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Open Vocabulary Panoptic Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
DreamMask: Boosting Open-vocabulary Panoptic Segmentation with Synthetic Data
Open-Vocabulary Panoptic Segmentation Using BERT Pre-Training of Vision-Language Multiway Transformer Model
EOV-Seg: Efficient Open-Vocabulary Panoptic Segmentation
Adapting Vision-Language Model with Fine-grained Semantics for Open-Vocabulary Segmentation
Collaborative Vision-Text Representation Optimizing for Open-Vocabulary Segmentation
PosSAM: Panoptic Open-vocabulary Segment Anything
UMG-CLIP: A Unified Multi-Granularity Vision Generalist for Open-World Understanding
3D Open-Vocabulary Panoptic Segmentation with 2D-3D Vision-Language Distillation
CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction
Panoptic Vision-Language Feature Fields
Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP
FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation
Open-vocabulary Panoptic Segmentation with Embedding Modulation
Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models
Open-Vocabulary Universal Image Segmentation with MaskCLIP
Extract Free Dense Labels from CLIP
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.