A Global to Local Double Embedding Method for Multi-person Pose Estimation
Multi-person pose estimation is a fundamental and challenging problem to many computer vision tasks. Most existing methods can be broadly categorized into two classes: top-down and bottom-up methods. Both of the two types of methods involve two stages, namely, person detection and joints detection. Conventionally, the two stages are implemented separately without considering their interactions between them, and this may inevitably cause some issue intrinsically. In this paper, we present a novel method to simplify the pipeline by implementing person detection and joints detection simultaneously. We propose a Double Embedding (DE) method to complete the multi-person pose estimation task in a global-to-local way. DE consists of Global Embedding (GE) and Local Embedding (LE). GE encodes different person instances and processes information covering the whole image and LE encodes the local limbs information. GE functions for the person detection in top-down strategy while LE connects the rest joints sequentially which functions for joint grouping and information processing in A bottom-up strategy. Based on LE, we design the Mutual Refine Machine (MRM) to reduce the prediction difficulty in complex scenarios. MRM can effectively realize the information communicating between keypoints and further improve the accuracy. We achieve the competitive results on benchmarks MSCOCO, MPII and CrowdPose, demonstrating the effectiveness and generalization ability of our method.
Code (0)
등록된 구현이 없습니다.
Tasks
Human DetectionMulti-Person Pose EstimationPose EstimationSimilar Papers 제목 키워드 기반
Double-chain Constraints for 3D Human Pose Estimation in Images and Videos
Reconstructing 3D poses from 2D poses lacking depth information is particularly challenging due to the complexity and diversity of human motion. The key is to effectively model the spatial constraints between joints to l…
3D Human Pose EstimationMonocular 3D Human Pose EstimationPose EstimationFederated Recommendation with Additive Personalization
Building recommendation systems via federated learning (FL) is a new emerging challenge for advancing next-generation Internet service and privacy protection. Existing approaches train shared item embedding by FL while k…
Federated LearningRecommendation SystemsLGMRec: Local and Global Graph Learning for Multimodal Recommendation
The multimodal recommendation has gradually become the infrastructure of online media platforms, enabling them to provide personalized service to users through a joint modeling of user historical behaviors (e.g., purchas…
Graph EmbeddingGraph Learninghypergraph embeddingMultimodal RecommendationPersonalized Federated Knowledge Graph Embedding with Client-Wise Relation Graph
Federated Knowledge Graph Embedding (FKGE) has recently garnered considerable interest due to its capacity to extract expressive representations from distributed knowledge graphs, while concurrently safeguarding the priv…
Entity Embeddingsglobal-optimizationGraph EmbeddingKnowledge Graph Embedding+2Double Graphs Regularized Multi-view Subspace Clustering
Recent years have witnessed a growing academic interest in multi-view subspace clustering. In this paper, we propose a novel Double Graphs Regularized Multi-view Subspace Clustering (DGRMSC) method, which aims to harness…
ClusteringMulti-view Subspace Clustering