Skip to content
conftrace_

Papers (including subtopics)

Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals CVPR 2018 Detect-and-Track: Efficient Pose Estimation in Videos CVPR 2018 Detect Globally, Refine Locally: A Novel Approach to Saliency Detection CVPR 2018 Detecting and Recognizing Human-Object Interactions CVPR 2018 Dialog-based Interactive Image Retrieval NeurIPS 2018 DifNet: Semantic Segmentation by Diffusion Networks NeurIPS 2018 Direction-Aware Spatial Context Features for Shadow Detection CVPR 2018 Direct Shape Regression Networks for End-to-End Face Alignment CVPR 2018 Discovering Point Lights With Intensity Distance Fields CVPR 2018 Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning NeurIPS 2018 Discriminative Feature Representation for Person Re-identification by Batch-contrastive Loss ACML 2018 Discriminative Learning of Open-Vocabulary Object Retrieval and Localization by Negative Phrase Augmentation EMNLP 2018 Disentangled Person Image Generation CVPR 2018 Disentangling 3D Pose in a Dendritic CNN for Unconstrained 2D Face Alignment CVPR 2018 Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition CVPR 2018 Diversity Regularized Spatiotemporal Attention for Video-Based Person Re-Identification CVPR 2018 Divide and Grow: Capturing Huge Diversity in Crowd Images With Incrementally Growing CNN CVPR 2018 Domain Adaptive Faster R-CNN for Object Detection in the Wild CVPR 2018 Do not Lose the Details: Reinforced Representation Learning for High Performance Visual Tracking IJCAI 2018 DOTA: A Large-Scale Dataset for Object Detection in Aerial Images CVPR 2018 DoubleFusion: Real-Time Capture of Human Performances With Inner Body Shapes From a Single Depth Sensor CVPR 2018 DRPose3D: Depth Ranking in 3D Human Pose Estimation IJCAI 2018 Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-Identification CVPR 2018 Dual Principal Component Pursuit JMLR 2018 Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks ICML 2018