CVPR2019| CVPR论文接收列表！
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【1】Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos（Romero Morais; Vuong Le; Truyen Tran; Budhaditya Saha; Moussa Mansour; Svetha Venkatesh ）
论文地址：https://arxiv.org/abs/1903.03295
【2】Learning from Synthetic Data for Crowd Counting in the Wild（Qi Wang, Junyu Gao, Wei Lin, Yuan Yuan）
论文地址：https://arxiv.org/abs/1903.03303
【3】Knowledge-Embedded Routing Network for Scene Graph Generation（Tianshui Chen, Weihao Yu, Riquan Chen, Liang Lin）
论文地址：https://arxiv.org/abs/1903.03326
【4】Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval（Anjan Dutta, Zeynep Akata）
论文地址：https://arxiv.org/abs/1903.03372
【5】Structured Knowledge Distillation for Semantic Segmentation
https://arxiv.org/pdf/1903.04197.pdf
【5】Strong-Weak Distribution Alignment for Adaptive Object Detection（Kuniaki Saito1、Yoshitaka Ushiku2、Tatsuya Harada2,3、Kate Saenko1，波士顿大、学东京大学）
论文地址：https://arxiv.org/pdf/1812.04798.pdf
【7】PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation（Fenggen Yu、Kun Liu1、Yan Zhang1、Chenyang Zhu、Kai Xu，南京大学、国防科技大学）
论文地址：https://arxiv.org/pdf/1903.00709.pdf
【9】Understanding and Visualizing Deep Visual Saliency Models（Sen He、Hamed R. Tavakoli、Ali Borji、Yang Mi、Nicolas Pugeault，埃克塞特大学、阿尔托大学）
论文地址：https://arxiv.org/pdf/1903.02501.pdf
【9】Depth Coefficients for Depth Completion（Saif Imran、Yunfei Long、Xiaoming Liu、Daniel Morris，密歇根州立大学）
论文地址：https://arxiv.org/pdf/1903.05421.pdf
【10】RVOS: End-to-End Recurrent Network for Video Object Segmentation
论文地址：https://arxiv.org/pdf/1903.05612.pdf
【11】Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis（北京大学、加利福尼亚大学）
论文地址：https://arxiv.org/pdf/1903.05628.pdf
【12】MirrorGAN: Learning Text-to-image Generation by Redescription
论文地址：https://arxiv.org/pdf/1903.05854.pdf
【13】Deep Transfer Learning for Multiple Class Novelty Detection
论文地址：https://arxiv.org/abs/1903.02196
【14】AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Data
https://arxiv.org/pdf/1901.04596.pdf
【15】ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding
https://arxiv.org/pdf/1811.11968.pdf
【16】Fast Online Object Tracking and Segmentation: A Unifying Approach
开源：https://github.com/foolwood/SiamMask
【17】Dual Encoding for Zero-Example Video Retrieval
论文地址：https://arxiv.org/abs/1809.06181
开源地址：https://github.com/danieljf24/dual_encoding
【18】Supervised Fitting of Geometric Primitives to 3D Point Clouds
https://arxiv.org/abs/1811.08988
【19】Learning 3D Human Dynamics from Video
https://arxiv.org/abs/1812.01601
【20】Explainable and Explicit Visual Reasoning over Scene Graphs
https://arxiv.org/abs/1812.01855
【21】Learning Parallax Attention for Stereo Image Super-Resolution
https://arxiv.org/abs/1903.05784
【22】AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs
https://arxiv.org/abs/1903.07062
【23】QATM: Quality-Aware Template Matching For Deep Learning
https://arxiv.org/abs/1903.07254
【24】Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection
https://arxiv.org/abs/1903.07256
【25】Self-calibrating Deep Photometric Stereo Networks（oral）
https://arxiv.org/abs/1903.07366
【26】Understanding the Limitations of CNN-based Absolute Camera Pose Regression
https://arxiv.org/abs/1903.07504
【27】Learning Correspondence from the Cycle-Consistency of Time
https://arxiv.org/abs/1903.07593
http://ajabri.github.io/timecycle
【28】Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving
https://arxiv.org/abs/1812.07179
【29】SimulCap : Single-View Human Performance Capture with Cloth Simulation
https://arxiv.org/abs/1903.06323
【30】Neural Sequential Phrase Grounding (SeqGROUND)
https://arxiv.org/abs/1903.07669
【31】Direct Object Recognition Without Line-of-Sight Using Optical Coherence
https://arxiv.org/abs/1903.07705
【32】SceneCode: Monocular Dense Semantic Reconstruction using Learned Encoded Scene Representations
https://arxiv.org/abs/1903.06482
【33】Probabilistic End-to-end Noise Correction for Learning with Noisy Labels
https://arxiv.org/abs/1903.07788
【34】Semantic Image Synthesis with Spatially-Adaptive Normalization（oral）
https://arxiv.org/abs/1903.07291
【35】Inverse Path Tracing for Joint Material and Lighting Estimation（oral）
https://arxiv.org/abs/1903.07145
【36】Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
https://arxiv.org/abs/1903.05628
https://github.com/HelenMao/MSGAN
【37】Selective Kernel Networks
https://arxiv.org/abs/1903.06586
【38】A Cross-Season Correspondence Dataset for Robust Semantic Segmentation
https://arxiv.org/abs/1903.06916
【39】Unsupervised Part-Based Disentangling of Object Shape and Appearance
https://arxiv.org/abs/1903.06946
【40】Inserting Videos into Videos
https://arxiv.org/abs/1903.06571
【41】Disentangling Latent Space for VAE by Label Relevant/Irrelevant Dimensions
https://arxiv.org/abs/1812.09502
【42】Domain Generalization by Solving Jigsaw Puzzles
https://arxiv.org/abs/1903.06864
【43】Fast Interactive Object Annotation with Curve-GCN
https://arxiv.org/abs/1903.06874
【44】MFAS: Multimodal Fusion Architecture Search
https://arxiv.org/abs/1903.06496
【45】OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations
https://arxiv.org/abs/1903.08550
【46】An Efficient Schmidt-EKF for 3D Visual-Inertial SLAM
https://arxiv.org/abs/1903.08636
【47】Photometric Mesh Optimization for Video-Aligned 3D Object Reconstruction
https://arxiv.org/abs/1903.08642
code: https://chenhsuanlin.bitbucket.io/photometric-mesh-optim/
【48】Towards Robust Curve Text Detection with Conditional Spatial Expansion
https://arxiv.org/abs/1903.08836
【49】Learning with Batch-wise Optimal Transport Loss for 3D Shape Recognition
https://arxiv.org/abs/1903.08923
【50】Weakly-Supervised Discovery of Geometry-Aware Representation for 3D Human Pose Estimation
https://arxiv.org/pdf/1903.08839.pdf
【51】Patch-based Progressive 3D Point Set Upsampling
https://arxiv.org/abs/1811.11286