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Zero-Shot Semantic Segmentation
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
3D-PointZshotS: Geometry-Aware 3D Point Cloud Zero-Shot Semantic Segmentation Narrowing the Visual-Semantic Gap
Bridge the Gap Between Visual and Linguistic Comprehension for Generalized Zero-shot Semantic Segmentation
Disentangling CLIP for Multi-Object Perception
FLAIR: VLM with Fine-grained Language-informed Image Representations
Open-RGBT: Open-vocabulary RGB-T Zero-shot Semantic Segmentation in Open-world Environments
Segment Anything Model for automated image data annotation: empirical studies using text prompts from Grounding DINO
OpenObj: Open-Vocabulary Object-Level Neural Radiance Fields with Fine-Grained Understanding
DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized Cut
Cascade-CLIP: Cascaded Vision-Language Embeddings Alignment for Zero-Shot Semantic Segmentation
AlignZeg: Mitigating Objective Misalignment for Zero-shot Semantic Segmentation
Semantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots
OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation
Annotation Free Semantic Segmentation with Vision Foundation Models
Language-Driven Visual Consensus for Zero-Shot Semantic Segmentation
Learning Segmented 3D Gaussians via Efficient Feature Unprojection for Zero-shot Neural Scene Segmentation
Spectral Prompt Tuning:Unveiling Unseen Classes for Zero-Shot Semantic Segmentation
CSL: Class-Agnostic Structure-Constrained Learning for Segmentation Including the Unseen
SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference
SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding
CLIP Is Also a Good Teacher: A New Learning Framework for Inductive Zero-shot Semantic Segmentation
An easy zero-shot learning combination: Texture Sensitive Semantic Segmentation IceHrNet and Advanced Style Transfer Learning Strategy
CLIP-DIY: CLIP Dense Inference Yields Open-Vocabulary Semantic Segmentation For-Free
Masked Momentum Contrastive Learning for Zero-shot Semantic Understanding
MixReorg: Cross-Modal Mixed Patch Reorganization is a Good Mask Learner for Open-World Semantic Segmentation
What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation
Exploring Open-Vocabulary Semantic Segmentation without Human Labels
Interactive Segment Anything NeRF with Feature Imitation
Delving into Shape-aware Zero-shot Semantic Segmentation
MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation
[CLS] Token is All You Need for Zero-Shot Semantic Segmentation
SATR: Zero-Shot Semantic Segmentation of 3D Shapes
Open-Vocabulary Semantic Segmentation with Decoupled One-Pass Network
ZegOT: Zero-shot Segmentation Through Optimal Transport of Text Prompts
Class Enhancement Losses with Pseudo Labels for Zero-shot Semantic Segmentation
ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation
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