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Semantic Segmentation On Sun Rgbd

Metrics

Mean IoU (test)

Results

Performance results of various models on this benchmark

Model Name
Mean IoU (test)
Paper TitleRepository
DFormer-L48.17Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation-
CMX (B5)-Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis-
EMSANet (2x ResNet-34 NBt1D, PanopticNDT version, finetuned)-Context Contrasted Feature and Gated Multi-Scale Aggregation for Scene Segmentation
CMX (B4)-CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers-
FSFNet-Deep feature selection-and-fusion for RGB-D semantic segmentation-
TokenFusion (S)-Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation-
TokenFusion (S)-Depth-aware CNN for RGB-D Segmentation-
PSD-ResNet50-ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation-
DFormer-B-RDFNet: RGB-D Multi-Level Residual Feature Fusion for Indoor Semantic Segmentation-
EMSANet (2x ResNet-34 NBt1D, PanopticNDT version, finetuned)-PanopticNDT: Efficient and Robust Panoptic Mapping-
GeminiFusion (MiT-B5)-GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformer-
DFormer-L-Attention-guided Chained Context Aggregation for Semantic Segmentation-
TokenFusion (S)-Multimodal Token Fusion for Vision Transformers-
TokenFusion (Ti)-Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation-
DFormer-L-DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation-
DPLNet -Self-Supervised Model Adaptation for Multimodal Semantic Segmentation-
DPLNet -Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning-
TokenFusion (Ti)-Multimodal Token Fusion for Vision Transformers-
CMX (B4)-Pixel Difference Convolutional Network for RGB-D Semantic Segmentation-
DPLNet -Recurrent Scene Parsing with Perspective Understanding in the Loop-
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Semantic Segmentation On Sun Rgbd | SOTA | HyperAI