https://github.com/DLTK/models
공식 코드NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines
Papers with Code 아카이브
2D Semantic Segmentation
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/DTaoo/Multimodal-Aerial-Scene-Recognition
공식 코드Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition
https://github.com/Danial-Qashqai/CSFNet
공식 코드https://github.com/Daniyanaj/vMixer
공식 코드https://github.com/DarthReca/magnifier-california
공식 코드Magnifier: A Multi-grained Neural Network-based Architecture for Burned Area Delineation
https://github.com/Dayan-Guan/DA-VSN
공식 코드Domain Adaptive Video Segmentation via Temporal Consistency Regularization
https://github.com/DebeshJha/2020-CBMS-DoubleU-Net
공식 코드DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
https://github.com/DebeshJha/ResUNetplusplus
공식 코드ResUNet++: An Advanced Architecture for Medical Image Segmentation
https://github.com/DerrickWang005/CRIS.pytorch
공식 코드https://github.com/DiffusionLight/DiffusionLight
공식 코드DiffusionLight: Light Probes for Free by Painting a Chrome Ball
https://github.com/Digital-Dermatology/t-loss
공식 코드https://github.com/DinhHieuHoang/SAM3D
공식 코드https://github.com/DirtyHarryLYL/HAKE-Action-Torch
공식 코드HAKE: A Knowledge Engine Foundation for Human Activity Understanding
https://github.com/Dmmm1997/DeRIS
공식 코드https://github.com/Dong-JinKim/DenseRelationalCaptioning
공식 코드Dense Relational Image Captioning via Multi-task Triple-Stream Networks
https://github.com/DreamtaleCore/SGRRN
공식 코드https://github.com/DriveVLA/OpenDriveVLA
공식 코드OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model
https://github.com/EagleMIT/EMKD
공식 코드Efficient Medical Image Segmentation Based on Knowledge Distillation
https://github.com/Earth-Intelligence-Lab/vleo-bench
공식 코드Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data
https://github.com/Elbria/xformal-FoST
공식 코드XFORMAL: A Benchmark for Multilingual Formality Style Transfer
https://github.com/EmbodiedVision/cosection
공식 코드Where Does It End? -- Reasoning About Hidden Surfaces by Object Intersection Constraints
https://github.com/EmbodiedVision/slot_attention_normalization
공식 코드Attention Normalization Impacts Cardinality Generalization in Slot Attention
https://github.com/EmmaW8/BEAL
공식 코드Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation
https://github.com/Engineering-Course/CIHP_PGN
공식 코드https://github.com/FGNN-Author/FGNN
공식 코드https://github.com/FacePerceiver/FaRL
공식 코드General Facial Representation Learning in a Visual-Linguistic Manner
https://github.com/Factral/UnMix-NeRF
공식 코드https://github.com/FangShancheng/ABINet
공식 코드https://github.com/FengheTan9/Medical-Image-Segmentation-Benchmarks
공식 코드CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion
https://github.com/FengheTan9/Multi-Level-Global-Context-Cross-Consistency
공식 코드https://github.com/FightingFighting/GPS
공식 코드Gradient-based Parameter Selection for Efficient Fine-Tuning
https://github.com/ForawardStar/HIPe
공식 코드Hierarchical Image Peeling: A Flexible Scale-space Filtering Framework
https://github.com/FreeformRobotics/BORM
공식 코드BORM: Bayesian Object Relation Model for Indoor Scene Recognition
https://github.com/FreeformRobotics/OTS
공식 코드Object-to-Scene: Learning to Transfer Object Knowledge to Indoor Scene Recognition
https://github.com/GAP-LAB-CUHK-SZ/SAMPro3D
공식 코드SAMPro3D: Locating SAM Prompts in 3D for Zero-Shot Scene Segmentation
https://github.com/GAP-LAB-CUHK-SZ/TO-Scene
공식 코드TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.