Papers with Code 아카이브
Gait Recognition
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<span style="color:grey; opacity: 0.6">( Image credit: GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
KeyPoint Relative Position Encoding for Face Recognition
BigGait: Learning Gait Representation You Want by Large Vision Models
The Paradox of Motion: Evidence for Spurious Correlations in Skeleton-based Gait Recognition Models
QAGait: Revisit Gait Recognition from a Quality Perspective
HybridGait: A Benchmark for Spatial-Temporal Cloth-Changing Gait Recognition with Hybrid Explorations
A Large-Scale Re-identification Analysis in Sporting Scenarios: the Betrayal of Reaching a Critical Point
STRIDE: Single-video based Temporally Continuous Occlusion-Robust 3D Pose Estimation
Cross-Covariate Gait Recognition: A Benchmark
A Multi-Stage Adaptive Feature Fusion Neural Network for Multimodal Gait Recognition
You Can Run but not Hide: Improving Gait Recognition with Intrinsic Occlusion Type Awareness
GaitContour: Efficient Gait Recognition based on a Contour-Pose Representation
SkeletonGait: Gait Recognition Using Skeleton Maps
HiH: A Multi-modal Hierarchy in Hierarchy Network for Unconstrained Gait Recognition
POISE: Pose Guided Human Silhouette Extraction under Occlusions
Learning to Simplify Spatial-Temporal Graphs in Gait Analysis
InvKA: Gait Recognition via Invertible Koopman Autoencoder
Human Gait Recognition using Deep Learning: A Comprehensive Review
FastPoseGait: A Toolbox and Benchmark for Efficient Pose-based Gait Recognition
Parsing is All You Need for Accurate Gait Recognition in the Wild
TriGait: Aligning and Fusing Skeleton and Silhouette Gait Data via a Tri-Branch Network
Free Lunch for Gait Recognition: A Novel Relation Descriptor
GaitPT: Skeletons Are All You Need For Gait Recognition
PsyMo: A Dataset for Estimating Self-Reported Psychological Traits from Gait
Reducing Training Demands for 3D Gait Recognition with Deep Koopman Operator Constraints
Condition-Adaptive Graph Convolution Learning for Skeleton-Based Gait Recognition
GaitASMS: Gait Recognition by Adaptive Structured Spatial Representation and Multi-Scale Temporal Aggregation
Distillation-guided Representation Learning for Unconstrained Gait Recognition
GaitMorph: Transforming Gait by Optimally Transporting Discrete Codes
GaitFormer: Revisiting Intrinsic Periodicity for Gait Recognition
Hierarchical Spatio-Temporal Representation Learning for Gait Recognition
Watch Where You Head: A View-biased Domain Gap in Gait Recognition and Unsupervised Adaptation
GaitGCI: Generative Counterfactual Intervention for Gait Recognition
MetaGait: Learning to Learn an Omni Sample Adaptive Representation for Gait Recognition
GaitMPL: Gait Recognition with Memory-Augmented Progressive Learning
GaitGS: Temporal Feature Learning in Granularity and Span Dimension for Gait Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.