Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: Mapping Stacked Decision Forests to Deep and Sparse Convolutional Neural Networks for Semantic Segmentation
Fully Convolutional Networks for Semantic Segmentation
Dense In Dense: Training Segmentation from Scratch
Progressively Diffused Networks for Semantic Image Segmentation
Deep Dual Learning for Semantic Image Segmentation
Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade
SCNet: A simplified encoder-decoder CNN for semantic segmentation
Stacked fully convolutional networks with multi-channel learning: application to medical image segmentation
Convolutional Scale Invariance for Semantic Segmentation
LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation
Weakly Supervised Learning with Deep Convolutional Neural Networks for Semantic Segmentation: Understanding Semantic Layout of Images with Minimum Human Supervision
Decision Forests, Convolutional Networks and the Models in-Between
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Predicting Deeper into the Future of Semantic Segmentation
ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
Analysis of Efficient CNN Design Techniques for Semantic Segmentation
Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery
3D Semantic Mapping Based on Convolutional Neural Networks
Deep Neural Decision Forests
HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation
Mining Educational Data Using Classification to Decrease Dropout Rate of Students
Keep it Unreal: Bridging the Realism Gap for 2.5D Recognition with Geometry Priors Only
Relational deep learning: a deep latent variable model for link prediction
Learning from hints in neural networks
Reinforcement learning and planning for preference balancing tasks