Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
|
Subtasks
Datasets
Greatest papers with code
Pyramid Scene Parsing Network
CVPR 2017
• tensorflow/models
• 
Scene parsing is challenging for unrestricted open vocabulary and diverse scenes.
Ranked #3 on
Video Semantic Segmentation
on Cityscapes val
LESION SEGMENTATION REAL-TIME SEMANTIC SEGMENTATION SCENE PARSING VIDEO SEMANTIC SEGMENTATION
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
NeurIPS 2018
• tensorflow/models
•
Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks.
Ranked #3 on
Human Part Segmentation
on PASCAL-Part
IMAGE CLASSIFICATION META-LEARNING SEMANTIC SEGMENTATION STREET SCENE PARSING
Semantic Understanding of Scenes through the ADE20K Dataset
• CSAILVision/semantic-segmentation-pytorch
• 
Scene parsing, or recognizing and segmenting objects and stuff in an image, is one of the key problems in computer vision.
Semantic Flow for Fast and Accurate Scene Parsing
ECCV 2020
• donnyyou/torchcv
•
A common practice to improve the performance is to attain high resolution feature maps with strong semantic representation.
OPTICAL FLOW ESTIMATION REAL-TIME SEMANTIC SEGMENTATION SCENE PARSING
OCNet: Object Context Network for Scene Parsing
To capture richer context information, we further combine our interlaced sparse self-attention scheme with the conventional multi-scale context schemes including pyramid pooling~\citep{zhao2017pyramid} and atrous spatial pyramid pooling~\citep{chen2018deeplab}.
Ranked #20 on
Semantic Segmentation
on Cityscapes test
PSANet: Point-wise Spatial Attention Network for Scene Parsing
ECCV 2018
• open-mmlab/mmsegmentation
•
We notice information flow in convolutional neural networks is restricted inside local neighborhood regions due to the physical design of convolutional filters, which limits the overall understanding of complex scenes.
Ranked #20 on
Semantic Segmentation
on ADE20K val
SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications
Drones or general Unmanned Aerial Vehicles (UAVs), endowed with computer vision function by on-board cameras and embedded systems, have become popular in a wide range of applications.
Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs
• MIT-SPARK/Kimera
•
This mental model captures geometric and semantic aspects of the scene, describes the environment at multiple levels of abstractions (e. g., objects, rooms, buildings), includes static and dynamic entities and their relations (e. g., a person is in a room at a given time).
3D RECONSTRUCTION OBJECT LOCALIZATION SCENE PARSING VISUAL LOCALIZATION
Structured Knowledge Distillation for Semantic Segmentation
CVPR 2019
• irfanICMLL/structure_knowledge_distillation
•
We further propose to distill the structured knowledge from cumbersome networks into compact networks, which is motivated by the fact that semantic segmentation is a structured prediction problem.
IMAGE CLASSIFICATION KNOWLEDGE DISTILLATION SCENE PARSING SEMANTIC SEGMENTATION STRUCTURED PREDICTION
Structured Knowledge Distillation for Dense Prediction
CVPR 2019
• irfanICMLL/structure_knowledge_distillation
•
Here we propose to distill structured knowledge from large networks to compact networks, taking into account the fact that dense prediction is a structured prediction problem.
DEPTH ESTIMATION IMAGE CLASSIFICATION KNOWLEDGE DISTILLATION OBJECT DETECTION SCENE PARSING SEMANTIC SEGMENTATION STRUCTURED PREDICTION