Papers with Code 아카이브
Instance Segmentation
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Instance Segmentation is a computer vision task that involves identifying and separating individual objects within an image, including detecting the boundaries of each object and assigning a unique label to each object. The goal of instance segmentation is to produce a pixel-wise segmentation map of the image, where each pixel is assigned to a specific object instance.
Image Credit: Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers, CVPR'21
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/minghanli/mdqe_cvpr2023
공식 코드MDQE: Mining Discriminative Query Embeddings to Segment Occluded Instances on Challenging Videos
https://github.com/minghanli/univs
공식 코드UniVS: Unified and Universal Video Segmentation with Prompts as Queries
https://github.com/mingliangzhang2018/PGDP
공식 코드https://github.com/minhquanlecs/maskdiff
공식 코드https://github.com/miquel-espinosa/no-time-to-train
공식 코드No time to train! Training-Free Reference-Based Instance Segmentation
https://github.com/miranheo/genvis
공식 코드https://github.com/miriambellver/refvos
공식 코드RefVOS: A Closer Look at Referring Expressions for Video Object Segmentation
https://github.com/mit-han-lab/efficientvit
공식 코드EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
https://github.com/mkang315/asf-yolo
공식 코드ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentation
https://github.com/mkimhi/RISE
공식 코드Robot Instance Segmentation with Few Annotations for Grasping
https://github.com/mlpc-ucsd/CoaT
공식 코드https://github.com/mlpc-ucsd/maskclip
공식 코드https://github.com/mmunir127/rapidnet-official
공식 코드RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone
https://github.com/mmv-lab/mmv_im2im
공식 코드MMV_Im2Im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation
https://github.com/mohammedsb/harmony
공식 코드https://github.com/mrinal054/instance_teeth_segmentation
공식 코드A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-rays
https://github.com/mseg-dataset/mseg-semantic
공식 코드MSeg: A Composite Dataset for Multi-domain Semantic Segmentation
https://github.com/msiam/pixfoundation
공식 코드PixFoundation: Are We Heading in the Right Direction with Pixel-level Vision Foundation Models?
https://github.com/mttr2021/MTTR
공식 코드End-to-End Referring Video Object Segmentation with Multimodal Transformers
https://github.com/muhammad-ibraheem-siddiqui/persense
공식 코드PerSense: Personalized Instance Segmentation in Dense Images
https://github.com/murufeng/EPSANet
공식 코드EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network
https://github.com/naivete5656/WSISPDR
공식 코드Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response
https://github.com/nanh5837/lettucemots
공식 코드https://github.com/naver-ai/maskris
공식 코드MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation
https://github.com/naver-ai/rdnet
공식 코드https://github.com/naver-ai/relabel_imagenet
공식 코드Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels
https://github.com/naver-ai/vidt
공식 코드An Extendable, Efficient and Effective Transformer-based Object Detector
https://github.com/navidstuv/NuClick
공식 코드NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images
https://github.com/neel-dey/anystar
공식 코드AnyStar: Domain randomized universal star-convex 3D instance segmentation
https://github.com/neo85824/epsnet
공식 코드EPSNet: Efficient Panoptic Segmentation Network with Cross-layer Attention Fusion
https://github.com/nerminsamet/houghnet
공식 코드HoughNet: Integrating near and long-range evidence for visual detection
https://github.com/next-chatv/next-chat
공식 코드https://github.com/nguyendinhson-kaist/mmsports23-seg-autoid
공식 코드Instance Segmentation under Occlusions via Location-aware Copy-Paste Data Augmentation
https://github.com/ngzhili/syntable
공식 코드https://github.com/nii-yamagishilab/fashion_adv
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.