 链接:https://arxiv.org/abs/2003.12943
【12】 Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection 利用边缘检测的长方体物体精细化平面分割 作者:Alexander Naumann, Kai Furmans 链接:https://arxiv.org/abs/2003.12870
【13】 OCmst: One-class Novelty Detection using Convolutional Neural Network and Minimum Spanning Trees OCmst:基于卷积神经网络和最小生成树的一类新颖性检测 作者:Riccardo La Grassa, Nicola Landro 链接:https://arxiv.org/abs/2003.13524
【1】 Vox2Vox: 3D-GAN for Brain Tumour Segmentation Vox2Vox:3D-GaN用于脑肿瘤分割 作者:Marco Domenico Cirillo, Anders Eklund 链接:https://arxiv.org/abs/2003.13653
【2】 Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks 使用金字塔占有率网络从图像预测语义地图表示 作者:Thomas Roddick, Roberto Cipolla 链接:https://arxiv.org/abs/2003.13402
【3】 TapLab: A Fast Framework for Semantic Video Segmentation Tapping into Compressed-Domain Knowledge TapLab:一种利用压缩域知识的快速语义视频分割框架 作者:Junyi Feng, Haibin Ling 链接:https://arxiv.org/abs/2003.13260
【4】 Memory Aggregation Networks for Efficient Interactive Video Object Segmentation 用于高效交互式视频对象分割的存储器聚合网络 作者:Jiaxu Miao, Yi Yang 备注:Accepted to CVPR 2020. 10 pages, 9 figures 链接:https://arxiv.org/abs/2003.13246
【5】 Learning a Weakly-Supervised Video Actor-Action Segmentation Model with a Wise Selection 具有明智选择的弱监督视频演员-动作分割模型的学习 作者:Jie Chen, Chenliang Xu 备注:11 pages, 8 figures, cvpr-2020 supplementary video: this https URL 链接:https://arxiv.org/abs/2003.13141
【6】 Defect segmentation: Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network 缺陷分割:使用卷积神经网络将隧道衬砌内部缺陷与探地雷达数据映射 作者:Senlin Yang, Qingmei Sui 链接:https://arxiv.org/abs/2003.13120
【7】 Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds 点云上弱监督三维语义分割的多路径区域挖掘 作者:Jiacheng Wei, Lihua Xie 备注:Accepted by CVPR2020 链接:https://arxiv.org/abs/2003.13035
【1】 NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search NPENAS:神经结构搜索的神经预测器引导进化 作者:Chen Wei, Jimin Liang 链接:https://arxiv.org/abs/2003.12857
【1】 Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark Estimation 基于注意恢复和界标估计迭代协作的深脸超分辨率 作者:Cheng Ma, Jie Zhou 备注:Accepted to CVPR 2020 链接:https://arxiv.org/abs/2003.13063
【2】 Realistic Face Reenactment via Self-Supervised Disentangling of Identity and Pose 通过自我监督解开身份和姿势再现逼真的人脸 作者:Xianfang Zeng, Yong Liu 链接:https://arxiv.org/abs/2003.12957
【3】 One-Shot Domain Adaptation For Face Generation 一次区域自适应人脸生成算法 作者:Chao Yang, Ser-Nam Lim 备注:Accepted to CVPR 2020 链接:https://arxiv.org/abs/2003.12869
【1】 Improved Gradient based Adversarial Attacks for Quantized Networks 一种改进的基于梯度的量化网络对抗攻击 作者:Kartik Gupta, Thalaiyasingam Ajanthan 链接:https://arxiv.org/abs/2003.13511
【2】 Adversarial Feature Hallucination Networks for Few-Shot Learning 面向少发学习的对抗性特征幻觉网络 作者:Kai Li, Yun Fu 备注:To appear in CVPR 2020 链接:https://arxiv.org/abs/2003.13193
【3】 Gradually Vanishing Bridge for Adversarial Domain Adaptation 逐渐消失的对抗性领域适应之桥 作者:Shuhao Cui, Qi Tian 备注:CVPR2020 链接:https://arxiv.org/abs/2003.13183
【4】 Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning 对抗鲁棒性:从自我监督预训练到微调 作者:Tianlong Chen, Zhangyang Wang 备注:CVPR 2020 链接:https://arxiv.org/abs/2003.12862
【5】 Adversarial Imitation Attack 对抗性模仿攻击 作者:Mingyi Zhou, Ce Zhu 链接:https://arxiv.org/abs/2003.12760
【6】 DaST: Data-free Substitute Training for Adversarial Attacks DAST:对抗攻击的无数据替代训练 作者:Mingyi Zhou, Ce Zhu 备注:Accepted by CVPR2020 链接:https://arxiv.org/abs/2003.12703
【1】 Speech2Action: Cross-modal Supervision for Action Recognition Speech2Action:用于动作识别的跨模式监督 作者:Arsha Nagrani, Andrew Zisserman 备注:Accepted to CVPR 2020 链接:https://arxiv.org/abs/2003.13594
【2】 Context Based Emotion Recognition using EMOTIC Dataset 使用EMOTIC数据集的基于上下文的情感识别 作者:Ronak Kosti, Agata Lapedriza 链接:https://arxiv.org/abs/2003.13401
【3】 MetaFuse: A Pre-trained Fusion Model for Human Pose Estimation MetaFuse:一种用于人体姿态估计的预训练融合模型 作者:Rongchang Xie, Yizhou Wang 备注:Accepted to CVPR2020 链接:https://arxiv.org/abs/2003.13239
【4】 Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation 类比学习:无监督光流估计变换的可靠监督 作者:Liang Liu, Feiyue Huang 备注:Accepted to CVPR 2020 链接:https://arxiv.org/abs/2003.13045
【5】 AutoTrack: Towards High-Performance Visual Tracking for UAV with Automatic Spatio-Temporal Regularization 自动跟踪:实现自动时空正则化的无人机高性能视觉跟踪 作者:Yiming Li, Geng Lu 备注:2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 链接:https://arxiv.org/abs/2003.12949
【1】 Domain-aware Visual Bias Eliminating for Generalized Zero-Shot Learning 用于广义零发射学习的领域感知视觉偏差消除 作者:Shaobo Min, Yongdong Zhang 备注:Accepted by CVPR2020 链接:https://arxiv.org/abs/2003.13261
【2】 Learning to Learn Single Domain Generalization 学会学习单域概括 作者:Fengchun Qiao, Xi Peng 备注:In CVPR 2020 (13 pages including supplementary material). The source code and pre-trained models are publicly available at: this https URL 链接:https://arxiv.org/abs/2003.13216
【3】 Diagnosis of Breast Cancer using Hybrid Transfer Learning 混合转移学习在乳腺癌诊断中的应用 作者:Subrato Bharati, Prajoy Podder 链接:https://arxiv.org/abs/2003.13503
【4】 Mutual Learning Network for Multi-Source Domain Adaptation 用于多源域自适应的互学习网络 作者:Zhenpeng Li, Jieping Ye 链接:https://arxiv.org/abs/2003.12944
【1】 DHP: Differentiable Meta Pruning via HyperNetworks DHP:通过超网络的可微分元剪枝 作者:Yawei Li, Radu Timofte 链接:https://arxiv.org/abs/2003.13683
【2】 Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging 大规模快照压缩成像的即插即用算法 作者:Xin Yuan, Qionghai Dai 备注:CVPR 2020 链接:https://arxiv.org/abs/2003.13654
【3】 Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO 比快速更快:GPU加速的高速VIO前端 作者:Balazs Nagy, Davide Scaramuzza 备注:Submitted to IEEE International Conference on Intelligent Robots and Systems (IROS), 2020. Open-source implementation available at this https URL 链接:https://arxiv.org/abs/2003.13493
【4】 Acceleration of Convolutional Neural Network Using FFT-Based Split Convolutions 用基于FFT的分裂卷积加速卷积神经网络 作者:Kamran Chitsaz, Shahram Shirani 链接:https://arxiv.org/abs/2003.12621
【5】 How Not to Give a FLOP: Combining Regularization and Pruning for Efficient Inference 如何避免失败:结合正则化和修剪以实现有效的推理 作者:Tai Vu, Roy Nehoran 链接:https://arxiv.org/abs/2003.13593
【6】 BVI-DVC: A Training Database for Deep Video Compression BVI-DVC:一个深度视频压缩训练数据库 作者:Di Ma, David R. Bull 链接:https://arxiv.org/abs/2003.13552
【7】 Optimizing Geometry Compression using Quantum Annealing 利用量子退火优化几何压缩 作者:Sebastian Feld, Claudia Linnhoff-Popien 链接:https://arxiv.org/abs/2003.13253
【8】 Image compression optimized for 3D reconstruction by utilizing deep neural networks 利用深层神经网络优化三维重建的图像压缩 作者:Alex Golts, Yoav Y. Schechner 链接:https://arxiv.org/abs/2003.12618
【1】 Super Resolution for Root Imaging 根成像的超分辨率 作者:Jose F. Ruiz-Munoz, James E. Baciak 链接:https://arxiv.org/abs/2003.13537
【2】 High-Order Residual Network for Light Field Super-Resolution 用于光场超分辨的高阶残差网络 作者:Nan Meng, Edmund Y. Lam 链接:https://arxiv.org/abs/2003.13094
【3】 Structure-Preserving Super Resolution with Gradient Guidance 梯度导向的结构保持超分辨率 作者:Cheng Ma, Jie Zhou 备注:Accepted to CVPR 2020 链接:https://arxiv.org/abs/2003.13081
【1】 PointGMM: a Neural GMM Network for Point Clouds PointGMM:一种用于点云的神经GMM网络 作者:Amir Hertz, Daniel Cohen-Or 备注:CVPR 2020 -- final version 链接:https://arxiv.org/abs/2003.13326
【2】 A Benchmark for Point Clouds Registration Algorithms 点云配准算法的基准 作者:Simone Fontana, Domenico Giorgio Sorrenti 链接:https://arxiv.org/abs/2003.12841
【1】 Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets 重新思考深度可分卷积:核内相关如何导致改进的MobileNets 作者:Daniel Haase, Manuel Amthor 备注:Accepted by CVPR 2020 链接:https://arxiv.org/abs/2003.13549