HyperAI
Home
News
Latest Papers
Tutorials
Datasets
Wiki
SOTA
LLM Models
GPU Leaderboard
Events
Search
About
English
HyperAI
Toggle sidebar
Search the site…
⌘
K
Home
SOTA
Semantic Segmentation
Semantic Segmentation On Nyu Depth V2
Semantic Segmentation On Nyu Depth V2
Metrics
Mean IoU
Results
Performance results of various models on this benchmark
Columns
Model Name
Mean IoU
Paper Title
Repository
DynMM (ResNet-50)
51.0%
Dynamic Multimodal Fusion
GeminiFusion (Swin-Large)
60.9
GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformer
CMX (B2)
54.4%
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
LS-DeconvNet
45.9%
Locality-Sensitive Deconvolution Networks With Gated Fusion for RGB-D Indoor Semantic Segmentation
-
ESANet (R18-NBt1D )
48.17
Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis
-
3DGNN
43.1%
3D Graph Neural Networks for RGBD Semantic Segmentation
AsymFormer
55.3%
AsymFormer: Asymmetrical Cross-Modal Representation Learning for Mobile Platform Real-Time RGB-D Semantic Segmentation
AsymFusion (ResNet-152)
51.2%
Learning Deep Multimodal Feature Representation with Asymmetric Multi-layer Fusion
SwinMTL
58.14%
SwinMTL: A Shared Architecture for Simultaneous Depth Estimation and Semantic Segmentation from Monocular Camera Images
OmniVec2
63.6
OmniVec2 - A Novel Transformer based Network for Large Scale Multimodal and Multitask Learning
-
MMAF-Net-152
44.8%
Multi-Modal Attention-based Fusion Model for Semantic Segmentation of RGB-Depth Images
-
GeminiFusion (MiT-B3)
56.8
GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformer
Cross-stitch
19.3%
Cross-stitch Networks for Multi-task Learning
HN-network
33.49%
RGB-based Semantic Segmentation Using Self-Supervised Depth Pre-Training
-
CMNeXt (B4)
56.9%
Delivering Arbitrary-Modal Semantic Segmentation
CFN
47.7%
Cascaded Feature Network for Semantic Segmentation of RGB-D Images
-
NDDR-CNN
43.3%
NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction
ACNet
48.3%
ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation
Malleable 2.5D (ResNet-101)
50.9%
Malleable 2.5D Convolution: Learning Receptive Fields along the Depth-axis for RGB-D Scene Parsing
SGACNet (R34-NBt1D)
49.4%
Spatial-information Guided Adaptive Context-aware Network for Efficient RGB-D Semantic Segmentation
0 of 116 row(s) selected.
Previous
Next