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SOTA
Offene Vokabular-Semantische Segmentierung
Open Vocabulary Semantic Segmentation On 3
Open Vocabulary Semantic Segmentation On 3
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mIoU
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
mIoU
Paper Title
MaskCLIP++
16.8
High-Quality Mask Tuning Matters for Open-Vocabulary Segmentation
Mask-Adapter
16.2
Mask-Adapter: The Devil is in the Masks for Open-Vocabulary Segmentation
CAT-Seg
16.0
CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation
MAFT+
15.1
Collaborative Vision-Text Representation Optimizing for Open-Vocabulary Segmentation
SILC
15.0
SILC: Improving Vision Language Pretraining with Self-Distillation
PosSAM
14.9
PosSAM: Panoptic Open-vocabulary Segment Anything
FC-CLIP
14.8
Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP
SCAN
14.0
Open-Vocabulary Segmentation with Semantic-Assisted Calibration
SED
13.9
SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation
EBSeg-L
13.7
Open-Vocabulary Semantic Segmentation with Image Embedding Balancing
SAN
13.7
Side Adapter Network for Open-Vocabulary Semantic Segmentation
CLIPSelf
12.4
CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction
MAFT-ViTL
12.1
Learning Mask-aware CLIP Representations for Zero-Shot Segmentation
ODISE
11.1
Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models
OVSeg Swin-B
9
Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP
MaskCLIP
8.2
Open-Vocabulary Universal Image Segmentation with MaskCLIP
SimSeg
7
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model
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Open Vocabulary Semantic Segmentation On 3 | SOTA | HyperAI