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Semantic Segmentation
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/akshay107/mimic-and-fool
공식 코드https://github.com/akuxcw/GridMask
공식 코드https://github.com/akvilonBrown/sme-metadata
공식 코드Metadata Improves Segmentation Through Multitasking Elicitation
https://github.com/al3x-o-o-hung/csam
공식 코드CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image Segmentation
https://github.com/alainjungo/reliability-challenges-uncertainty
공식 코드Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation
https://github.com/alanlukezic/d3s
공식 코드https://github.com/alanwxz/nuc-net
공식 코드NUC-Net: Non-uniform Cylindrical Partition Network for Efficient LiDAR Semantic Segmentation
https://github.com/albert-jin/cvt-assd
공식 코드CvT-ASSD: Convolutional vision-Transformer Based Attentive Single Shot MultiBox Detector
https://github.com/aldipiroli/semantic_spray_dataset
공식 코드Energy-based Detection of Adverse Weather Effects in LiDAR Data
https://github.com/aldrich2y/mianet
공식 코드MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation
https://github.com/aleemsidra/SaLIP
공식 코드Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation
https://github.com/alexander-g/inbd
공식 코드https://github.com/alexanderhberger/topo-pitfalls
공식 코드https://github.com/alexanderjaus/PPS
공식 코드https://github.com/alexdenker/eit_fenicsx
공식 코드https://github.com/alexgraikos/diffusion_priors
공식 코드https://github.com/alexmelekhin/mssplace
공식 코드MSSPlace: Multi-Sensor Place Recognition with Visual and Text Semantics
https://github.com/alextraveling/tomatoscanner
공식 코드TomatoScanner: phenotyping tomato fruit based on only RGB image
https://github.com/alexyouxin/explicit-shape-priors
공식 코드Learning with Explicit Shape Priors for Medical Image Segmentation
https://github.com/alexyouxin/slord-verse
공식 코드SLoRD: Structural Low-Rank Descriptors for Shape Consistency in Vertebrae Segmentation
https://github.com/alfredqin/db-sam
공식 코드DB-SAM: Delving into High Quality Universal Medical Image Segmentation
https://github.com/ali2500/burst-benchmark
공식 코드BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Video
https://github.com/alibaba-damo-academy/alice
공식 코드https://github.com/alibaba-damo-academy/ct-sam3d
공식 코드https://github.com/alibaba-damo-academy/med_query
공식 코드Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embedding
https://github.com/alibaba-damo-academy/pixel-lesion-patient-network
공식 코드Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Network
https://github.com/alibaba-damo-academy/self-supervised-anatomical-embedding-v2
공식 코드SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images
https://github.com/alibaba/uvosam
공식 코드UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything Model
https://github.com/aliborji/spatial_attention
공식 코드Addressing a fundamental limitation in deep vision models: lack of spatial attention
https://github.com/alimohammadiamirhossein/smite
공식 코드https://github.com/alinlab/ifseg
공식 코드IFSeg: Image-free Semantic Segmentation via Vision-Language Model
https://github.com/alinlab/selfpatch
공식 코드Patch-level Representation Learning for Self-supervised Vision Transformers
https://github.com/alipga/AMM_VOS
공식 코드https://github.com/alirezaazadbakht/kernel-sharing
공식 코드Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharing
https://github.com/alitabet/morton-net
공식 코드MortonNet: Self-Supervised Learning of Local Features in 3D Point Clouds
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