Adaptive Re-ranking Of Deep Feature For Person Re-identification

Abstract

Typical person re-identification (re-ID) methods train a deep CNN to extract deep features and combine them with a distance metric for the final evaluation. In this work, we focus on exploiting the full information encoded in the deep feature to boost the re-ID performance. First, we propose a Deep Feature Fusion (DFF) method to exploit the diverse information embedded in a deep feature. DFF treats each sub-feature as an information carrier and employs a diffusion process to exchange their information. Second, we propose an Adaptive Re-Ranking (ARR) method to exploit the contextual information encoded in the features of neighbors. ARR utilizes the contextual information to re-rank the retrieval results in an iterative manner. Particularly, it adds more contextual information after each iteration automatically to consider more matches. Third, we propose a strategy that combines DFF and ARR to enhance the performance. Extensive comparative evaluations demonstrate the superiority of the p

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyliu2018adaptive

Related papers

• Neighbor-based Feature And Index Enhancement For Person Re-identification (2025)0.00
• Deep-person: Learning Discriminative Deep Features For Person Re-identification (2017)16.90
• Ensemble Feature For Person Re-identification (2019)0.00
• Maskreid: A Mask Based Deep Ranking Neural Network For Person Re-identification (2018)0.00
• Advancing Person Re-identification: Tensor-based Feature Fusion And Multilinear Subspace Learning (2023)2.26
• Graph Convolution For Re-ranking In Person Re-identification (2021)8.35
• Deep Co-attention Based Comparators For Relative Representation Learning In Person Re-identification (2018)13.34
• Structured Deep Hashing With Convolutional Neural Networks For Fast Person Re-identification (2017)12.87