Incomplete Descriptor Mining with Elastic Loss for Person Re-Identification

Abstract

In this paper, we propose a novel person Re-ID model, Consecutive Batch DropBlock Network (CBDB-Net), to capture the attentive and robust person descriptor for the person Re-ID task. The CBDB-Net contains two novel designs: the Consecutive Batch DropBlock Module (CBDBM) and the Elastic Loss (EL). In the Consecutive Batch DropBlock Module (CBDBM), we firstly conduct uniform partition on the feature maps. And then, we independently and continuously drop each patch from top to bottom on the feature maps, which can output multiple incomplete feature maps. In the training stage, these multiple incomplete features can better encourage the Re-ID model to capture the robust person descriptor for the Re-ID task. In the Elastic Loss (EL), we design a novel weight control item to help the Re-ID model adaptively balance hard sample pairs and easy sample pairs in the whole training process. Through an extensive set of ablation studies, we verify that the Consecutive Batch DropBlock Module (CBDBM) and the Elastic Loss (EL) each contribute to the performance boosts of CBDB-Net. We demonstrate that our CBDB-Net can achieve the competitive performance on the three standard person Re-ID datasets (the Market-1501, the DukeMTMC-Re-ID, and the CUHK03 dataset), three occluded Person Re-ID datasets (the Occluded DukeMTMC, the Partial-REID, and the Partial iLIDS dataset), and a general image retrieval dataset (In-Shop Clothes Retrieval dataset).

arXiv commentAcceped by IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)

References in corpus (20)

• Adam: A Method for Stochastic Optimization
• FaceNet: A Unified Embedding for Face Recognition and Clustering
• In Defense of the Triplet Loss for Person Re-Identification
• Improved Regularization of Convolutional Neural Networks with Cutout
• Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
• Person Re-identification: Past, Present and Future
• Improving Person Re-identification by Attribute and Identity Learning
• Random Erasing Data Augmentation
• Pedestrian Alignment Network for Large-scale Person Re-identification
• DropBlock: A regularization method for convolutional networks
• Deep Representation Learning with Part Loss for Person Re-Identification
• GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval
• FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification
• Deep Transfer Learning for Person Re-identification
• Recognizing Partial Biometric Patterns
• The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching
• Learning View-Specific Deep Networks for Person Re-Identification
• Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free Approach
• High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification
• Spatiotemporal Co-attention Recurrent Neural Networks for Human-Skeleton Motion Prediction

Cited by in corpus (1)

• Person Re-identification via Attention Pyramid