Scene Parsing
75 papers with code • 2 benchmarks • 4 datasets
Scene parsing is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed. MIT Description
Benchmarks
These leaderboards are used to track progress in Scene Parsing
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Libraries
Use these libraries to find Scene Parsing models and implementations
Cityscapes
Stanford Background
PGDP5K
UNDD
Subtasks
Latest papers with no code
Feature boosting with efficient attention for scene parsing
This paper presents a novel feature-boosting network that gathers spatial context from multiple levels of feature extraction and computes the attention weights for each level of representation to generate the final class labels.
SAI3D: Segment Any Instance in 3D Scenes
Advancements in 3D instance segmentation have traditionally been tethered to the availability of annotated datasets, limiting their application to a narrow spectrum of object categories.
A Review and A Robust Framework of Data-Efficient 3D Scene Parsing with Traditional/Learned 3D Descriptors
To the best of our knowledge, there exists no unified framework that simultaneously solves the downstream high-level understanding tasks including both segmentation and detection, especially when labels are extremely limited.
CaveSeg: Deep Semantic Segmentation and Scene Parsing for Autonomous Underwater Cave Exploration
In this paper, we present CaveSeg - the first visual learning pipeline for semantic segmentation and scene parsing for AUV navigation inside underwater caves.
RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing
Additionally, we release SYN-UDTIRI, the first large-scale road scene parsing dataset that contains over 10, 407 RGB images, dense depth images, and the corresponding pixel-level annotations for both freespace and road defects of different shapes and sizes.
CACFNet: Cross-Modal Attention Cascaded Fusion Network for RGB-T Urban Scene Parsing
Color–thermal (RGB-T) urban scene parsing has recently attracted widespread interest.
EGFNet: Edge-Aware Guidance Fusion Network for RGB–Thermal Urban Scene Parsing
To address these problems, an edge-aware guidance fusion network (EGFNet) was developed in this study for RGB–thermal urban scene parsing.
Semantic Segmentation on VSPW Dataset through Contrastive Loss and Multi-dataset Training Approach
Video scene parsing incorporates temporal information, which can enhance the consistency and accuracy of predictions compared to image scene parsing.
Recyclable Semi-supervised Method Based on Multi-model Ensemble for Video Scene Parsing
Pixel-level Scene Understanding is one of the fundamental problems in computer vision, which aims at recognizing object classes, masks and semantics of each pixel in the given image.
Cross-CBAM: A Lightweight network for Scene Segmentation
And we propose a Cross Convolutional Block Attention Module(CCBAM), in which a cross-multiply operation is employed in the CCBAM module to make high-level semantic information guide low-level detail information.
