Papers with Code 아카이브
Video Polyp Segmentation
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Polyp-SES: Automatic Polyp Segmentation with Self-Enriched Semantic Model
Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model
Diff-VPS: Video Polyp Segmentation via a Multi-task Diffusion Network with Adversarial Temporal Reasoning
LGRNet: Local-Global Reciprocal Network for Uterine Fibroid Segmentation in Ultrasound Videos
SALI: Short-term Alignment and Long-term Interaction Network for Colonoscopy Video Polyp Segmentation
SSTFB: Leveraging self-supervised pretext learning and temporal self-attention with feature branching for real-time video polyp segmentation
MAST: Video Polyp Segmentation with a Mixture-Attention Siamese Transformer
Shifting More Attention to Breast Lesion Segmentation in Ultrasound Videos
Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?
WeakPolyp: You Only Look Bounding Box for Polyp Segmentation
Rectifying Noisy Labels with Sequential Prior: Multi-Scale Temporal Feature Affinity Learning for Robust Video Segmentation
AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder
Video Polyp Segmentation: A Deep Learning Perspective
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos
Full-Duplex Strategy for Video Object Segmentation
Shallow Attention Network for Polyp Segmentation
Progressively Normalized Self-Attention Network for Video Polyp Segmentation
ACSNet: Action-Context Separation Network for Weakly Supervised Temporal Action Localization
MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object Segmentation
PraNet: Parallel Reverse Attention Network for Polyp Segmentation
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.