Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: A Residual Encoder-Decoder Network for Semantic Segmentation in Autonomous Driving Scenarios
LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
Fully Convolutional Networks for Semantic Segmentation
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Predicting Deeper into the Future of Semantic Segmentation
Semi and Weakly Supervised Semantic Segmentation Using Generative Adversarial Network
Efficient deep models for monocular road segmentation
End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks
RefineNet: Refining Object Detectors for Autonomous Driving
Convolutional Scale Invariance for Semantic Segmentation
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation
RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation
LiSeg: Lightweight Road-object Semantic Segmentation In 3D LiDAR Scans For Autonomous Driving
Towards self-driving car using convolutional neural network and road lane detector
segmentation - based urban traffic scene understanding .
Low cost and power CNN/deep learning solution for automated driving
BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
A Systematic Comparison of Deep Learning Architectures in an Autonomous Vehicle
Road Scene Segmentation from a Single Image
Hierarchically Supervised Deconvolutional Network for Semantic Video Segmentation
Scalar implicatures: experiments at the semantics–pragmatics interface
Deep features-based speech emotion recognition for smart affective services
Elastic Spatial Query Processing in OpenStack Cloud Computing Environment for Time-Constraint Data Analysis
problem formulation 2 . 1 . 2 d uav dynamical models .
Efficient object localization using Convolutional Networks