Scene Labeling
4 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Scene Labeling
Stanford Background
Latest papers with no code
Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions
Rather than relying on elaborative annotations (e. g., manually labeled semantic maps and relations), we train our deep model in a weakly-supervised learning manner by leveraging the descriptive sentences of the training images.
Exploring Directional Path-Consistency for Solving Constraint Networks
Among the local consistency techniques used for solving constraint networks, path-consistency (PC) has received a great deal of attention.
Unsupervised Semantic Scene Labeling for Streaming Data
We introduce an unsupervised semantic scene labeling approach that continuously learns and adapts semantic models discovered within a data stream.
Episodic CAMN: Contextual Attention-Based Memory Networks With Iterative Feedback for Scene Labeling
Scene labeling can be seen as a sequence-sequence prediction task (pixels-labels), and it is quite important to leverage relevant context to enhance the performance of pixel classification.
Learning Deep Representations for Scene Labeling with Semantic Context Guided Supervision
The experiments show that our proposed method makes deep models learn more discriminative feature representations without increasing model size or complexity.
Deep Contextual Recurrent Residual Networks for Scene Labeling
Designed as extremely deep architectures, deep residual networks which provide a rich visual representation and offer robust convergence behaviors have recently achieved exceptional performance in numerous computer vision problems.
Self corrective Perturbations for Semantic Segmentation and Classification
Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performance in many computer vision problems.
Scene Labeling using Gated Recurrent Units with Explicit Long Range Conditioning
A novel neural network architecture is built for scene labeling tasks where one of the variants of the new RNN unit, Gated Recurrent Unit with Explicit Long-range Conditioning (GRU-ELC), is used to model multi scale contextual dependencies in images.
Computationally Efficient Target Classification in Multispectral Image Data with Deep Neural Networks
The required communication links and archiving of the video data are still expensive and this setup excludes preemptive actions to respond to imminent threats.
Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks
This work investigates the use of deep fully convolutional neural networks (DFCNN) for pixel-wise scene labeling of Earth Observation images.
