Paper Reading AI Learner

Devil's in the Detail: Graph-based Key-point Alignment and Embedding for Person Re-ID

2020-09-11 06:28:56
Xinyang Jiang, Fufu Yu, Yifei Gong, Shizhen Zhao, Xiaowei Guo, Feiyue Huang, Wei-Shi Zheng, Xing Sun

Abstract

Although Person Re-Identification has made impressive progress, difficult cases like occlusion, change of view-point and similar clothing still bring great challenges. Besides overall visual features, matching and comparing detailed local information is also essential for tackling these challenges. This paper proposes two key recognition patterns to better utilize the local information of pedestrian images. From the spatial perspective, the model should be able to select and align key-points from the image pairs for comparison (i.e. key-points alignment). From the perspective of feature channels, the feature of a query image should be dynamically adjusted based on the gallery image it needs to match (i.e. conditional feature embedding). Most of the existing methods are unable to satisfy both key-point alignment and conditional feature embedding. By introducing novel techniques including correspondence attention module and discrepancy-based GCN, we propose an end-to-end ReID method that integrates both patterns into a unified framework, called Siamese-GCN. The experiments show that Siamese-GCN achieves state-of-the-art performance on three public datasets.

Abstract (translated)

URL

https://arxiv.org/abs/2009.05250

PDF

https://arxiv.org/pdf/2009.05250.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot