Table of Contents

• Deep Learning for Automated Tagging of Fashion ImagesPatricia Gutierrez, Pierre-Antoine Sondag, Petar Butkovic, Mauro Lacy, Jordi Berges, Felipe Bertrand, Arne Knudson. 3-11 [doi]
• Brand > Logo: Visual Analysis of Fashion BrandsM. Hadi Kiapour, Robinson Piramuthu. 12-20 [doi]
• Tiered Deep Similarity Search for FashionDipu Manandhar, Muhammet Bastan, Kim-Hui Yap. 21-29 [doi]
• Deep Fashion Analysis with Feature Map Upsampling and Landmark-Driven AttentionJingyuan Liu, Hong Lu. 30-36 [doi]
• DesIGN: Design Inspiration from Generative NetworksOthman Sbai, Mohamed Elhoseiny, Antoine Bordes, Yann LeCun, Camille Couprie. 37-44 [doi]
• FashionSearchNet: Fashion Search with Attribute ManipulationKenan E. Ak, Ashraf A. Kassim, Joo-Hwee Lim, Jo Yew Tham. 45-53 [doi]
• CRAFT: Complementary Recommendation by Adversarial Feature TransformCong Phuoc Huynh, Arridhana Ciptadi, Ambrish Tyagi, Amit Agrawal. 54-66 [doi]
• Full-Body High-Resolution Anime Generation with Progressive Structure-Conditional Generative Adversarial NetworksKoichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, Yusuke Uchida. 67-74 [doi]
• Convolutional Photomosaic Generation via Multi-scale Perceptual LossesMatthew Tesfaldet, Nariman Saftarli, Marcus A. Brubaker, Konstantinos G. Derpanis. 75-83 [doi]
• Action Anticipation by Predicting Future Dynamic ImagesCristian Rodriguez, Basura Fernando, Hongdong Li. 89-105 [doi]
• Predicting Action TubesGurkirt Singh, Suman Saha, Fabio Cuzzolin. 106-123 [doi]
• Forecasting Hands and Objects in Future FramesChenyou Fan, Jangwon Lee, Michael S. Ryoo. 124-137 [doi]
• RED: A Simple but Effective Baseline Predictor for the TrajNet BenchmarkStefan Becker, Ronny Hug, Wolfgang Hübner, Michael Arens. 138-153 [doi]
• Joint Future Semantic and Instance Segmentation PredictionCamille Couprie, Pauline Luc, Jakob Verbeek. 154-168 [doi]
• Context Graph Based Video Frame Prediction Using Locally Guided ObjectivePrateep Bhattacharjee, Sukhendu Das. 169-185 [doi]
• Convolutional Neural Network for Trajectory PredictionNishant Nikhil, Brendan Tran Morris. 186-196 [doi]
• Action Alignment from Gaze Cues in Human-Human and Human-Robot InteractionNuno Ferreira Duarte, Mirko Rakovic, Jorge Marques, José Santos-Victor. 197-212 [doi]
• Group LSTM: Group Trajectory Prediction in Crowded ScenariosNiccoló Bisagno, Bo Zhang, Nicola Conci. 213-225 [doi]
• Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera RelocalizationXiaotian Li, Juha Ylioinas, Jakob Verbeek, Juho Kannala. 229-245 [doi]
• Deep Normal Estimation for Automatic Shading of Hand-Drawn CharactersMatis Hudon, Mairéad Grogan, Rafael Pagés, Aljosa Smolic. 246-262 [doi]
• 3D Surface Reconstruction by PointillismOlivia Wiles, Andrew Zisserman. 263-280 [doi]
• Detecting Parallel-Moving Objects in the Monocular Case Employing CNN Depth MapsNolang Fanani, Matthias Ochs, Rudolf Mester. 281-297 [doi]
• Object Pose Estimation from Monocular Image Using Multi-view Keypoint CorrespondenceJogendra Nath Kundu, M. V. Rahul, Aditya Ganeshan, R. Venkatesh Babu. 298-313 [doi]
• 3DContextNet: K-d Tree Guided Hierarchical Learning of Point Clouds Using Local and Global Contextual CuesWei Zeng, Theo Gevers. 314-330 [doi]
• Evaluation of CNN-Based Single-Image Depth Estimation MethodsTobias Koch, Lukas Liebel, Friedrich Fraundorfer, Marco Körner 0001. 331-348 [doi]
• A Simple Approach to Intrinsic Correspondence Learning on Unstructured 3D MeshesIsaak Lim, Alexander Dielen, Marcel Campen, Leif Kobbelt. 349-362 [doi]
• Learning Structure-from-Motion from MotionClément Pinard, Laure Chevalley, Antoine Manzanera, David Filliat. 363-376 [doi]
• Learning Spectral Transform Network on 3D Surface for Non-rigid Shape AnalysisRuixuan Yu, Jian Sun, Huibin Li. 377-394 [doi]
• Know What Your Neighbors Do: 3D Semantic Segmentation of Point CloudsFrancis Engelmann, Theodora Kontogianni, Jonas Schult, Bastian Leibe. 395-409 [doi]
• Deep Learning for Multi-path Error Removal in ToF SensorsGianluca Agresti, Pietro Zanuttigh. 410-426 [doi]
• PosIX-GAN: Generating Multiple Poses Using GAN for Pose-Invariant Face RecognitionAvishek Bhattacharjee, Samik Banerjee, Sukhendu Das. 427-443 [doi]
• Semi-supervised Semantic MatchingZakaria Laskar, Juho Kannala. 444-455 [doi]
• Multi-kernel Diffusion CNNs for Graph-Based Learning on Point CloudsLasse Hansen, Jasper Diesel, Mattias P. Heinrich. 456-469 [doi]
• Attaining Human-Level Performance with Atlas Location Autocontext for Anatomical Landmark Detection in 3D CT DataAlison Q. O'Neil, Antanas Kascenas, Joseph Henry, Daniel Wyeth, Matthew Shepherd, Erin Beveridge, Lauren Clunie, Carrie Sansom, Evelina Seduikyte, Keith Muir, Ian Poole. 470-484 [doi]
• Deep Fundamental Matrix Estimation Without CorrespondencesOmid Poursaeed, Guandao Yang, Aditya Prakash, Qiuren Fang, Hanqing Jiang, Bharath Hariharan, Serge J. Belongie. 485-497 [doi]
• High Quality Facial Surface and Texture Synthesis via Generative Adversarial NetworksRon Slossberg, Gil Shamai, Ron Kimmel. 498-513 [doi]
• Recent Advances at the Brain-Driven Computer Vision Workshop 2018Simone Palazzo, Isaak Kavasidis, Dimitris Kastaniotis, Stavros I. Dimitriadis. 519-525 [doi]
• CapsuleGAN: Generative Adversarial Capsule NetworkAyush Jaiswal, Wael AbdAlmageed, Yue Wu 0001, Premkumar Natarajan. 526-535 [doi]
• Navigational Affordance Cortical Responses Explained by Scene-Parsing ModelKshitij Dwivedi, Gemma Roig. 536-545 [doi]
• A Context-Aware Capsule Network for Multi-label ClassificationSameera Ramasinghe, C. D. Athuraliya, Salman H. Khan. 546-554 [doi]
• Brain-Inspired Robust Delineation OperatorNicola Strisciuglio, George Azzopardi, Nicolai Petkov. 555-565 [doi]
• Understanding Fake FacesRyota Natsume, Kazuki Inoue, Yoshihiro Fukuhara, Shintaro Yamamoto, Shigeo Morishima, Hirokatsu Kataoka. 566-576 [doi]
• Characterization of Visual Object Representations in Rat Primary Visual CortexSebastiano Vascon, Ylenia Parin, Eis Annavini, Mattia D'Andola, Davide Zoccolan, Marcello Pelillo. 577-586 [doi]
• Learning Event Representations by Encoding the Temporal ContextCatarina Dias, Mariella Dimiccoli. 587-596 [doi]
• Decoding Generic Visual Representations from Human Brain Activity Using Machine LearningAngeliki Papadimitriou, Nikolaos Passalis, Anastasios Tefas. 597-606 [doi]
• EmoP3D: A Brain Like Pyramidal Deep Neural Network for Emotion RecognitionEmanuel Di Nardo, Alfredo Petrosino, Ihsan Ullah. 607-616 [doi]
• Subitizing with Variational AutoencodersRijnder Wever, Tom F. H. Runia. 617-627 [doi]
• The Second Workshop on 3D Reconstruction Meets Semantics: Challenge Results DiscussionRadim Tylecek, Torsten Sattler, Hoang-An Le, Thomas Brox, Marc Pollefeys, Robert B. Fisher, Theo Gevers. 631-644 [doi]
• A Deeper Look at 3D Shape ClassifiersJong-Chyi Su, Matheus Gadelha, Rui Wang 0003, Subhransu Maji. 645-661 [doi]
• 3D-PSRNet: Part Segmented 3D Point Cloud Reconstruction from a Single ImagePriyanka Mandikal, Navaneet K. L., R. Venkatesh Babu. 662-674 [doi]
• Exploiting Multi-layer Features Using a CNN-RNN Approach for RGB-D Object RecognitionAli Caglayan, Ahmet Burak Can. 675-688 [doi]
• Temporally Consistent Depth Estimation in Videos with Recurrent ArchitecturesDenis Tananaev, Huizhong Zhou, Benjamin Ummenhofer, Thomas Brox. 689-701 [doi]
• End-to-End 6-DoF Object Pose Estimation Through Differentiable RasterizationAndrea Palazzi, Luca Bergamini, Simone Calderara, Rita Cucchiara. 702-715 [doi]
• YOLO3D: End-to-End Real-Time 3D Oriented Object Bounding Box Detection from LiDAR Point CloudWaleed Ali, Sherif Abdelkarim, Mahmoud Zidan, Mohamed Zahran, Ahmad El Sallab. 716-728 [doi]
• Increasing the Robustness of CNN-Based Human Body Segmentation in Range Images by Modeling Sensor-Specific ArtifactsLama Seoud, Jonathan Boisvert, Marc-Antoine Drouin, Michel Picard, Guy Godin. 729-743 [doi]