Browse > Computer Vision > Scene Parsing > Scene Understanding

Scene Understanding

51 papers with code · Computer Vision
Subtask of Scene Parsing

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Greatest papers with code

Unified Perceptual Parsing for Scene Understanding

ECCV 2018 CSAILVision/semantic-segmentation-pytorch

In this paper, we study a new task called Unified Perceptual Parsing, which requires the machine vision systems to recognize as many visual concepts as possible from a given image.

SCENE UNDERSTANDING

Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions

13 Feb 2018facebookresearch/TensorComprehensions

Deep learning models with convolutional and recurrent networks are now ubiquitous and analyze massive amounts of audio, image, video, text and graph data, with applications in automatic translation, speech-to-text, scene understanding, ranking user preferences, ad placement, etc.

SCENE UNDERSTANDING

Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

9 Nov 2015alexgkendall/caffe-segnet

Semantic segmentation is an important tool for visual scene understanding and a meaningful measure of uncertainty is essential for decision making.

DECISION MAKING SCENE UNDERSTANDING SEMANTIC SEGMENTATION

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

2 Nov 2015alexgkendall/caffe-segnet

We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.

REAL-TIME SEMANTIC SEGMENTATION SCENE SEGMENTATION SCENE UNDERSTANDING

Dilated Residual Networks

CVPR 2017 osmr/imgclsmob

Convolutional networks for image classification progressively reduce resolution until the image is represented by tiny feature maps in which the spatial structure of the scene is no longer discernible.

IMAGE CLASSIFICATION OBJECT LOCALIZATION SCENE UNDERSTANDING SEMANTIC SEGMENTATION

Spatial As Deep: Spatial CNN for Traffic Scene Understanding

17 Dec 2017XingangPan/SCNN

Although CNN has shown strong capability to extract semantics from raw pixels, its capacity to capture spatial relationships of pixels across rows and columns of an image is not fully explored.

LANE DETECTION SCENE UNDERSTANDING

Matterport3D: Learning from RGB-D Data in Indoor Environments

18 Sep 2017niessner/Matterport

Access to large, diverse RGB-D datasets is critical for training RGB-D scene understanding algorithms.

SCENE UNDERSTANDING SEMANTIC SEGMENTATION

High Quality Monocular Depth Estimation via Transfer Learning

31 Dec 2018ialhashim/DenseDepth

Accurate depth estimation from images is a fundamental task in many applications including scene understanding and reconstruction.

MONOCULAR DEPTH ESTIMATION SCENE UNDERSTANDING TRANSFER LEARNING