https://github.com/bscs12/bussam
공식 코드Ultrasound SAM Adapter: Adapting SAM for Breast Lesion Segmentation in Ultrasound Images
Papers with Code 아카이브
2D Semantic Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/buaahugegun/dcfm
공식 코드Deep Common Feature Mining for Efficient Video Semantic Segmentation
https://github.com/buaavrcg/LEGaussians
공식 코드Language Embedded 3D Gaussians for Open-Vocabulary Scene Understanding
https://github.com/bupt-ai-cz/Meta-SelfLearning
공식 코드Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark
https://github.com/bupt-ai-cz/wuda
공식 코드WUDA: Unsupervised Domain Adaptation Based on Weak Source Domain Labels
https://github.com/buxiangzhiren/vd-it
공식 코드Exploring Pre-trained Text-to-Video Diffusion Models for Referring Video Object Segmentation
https://github.com/bwgzk/cyclemix
공식 코드CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision
https://github.com/bwittmann/vesselFM
공식 코드vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation
https://github.com/bx0903/pdc
공식 코드Parameter Decoupling Strategy for Semi-supervised 3D Left Atrium Segmentation
https://github.com/bxiang233/panopticsegforlargescalepointcloud
공식 코드Towards accurate instance segmentation in large-scale LiDAR point clouds
https://github.com/bxiang233/panopticsegformobilemappingpointclouds
공식 코드A Review of Panoptic Segmentation for Mobile Mapping Point Clouds
https://github.com/by-liu/confkd
공식 코드Segmentation with mixed supervision: Confidence maximization helps knowledge distillation
https://github.com/bytedance/MTVQA
공식 코드MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering
https://github.com/bytedance/e2str
공식 코드Multi-modal In-Context Learning Makes an Ego-evolving Scene Text Recognizer
https://github.com/c-a-schiller/var4arl
공식 코드https://github.com/caganselim/tltm
공식 코드Two-Level Temporal Relation Model for Online Video Instance Segmentation
https://github.com/cai4cai/ace-dliris
공식 코드Average Calibration Error: A Differentiable Loss for Improved Reliability in Image Segmentation
https://github.com/caijie0620/openrr-1k
공식 코드OpenRR-1k: A Scalable Dataset for Real-World Reflection Removal
https://github.com/caipeide/VTGNet
공식 코드VTGNet: A Vision-based Trajectory Generation Network for Autonomous Vehicles in Urban Environments
https://github.com/caipengzhou/brau-netplusplus
공식 코드BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation
https://github.com/cake-lab/PointAR
공식 코드PointAR: Efficient Lighting Estimation for Mobile Augmented Reality
https://github.com/cake-lab/Xihe
공식 코드Xihe: A 3D Vision-based Lighting Estimation Framework for Mobile Augmented Reality
https://github.com/camma-public/surglatentgraph
공식 코드Optimizing Latent Graph Representations of Surgical Scenes for Zero-Shot Domain Transfer
https://github.com/canjie-luo/real-300k
공식 코드https://github.com/caoluyang0830/cvbm
공식 코드https://github.com/caoql98/frinet
공식 코드Few-Shot Rotation-Invariant Aerial Image Semantic Segmentation
https://github.com/carrenD/ummkd
공식 코드Unpaired Multi-modal Segmentation via Knowledge Distillation
https://github.com/carrierlxk/AGNN
공식 코드Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks
https://github.com/carrierlxk/COSNet
공식 코드See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks
https://github.com/carrierlxk/GraphMemVOS
공식 코드Video Object Segmentation with Episodic Graph Memory Networks
https://github.com/carrierlxk/MuG
공식 코드https://github.com/casia-iva-lab/fastsam
공식 코드https://github.com/casia-iva-lab/zbs
공식 코드https://github.com/catalina17/VideoNavQA
공식 코드VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering
https://github.com/catherine-r-he/hetnet
공식 코드Efficient Mirror Detection via Multi-level Heterogeneous Learning
https://github.com/cccccczh404/h-sam
공식 코드Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.