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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/fazhan-cs/dcp
공식 코드https://github.com/fdistinctions/ijcai18
공식 코드Empirical Analysis of Foundational Distinctions in Linked Open Data
https://github.com/fe1ixxu/vt-stower
공식 코드VAE based Text Style Transfer with Pivot Words Enhancement Learning
https://github.com/fedezocco/matvisiongluinh-pytorch_tensorflow
공식 코드Visual Material Characteristics Learning for Circular Healthcare
https://github.com/fedml-ai/fedcv
공식 코드FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks
https://github.com/fedortaggenbrock/self-supervised-partial-cycle-consistency
공식 코드Self-Supervised Partial Cycle-Consistency for Multi-View Matching
https://github.com/feibryantkit/lf-tracy
공식 코드LF Tracy: A Unified Single-Pipeline Approach for Salient Object Detection in Light Field Cameras
https://github.com/feibryantkit/oafuser
공식 코드OAFuser: Towards Omni-Aperture Fusion for Light Field Semantic Segmentation
https://github.com/feipanir/moda
공식 코드https://github.com/felixduelmer/dopus
공식 코드DopUS-Net: Quality-Aware Robotic Ultrasound Imaging based on Doppler Signal
https://github.com/fenghetan9/cmu-net
공식 코드CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network
https://github.com/fenghetan9/cmunext
공식 코드CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion
https://github.com/fenghetan9/hi-end-mae
공식 코드https://github.com/fenghetan9/llm4seg
공식 코드Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster
https://github.com/fenghetan9/mambamim
공식 코드https://github.com/fenghetan9/mobileutr
공식 코드https://github.com/fengjiang5/knowledge-distillation-from-cylinder3d-to-polarnet
공식 코드Knowledge Distillation from 3D to Bird's-Eye-View for LiDAR Semantic Segmentation
https://github.com/fengweie/uda_clr
공식 코드Unsupervised Domain Adaptive Fundus Image Segmentation with Category-level Regularization
https://github.com/fengzicai/awesomewmad
공식 코드https://github.com/fengzicai/s2s
공식 코드Shape2Scene: 3D Scene Representation Learning Through Pre-training on Shape Data
https://github.com/fengzicai/wmad-benchmarks
공식 코드https://github.com/fepegar/resseg-ijcars
공식 코드https://github.com/fferflo/semantic-meshes
공식 코드Improving Semantic Image Segmentation via Label Fusion in Semantically Textured Meshes
https://github.com/fgirbal/segment-select-correct
공식 코드Segment, Select, Correct: A Framework for Weakly-Supervised Referring Segmentation
https://github.com/fhgyuanshen/hybridgl
공식 코드https://github.com/fidshu/srsnet
공식 코드SRSNetwork: Siamese Reconstruction-Segmentation Networks based on Dynamic-Parameter Convolution
https://github.com/firesans/nonlatinphotoocr
공식 코드Towards Boosting the Accuracy of Non-Latin Scene Text Recognition
https://github.com/fjq-tongji/mllm-sul
공식 코드https://github.com/fkhan98/medical-image-segmentation-with-attention-based-residual-double-u-net
공식 코드AttResDU-Net: Medical Image Segmentation Using Attention-based Residual Double U-Net
https://github.com/fkluger/cuboids_revisited
공식 코드https://github.com/floatseg/floatseg.github.io
공식 코드FLOAT: Factorized Learning of Object Attributes for Improved Multi-object Multi-part Scene Parsing
https://github.com/flytxtds/scene-recognition
공식 코드Scene Recognition by Combining Local and Global Image Descriptors
https://github.com/fnzhan/Illumination-Estimation
공식 코드GMLight: Lighting Estimation via Geometric Distribution Approximation
https://github.com/foundationvision/uniref
공식 코드UniRef++: Segment Every Reference Object in Spatial and Temporal Spaces
https://github.com/fpv-iplab/mid-level-fusion-nav
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.