Papers with Code 아카이브
Human Instance Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 1
- 보존된 평가 기록
- 18
Instance segmentation is the task of detecting and delineating each distinct object of interest appearing in an image.
Image Credit: Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Crowd-SAM: SAM as a Smart Annotator for Object Detection in Crowded Scenes
You Only Learn One Query: Learning Unified Human Query for Single-Stage Multi-Person Multi-Task Human-Centric Perception
Object-Centric Multi-Task Learning for Human Instances
Test-time Adaptation vs. Training-time Generalization: A Case Study in Human Instance Segmentation using Keypoints Estimation
Humans need not label more humans: Occlusion Copy & Paste for Occluded Human Instance Segmentation
Occlusion-Aware Instance Segmentation via BiLayer Network Architectures
Invisible-to-Visible: Privacy-Aware Human Instance Segmentation using Airborne Ultrasound via Collaborative Learning Variational Autoencoder
Human Instance Segmentation and Tracking via Data Association and Single-stage Detector
Real-time Human-Centric Segmentation for Complex Video Scenes
Fashion-Guided Adversarial Attack on Person Segmentation
SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation
PoSeg: Pose-Aware Refinement Network for Human Instance Segmentation
Pose2Seg: Detection Free Human Instance Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.