Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)

Abstract

Employing part-level features for pedestrian image description offers fine-grained information and has been verified as beneficial for person retrieval in very recent literature. A prerequisite of part discovery is that each part should be well located. Instead of using external cues, e.g., pose estimation, to directly locate parts, this paper lays emphasis on the content consistency within each part. Specifically, we target at learning discriminative part-informed features for person retrieval and make two contributions. (i) A network named Part-based Convolutional Baseline (PCB). Given an image input, it outputs a convolutional descriptor consisting of several part-level features. With a uniform partition strategy, PCB achieves competitive results with the state-of-the-art methods, proving itself as a strong convolutional baseline for person retrieval. (ii) A refined part pooling (RPP) method. Uniform partition inevitably incurs outliers in each part, which are in fact more similar to other parts. RPP re-assigns these outliers to the parts they are closest to, resulting in refined parts with enhanced within-part consistency. Experiment confirms that RPP allows PCB to gain another round of performance boost. For instance, on the Market-1501 dataset, we achieve (77.4+4.2)% mAP and (92.3+1.5)% rank-1 accuracy, surpassing the state of the art by a large margin.

References in corpus (10)

• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• R-FCN: Object Detection via Region-based Fully Convolutional Networks
• In Defense of the Triplet Loss for Person Re-Identification
• Person Re-identification: Past, Present and Future
• Pedestrian Alignment Network for Large-scale Person Re-identification
• Deep Representation Learning with Part Loss for Person Re-Identification
• GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval
• Deep Transfer Learning for Person Re-identification
• Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification
• Pose Invariant Embedding for Deep Person Re-identification

Cited by in corpus (20)

• MHSA-Net: Multi-Head Self-Attention Network for Occluded Person Re-Identification
• Recognizing Partial Biometric Patterns
• Parameter-Free Spatial Attention Network for Person Re-Identification
• Horizontal Pyramid Matching for Person Re-identification
• Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification
• Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification
• Person Re-Identification by Semantic Region Representation and Topology Constraint
• A review of Recent Techniques for Person Re-Identification
• Spatial and Temporal Mutual Promotion for Video-based Person Re-identification
• Spatial-Temporal Person Re-identification
• Attributes-aided Part Detection and Refinement for Person Re-identification
• Identity Preserving Generative Adversarial Network for Cross-Domain Person Re-identification
• In Defense of the Classification Loss for Person Re-Identification
• 3D Human Texture Estimation from a Single Image with Transformers
• Domain Adaptive Person Re-Identification via Camera Style Generation and Label Propagation
• Progressive Learning Algorithm for Efficient Person Re-Identification
• SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-Identification
• Sparse Label Smoothing Regularization for Person Re-Identification
• Cross-Resolution Person Re-identification with Deep Antithetical Learning
• Learning Incremental Triplet Margin for Person Re-identification