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Instance Segmentation

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하위 태스크: 17
평가표: 36
보존된 평가 기록: 338

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/liruilong940607/Pose2Seg

공식 코드

Pose2Seg: Detection Free Human Instance Segmentation

https://github.com/lishuhuai527/coco-unihuman

공식 코드

You Only Learn One Query: Learning Unified Human Query for Single-Stage Multi-Person Multi-Task Human-Centric Perception

https://github.com/liuxingbin/dbot

공식 코드

Exploring Target Representations for Masked Autoencoders

https://github.com/liuxy1103/GIDBIS

공식 코드

Graph Relation Distillation for Efficient Biomedical Instance Segmentation

https://github.com/liuxy1103/grdbis

공식 코드

Graph Relation Distillation for Efficient Biomedical Instance Segmentation

https://github.com/liuzhengzhe/One-Thing-One-Click

공식 코드

You Only Need One Thing One Click: Self-Training for Weakly Supervised 3D Scene Understanding

https://github.com/liwei109/sepvit

공식 코드

SepViT: Separable Vision Transformer

https://github.com/liwenchao0615/zerrinnet

공식 코드

Zero-Shot Enhancement of Low-Light Image Based on Retinex Decomposition

https://github.com/liwentomng/boxlevelset

공식 코드

Box-supervised Instance Segmentation with Level Set Evolution

https://github.com/lixiaotong97/mc-beit

공식 코드

mc-BEiT: Multi-choice Discretization for Image BERT Pre-training

https://github.com/lizhaoliu-lec/ccse

공식 코드

Instance Segmentation for Chinese Character Stroke Extraction, Datasets and Benchmarks

https://github.com/lkeab/BCNet

공식 코드

Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers

https://github.com/lluma/OCID-Ref

공식 코드

OCID-Ref: A 3D Robotic Dataset with Embodied Language for Clutter Scene Grounding

https://github.com/lmmmeng/overlock

공식 코드

OverLoCK: An Overview-first-Look-Closely-next ConvNet with Context-Mixing Dynamic Kernels

https://github.com/lmquan2000/mask-mixup

공식 코드

GUNNEL: Guided Mixup Augmentation and Multi-View Fusion for Aquatic Animal Segmentation

https://github.com/longkukuhi/armbench

공식 코드

RoboLLM: Robotic Vision Tasks Grounded on Multimodal Large Language Models

https://github.com/looooongChen/instance_segmentation_with_pixel_embeddings

공식 코드

Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints

https://github.com/looooongchen/instseg

공식 코드

Instance Segmentation of Dense and Overlapping Objects via Layering

https://github.com/looooongchen/sortedap

공식 코드

SortedAP: Rethinking evaluation metrics for instance segmentation

https://github.com/love6tao/aoi-overfitting-team

공식 코드

The Second-place Solution for CVPR VISION 23 Challenge Track 1 -- Data Effificient Defect Detection

https://github.com/lslrh/dynamask

공식 코드

DynaMask: Dynamic Mask Selection for Instance Segmentation

https://github.com/lslrh/sim

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SIM: Semantic-aware Instance Mask Generation for Box-Supervised Instance Segmentation

https://github.com/ltzovo/damamba

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DAMamba: Vision State Space Model with Dynamic Adaptive Scan

https://github.com/lucaswithai/visgraph

공식 코드

End-to-end video instance segmentation via spatial-temporal graph neural networks

https://github.com/lufanma/ifr

공식 코드

Implicit Feature Refinement for Instance Segmentation

https://github.com/luogen1996/MCN

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Multi-task Collaborative Network for Joint Referring Expression Comprehension and Segmentation

https://github.com/lutao2021/pidray

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PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detection

https://github.com/lxa9867/R2VOS

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Towards Robust Referring Video Object Segmentation with Cyclic Relational Consensus

https://github.com/lxtGH/BSSeg

공식 코드

BoundarySqueeze: Image Segmentation as Boundary Squeezing

https://github.com/lxtGH/GALD-Net

공식 코드

Global Aggregation then Local Distribution in Fully Convolutional Networks

https://github.com/lxtGH/SFSegNets

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Towards Efficient Scene Understanding via Squeeze Reasoning

https://github.com/lxtGH/TemporalPyramidRouting

공식 코드

Improving Video Instance Segmentation via Temporal Pyramid Routing

https://github.com/lxtgh/video-k-net

공식 코드

Video K-Net: A Simple, Strong, and Unified Baseline for Video Segmentation

https://github.com/lyclyc52/instance_nerf

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Instance Neural Radiance Field

https://github.com/lyndonzheng/VINV

공식 코드

Visiting the Invisible: Layer-by-Layer Completed Scene Decomposition

https://github.com/ma-xu/fcvit

공식 코드

A Close Look at Spatial Modeling: From Attention to Convolution

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.