Papers with Code 아카이브
Human Parsing
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Human parsing is the task of segmenting a human image into different fine-grained semantic parts such as head, torso, arms and legs.
<span style="color:grey; opacity: 0.6">( Image credit: Multi-Human-Parsing (MHP) )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
IntegratedPIFu: Integrated Pixel Aligned Implicit Function for Single-view Human Reconstruction
Body Part-Based Representation Learning for Occluded Person Re-Identification
Semantic Structure Enhanced Contrastive Adversarial Hash Network for Cross-media Representation Learning
Google Coral-based edge computing person reidentification using human parsing combined with analytical method
RepParser: End-to-End Multiple Human Parsing with Representative Parts
Identity-Sensitive Knowledge Propagation for Cloth-Changing Person Re-identification
Combining human parsing with analytical feature extraction and ranking schemes for high-generalization person reidentification
AIParsing: Anchor-free Instance-level Human Parsing
Text2Human: Text-Driven Controllable Human Image Generation
Human-Centered Prior-Guided and Task-Dependent Multi-Task Representation Learning for Action Recognition Pre-Training
Versatile Multi-Modal Pre-Training for Human-Centric Perception
InvPT: Inverted Pyramid Multi-task Transformer for Dense Scene Understanding
Describe me if you can! Characterized Instance-level Human Parsing
CDGNet: Class Distribution Guided Network for Human Parsing
Clicking Matters:Towards Interactive Human Parsing
Image Comes Dancing with Collaborative Parsing-Flow Video Synthesis
Quality-Aware Network for Face Parsing
An Efficient Style Virtual Try on Network for Clothing Business Industry
End-to-end One-shot Human Parsing
RobustFusion: Robust Volumetric Performance Reconstruction under Human-object Interactions from Monocular RGBD Stream
AAformer: Auto-Aligned Transformer for Person Re-Identification
Human De-occlusion: Invisible Perception and Recovery for Humans
Quality-Aware Network for Human Parsing
Parser-Free Virtual Try-on via Distilling Appearance Flows
Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing
PISE: Person Image Synthesis and Editing with Decoupled GAN
Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer
Nondiscriminatory Treatment: a straightforward framework for multi-human parsing
AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing
Progressive One-shot Human Parsing
Renovating Parsing R-CNN for Accurate Multiple Human Parsing
Robust Person Re-Identification through Contextual Mutual Boosting
Affinity-aware Compression and Expansion Network for Human Parsing
Identity-Guided Human Semantic Parsing for Person Re-Identification
From Real to Synthetic and Back: Synthesizing Training Data for Multi-Person Scene Understanding
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