https://github.com/xiaoshuihuang/epcl
공식 코드EPCL: Frozen CLIP Transformer is An Efficient Point Cloud Encoder
Papers with Code 아카이브
Semantic Segmentation
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xiaow-liu/sdcfusion
공식 코드Rethinking the Evaluation of Visible and Infrared Image Fusion
https://github.com/xiaoyao3302/2bdenoise
공식 코드Clean Label Disentangling for Medical Image Segmentation with Noisy Labels
https://github.com/xiaoyao3302/CCVC
공식 코드Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation
https://github.com/xiarho/cmda
공식 코드CMDA: Cross-Modality Domain Adaptation for Nighttime Semantic Segmentation
https://github.com/xiarho/madm
공식 코드Unsupervised Modality Adaptation with Text-to-Image Diffusion Models for Semantic Segmentation
https://github.com/xiarho/semat
공식 코드Towards Natural Image Matting in the Wild via Real-Scenario Prior
https://github.com/xiechenxi99/mass13k
공식 코드https://github.com/xieenze/PolarMask
공식 코드PolarMask: Single Shot Instance Segmentation with Polar Representation
https://github.com/xieenze/Segment_Transparent_Objects
공식 코드https://github.com/xieenze/Trans2Seg
공식 코드https://github.com/xiekkki/motionbev
공식 코드https://github.com/xilin1991/CluterNet
공식 코드Online Unsupervised Video Object Segmentation via Contrastive Motion Clustering
https://github.com/xilin1991/IMCNet
공식 코드Implicit Motion-Compensated Network for Unsupervised Video Object Segmentation
https://github.com/xincheng-yang/pointcat
공식 코드https://github.com/xing0047/rewrite
공식 코드Rewrite Caption Semantics: Bridging Semantic Gaps for Language-Supervised Semantic Segmentation
https://github.com/xing0047/tps
공식 코드Domain Adaptive Video Segmentation via Temporal Pseudo Supervision
https://github.com/xinge008/Cylinder3D
공식 코드Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation
https://github.com/xingmingyu123456/knowledgeswapping
공식 코드https://github.com/xingqunqi-lab/gkd-framework
공식 코드Exploring Generalizable Distillation for Efficient Medical Image Segmentation
https://github.com/xingyizhou/UniDet
공식 코드https://github.com/xingyul/meteornet
공식 코드MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences
https://github.com/xingyumex/alen
공식 코드ALEN: A Dual-Approach for Uniform and Non-Uniform Low-Light Image Enhancement
https://github.com/xingyumex/unir-net
공식 코드https://github.com/xinkunwang111/promise
공식 코드https://github.com/xinliu20/mec
공식 코드https://github.com/xinntao/SFTGAN
공식 코드Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform
https://github.com/xinyu-laura/mmuda
공식 코드https://github.com/xiweix/reme
공식 코드ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation
https://github.com/xjiangmed/ltuda
공식 코드https://github.com/xjock/deeplebv3plus-hedplus
공식 코드https://github.com/xjqicuhk/SIMS
공식 코드https://github.com/xjtu-cs-gao/satforhdmap
공식 코드Complementing Onboard Sensors with Satellite Map: A New Perspective for HD Map Construction
https://github.com/xjwu1024/WPS-SAM
공식 코드WPS-SAM: Towards Weakly-Supervised Part Segmentation with Foundation Models
https://github.com/xmed-lab/AllSpark
공식 코드https://github.com/xmed-lab/GenericSSL
공식 코드Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.