Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification

Abstract

Person re-identification (re-ID) requires rapid, flexible yet discriminant representations to quickly generalize to unseen observations on-the-fly and recognize the same identity across disjoint camera views. Recent effective methods are developed in a pair-wise similarity learning system to detect a fixed set of features from distinct regions which are mapped to their vector embeddings for the distance measuring. However, the most relevant and crucial parts of each image are detected independently without referring to the dependency conditioned on one and another. Also, these region based methods rely on spatial manipulation to position the local features in comparable similarity measuring. To combat these limitations, in this paper we introduce the Deep Co-attention based Comparators (DCCs) that fuse the co-dependent representations of the paired images so as to focus on the relevant parts of both images and produce their \textit{relative representations}. Given a pair of pedestrian images to be compared, the proposed model mimics the foveation of human eyes to detect distinct regions concurrent on both images, namely co-dependent features, and alternatively attend to relevant regions to fuse them into the similarity learning. Our comparator is capable of producing dynamic representations relative to a particular sample every time, and thus well-suited to the case of re-identifying pedestrians on-the-fly. We perform extensive experiments to provide the insights and demonstrate the effectiveness of the proposed DCCs in person re-ID. Moreover, our approach has achieved the state-of-the-art performance on three benchmark data sets: DukeMTMC-reID \cite{DukeMTMC}, CUHK03 \cite{FPNN}, and Market-1501 \cite{Market1501}.

References in corpus (14)

• Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
• DRAW: A Recurrent Neural Network For Image Generation
• Matching Networks for One Shot Learning
• Pedestrian Alignment Network for Large-scale Person Re-identification
• A Simple Way to Initialize Recurrent Networks of Rectified Linear Units
• Dynamic Coattention Networks For Question Answering
• Multi-View Spectral Clustering via Structured Low-Rank Matrix Factorization
• Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
• Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval
• Pose Invariant Embedding for Deep Person Re-identification
• What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification
• SVDNet for Pedestrian Retrieval
• Attentive Recurrent Comparators
• Crossing Generative Adversarial Networks for Cross-View Person Re-identification

Cited by in corpus (1)

• Sparse Label Smoothing Regularization for Person Re-Identification