Disentangled Representations for Short-Term and Long-Term Person Re-Identification

Abstract

We address the problem of person re-identification (reID), that is, retrieving person images from a large dataset, given a query image of the person of interest. A key challenge is to learn person representations robust to intra-class variations, as different persons could have the same attribute, and persons' appearances look different, e.g., with viewpoint changes. Recent reID methods focus on learning person features discriminative only for a particular factor of variations (e.g., human pose), which also requires corresponding supervisory signals (e.g., pose annotations). To tackle this problem, we propose to factorize person images into identity-related and unrelated features. Identity-related features contain information useful for specifying a particular person (e.g., clothing), while identity-unrelated ones hold other factors (e.g., human pose). To this end, we propose a new generative adversarial network, dubbed identity shuffle GAN (IS-GAN). It disentangles identity-related and unrelated features from person images through an identity-shuffling technique that exploits identification labels alone without any auxiliary supervisory signals. We restrict the distribution of identity-unrelated features or encourage the identity-related and unrelated features to be uncorrelated, facilitating the disentanglement process. Experimental results validate the effectiveness of IS-GAN, showing state-of-the-art performance on standard reID benchmarks, including Market-1501, CUHK03, and DukeMTMC-reID. We further demonstrate the advantages of disentangling person representations on a long-term reID task, setting a new state of the art on a Celeb-reID dataset.

arXiv admin note: substantial text overlap with arXiv:1910.12003

References in corpus (13)

• Adam: A Method for Stochastic Optimization
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Auto-Encoding Variational Bayes
• Instance Normalization: The Missing Ingredient for Fast Stylization
• In Defense of the Triplet Loss for Person Re-Identification
• Learning Discriminative Features with Multiple Granularities for Person Re-Identification
• Person Re-identification: Past, Present and Future
• Improving Person Re-identification by Attribute and Identity Learning
• AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
• FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification
• Measuring correlations between non-stationary series with DCCA coefficient
• Discovering Hidden Factors of Variation in Deep Networks
• Learning Shape Representations for Clothing Variations in Person Re-Identification

Cited by in corpus (5)

• Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification
• Content and Salient Semantics Collaboration for Cloth-Changing Person Re-Identification
• Cerberus: Attribute-based person re-identification using semantic IDs
• Image-Text-Image Knowledge Transfer for Lifelong Person Re-Identification with Hybrid Clothing States
• Digital Shielding for Cross-Domain Wi-Fi Signal Adaptation using Relativistic Average Generative Adversarial Network