HyperAI

Open Vocabulary Semantic Segmentation On 5

المقاييس

mIoU

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج
mIoU
Paper TitleRepository
TCL83.2Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs
MaskCLIP++96.8MaskCLIP++: A Mask-Based CLIP Fine-tuning Framework for Open-Vocabulary Image Segmentation
SCAN97.2Open-Vocabulary Segmentation with Semantic-Assisted Calibration
POMP89.4Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition
OVSeg Swin-B94.5Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP
EBSeg-L96.4Open-Vocabulary Semantic Segmentation with Image Embedding Balancing
ODISE84.6Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models
ZegFormer-Decoupling Zero-Shot Semantic Segmentation
TagAlign(trained with image-text pairs)87.9TagAlign: Improving Vision-Language Alignment with Multi-Tag Classification
ZSSeg-A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model
MAFT-ViTL92.1Learning Mask-aware CLIP Representations for Zero-Shot Segmentation-
PACL72.3Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning
HyperSeg92.1HyperSeg: Towards Universal Visual Segmentation with Large Language Model
MAFT+96.5Collaborative Vision-Text Representation Optimizing for Open-Vocabulary Segmentation
LaVG82.5In Defense of Lazy Visual Grounding for Open-Vocabulary Semantic Segmentation
SILC97.6SILC: Improving Vision Language Pretraining with Self-Distillation-
FC-CLIP95.4Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP
CAT-Seg97.0CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation
MAFT-ViTL92.1--
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