https://github.com/AlbertSuarez/object-cut
공식 코드Boundary-Aware Segmentation Network for Mobile and Web Applications
Papers with Code 아카이브
Camouflaged Object Segmentation
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Camouflaged object segmentation (COS) or Camouflaged object detection (COD), which was originally promoted by T.-N. Le et al. (2017), aims to identify objects that conceal their texture into the surrounding environment. The high intrinsic similarities between the target object and the background make COS/COD far more challenging than the traditional object segmentation task. Also, refer to the online benchmarks on CAMO dataset, COD dataset, and online demo.
<span style="color:grey; opacity: 0.6">( Image source: Anabranch Network for Camouflaged Object Segmentation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/DengPingFan/PraNet
공식 코드PraNet: Parallel Reverse Attention Network for Polyp Segmentation
https://github.com/GewelsJI/SINet-V2
공식 코드https://github.com/JingZhang617/COD-Rank-Localize-and-Segment
공식 코드Simultaneously Localize, Segment and Rank the Camouflaged Objects
https://github.com/Jun-Pu/UCOS-DA
공식 코드Unsupervised Camouflaged Object Segmentation as Domain Adaptation
https://github.com/Mhaiyang/CVPR2021_PFNet
공식 코드https://github.com/MrGiovanni/Nested-UNet
공식 코드UNet++: A Nested U-Net Architecture for Medical Image Segmentation
https://github.com/NathanUA/BASNet
공식 코드Boundary-Aware Segmentation Network for Mobile and Web Applications
https://github.com/ben57882/c2fnet-tscvt
공식 코드Camouflaged Object Detection via Context-aware Cross-level Fusion
https://github.com/cnyvfang/multicos
공식 코드Integrating Extra Modality Helps Segmentor Find Camouflaged Objects Well
https://github.com/cnyvfang/unicos
공식 코드Integrating Extra Modality Helps Segmentor Find Camouflaged Objects Well
https://github.com/danhntd/FS-CDIS
공식 코드The Art of Camouflage: Few-Shot Learning for Animal Detection and Segmentation
https://github.com/fupiao1998/TrasformerSOD
공식 코드Generative Transformer for Accurate and Reliable Salient Object Detection
https://github.com/fupiao1998/transformersod
공식 코드Generative Transformer for Accurate and Reliable Salient Object Detection
https://github.com/geshang777/FOCUS
공식 코드https://github.com/gewelsji/errnet
공식 코드Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network
https://github.com/huaaaliu/rgbx_semantic_segmentation
공식 코드CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
https://github.com/intcomp/camouflaged-vlm
공식 코드Open-Vocabulary Camouflaged Object Segmentation with Cascaded Vision Language Models
https://github.com/jyLin8100/GenSAM
공식 코드https://github.com/lartpang/ovcamo
공식 코드https://github.com/lartpang/zoomnet
공식 코드Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection
https://github.com/lartpang/zoomnext
공식 코드ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection
https://github.com/lwpyh/ProMaC_code
공식 코드Leveraging Hallucinations to Reduce Manual Prompt Dependency in Promptable Segmentation
https://github.com/lyu-yx/acumen
공식 코드https://github.com/nifangbaage/explicit-visual-prompt
공식 코드Explicit Visual Prompting for Low-Level Structure Segmentations
https://github.com/sdy1999/eamnet
공식 코드https://github.com/thograce/C2FNet
공식 코드Context-aware Cross-level Fusion Network for Camouflaged Object Detection
https://github.com/weathon/vcos
공식 코드ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object Segmentation
https://github.com/weijun88/F3Net
공식 코드F3Net: Fusion, Feedback and Focus for Salient Object Detection
https://github.com/wuzhe71/CPD
공식 코드Cascaded Partial Decoder for Fast and Accurate Salient Object Detection
https://github.com/ycyinchao/rdvp-msd
공식 코드https://github.com/zhengpeng7/birefnet
공식 코드Bilateral Reference for High-Resolution Dichotomous Image Segmentation
https://github.com/zhoustan/camsam2
공식 코드https://github.com/zhoustan/sam2-vcos
공식 코드When SAM2 Meets Video Camouflaged Object Segmentation: A Comprehensive Evaluation and Adaptation
https://dagshub.com/Bharat-mtr/EGNet
https://github.com/1044197988/TF.Keras-Commonly-used-models
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.