Papers with Code 아카이브
2D Semantic Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 17
- 평가표
- 11
- 보존된 평가 기록
- 42
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/king159/pair-net
공식 코드Pair then Relation: Pair-Net for Panoptic Scene Graph Generation
https://github.com/kiraving/SegGradCAM
공식 코드Towards Interpretable Semantic Segmentation via Gradient-weighted Class Activation Mapping
https://github.com/kiretd/Unsupervised-MIseg
공식 코드https://github.com/kkakkkka/etris
공식 코드Bridging Vision and Language Encoders: Parameter-Efficient Tuning for Referring Image Segmentation
https://github.com/kkk55596/D-Former
공식 코드D-Former: A U-shaped Dilated Transformer for 3D Medical Image Segmentation
https://github.com/kleinzcy/SASSnet
공식 코드Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images
https://github.com/kleinzcy/csfpn
공식 코드Cascaded Sparse Feature Propagation Network for Interactive Segmentation
https://github.com/kmaag/Temporal-False-Negative-Reduction
공식 코드False Negative Reduction in Video Instance Segmentation using Uncertainty Estimates
https://github.com/kmaag/adversarial-attack-detection-uncertainty
공식 코드Uncertainty-based Detection of Adversarial Attacks in Semantic Segmentation
https://github.com/kmaag/ood-tracking
공식 코드Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects
https://github.com/kmaag/uncertainty-weighted-loss
공식 코드Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation
https://github.com/kodenii/ref-diff
공식 코드Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models
https://github.com/koechslin/swin-transformer-semantic-segmentation
공식 코드https://github.com/kolesman/SEC
공식 코드Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation
https://github.com/koncle/coranet
공식 코드Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation
https://github.com/kormachine/partstad
공식 코드https://github.com/krishnabits001/domain_specific_cl
공식 코드https://github.com/krishnabits001/task_driven_data_augmentation
공식 코드Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation
https://github.com/krishnaswamylab/cuts
공식 코드https://github.com/ksgr5566/unsegarmanet
공식 코드https://github.com/ksgr5566/unseggnet
공식 코드UnSegGNet: Unsupervised Image Segmentation using Graph Neural Networks
https://github.com/kshitij3112/evlpsnet
공식 코드https://github.com/kshitij3112/evpsnet
공식 코드https://github.com/ku21fan/STR-Fewer-Labels
공식 코드https://github.com/ku21fan/cll-str
공식 코드Cross-Lingual Learning in Multilingual Scene Text Recognition
https://github.com/kwonjunn01/hi-mapper
공식 코드Improving Visual Recognition with Hyperbolical Visual Hierarchy Mapping
https://github.com/kyanchen/rsrefseg
공식 코드RSRefSeg: Referring Remote Sensing Image Segmentation with Foundation Models
https://github.com/kyanchen/rsrefseg2
공식 코드RSRefSeg 2: Decoupling Referring Remote Sensing Image Segmentation with Foundation Models
https://github.com/kytimmylai/dfuc2022
공식 코드https://github.com/kyusik-cho/cmom
공식 코드Domain Adaptive Video Semantic Segmentation via Cross-Domain Moving Object Mixing
https://github.com/kyyle2114/convolutional-adapter-for-segment-anything
공식 코드CAD: Memory Efficient Convolutional Adapter for Segment Anything
https://github.com/l-yezhu/si-dial
공식 코드Supplementing Missing Visions via Dialog for Scene Graph Generations
https://github.com/l1997i/DurLAR
공식 코드On Deep Learning for Geometric and Semantic Scene Understanding Using On-Vehicle 3D LiDAR
https://github.com/lRomul/ball-action-spotting
공식 코드https://github.com/lab-smile/domino
공식 코드DOMINO: Domain-aware Model Calibration in Medical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.