Scene Understanding
511 papers with code • 3 benchmarks • 43 datasets
Scene Understanding is something that to understand a scene. For instance, iPhone has function that help eye disabled person to take a photo by discribing what the camera sees. This is an example of Scene Understanding.
Benchmarks
These leaderboards are used to track progress in Scene Understanding
Libraries
Use these libraries to find Scene Understanding models and implementationsDatasets
ADE20K
KITTI Road
UAVid
RELLIS-3D
SUIM
SensatUrban
3RScan
MUSIC-AVQA
ShapeStacks
Toronto-3D
Subtasks
Most implemented papers
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
This dataset demonstrates the post flooded damages of the affected areas.
Boundary-Seeking Generative Adversarial Networks
eriklindernoren/PyTorch-GAN
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We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator.
Multi-Task Learning as Multi-Objective Optimization
IntelVCL/MultiObjectiveOptimization
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NeurIPS 2018
These algorithms are not directly applicable to large-scale learning problems since they scale poorly with the dimensionality of the gradients and the number of tasks.
ShelfNet for Fast Semantic Segmentation
Compared with real-time segmentation models such as BiSeNet, our model achieves higher accuracy at comparable speed on the Cityscapes Dataset, enabling the application in speed-demanding tasks such as street-scene understanding for autonomous driving.
From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network
sshaoshuai/PointCloudDet3D
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3D object detection from LiDAR point cloud is a challenging problem in 3D scene understanding and has many practical applications.
Adapting Deep Network Features to Capture Psychological Representations
To remedy this, we develop a method for adapting deep features to align with human similarity judgments, resulting in image representations that can potentially be used to extend the scope of psychological experiments.
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Despite the relevance of semantic scene understanding for this application, there is a lack of a large dataset for this task which is based on an automotive LiDAR.
Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN
vinthony/ghost-free-shadow-removal
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With the help of novel masks or scenes, we enhance the current datasets using synthesized shadow images.
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
The goal of COCO-Text is to advance state-of-the-art in text detection and recognition in natural images.
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
facebookresearch/TensorComprehensions
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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.
