Learning Discriminative Features with Multiple Granularities for Person Re-Identification

Abstract

The combination of global and partial features has been an essential solution to improve discriminative performances in person re-identification (Re-ID) tasks. Previous part-based methods mainly focus on locating regions with specific pre-defined semantics to learn local representations, which increases learning difficulty but not efficient or robust to scenarios with large variances. In this paper, we propose an end-to-end feature learning strategy integrating discriminative information with various granularities. We carefully design the Multiple Granularity Network (MGN), a multi-branch deep network architecture consisting of one branch for global feature representations and two branches for local feature representations. Instead of learning on semantic regions, we uniformly partition the images into several stripes, and vary the number of parts in different local branches to obtain local feature representations with multiple granularities. Comprehensive experiments implemented on the mainstream evaluation datasets including Market-1501, DukeMTMC-reid and CUHK03 indicate that our method robustly achieves state-of-the-art performances and outperforms any existing approaches by a large margin. For example, on Market-1501 dataset in single query mode, we obtain a top result of Rank-1/mAP=96.6%/94.2% with this method after re-ranking.

VMRFANet: View-specific Multi-Receptive Field Attention Network for Person Re-identification

“…Results on Market-1501: Our method achieves the best result on mAP metric, and the second best on rank 1. It outperforms all other approaches except a strip-based method MGN (Wang et al, 2018b) on rank 1 metric. However, MGN incorporates three independent branches after stage 3 of the ResNet50 backbone to extract features with multi-granularity.…”

“…After refining part pooling, the extracted local features are jointly trained with classification losses and have been concatenated as the final feature. Lately, (Wang et al, 2018b) proposed a multi-branch network to combine global and partial features at different granularities. With the combination of classification and triplet losses, it pushed the re-ID performances to a new level compared with previous state-of-the-art methods.…”

“…DukeMTMC-reID rank 1 mAP rank 1 mAP SVDNet 82.3 62.1 76.7 56.8 PAN 82.8 63.4 71.6 51.5 MultiScale (Chen et al, 2017) 88.9 73.1 79.2 60.6 MLFN (Chang et al, 2018) 90.0 74.3 81.0 62.8 HA-CNN (Li et al, 2018) 91.2 75.7 80.5 63.8 Mancs (Wang et al, 2018a) 93.1 82.3 84.9 71.8 Attention-Driven (Yang et al, 2019) 94.9 86.4 86.0 74.5 PCB+RPP (Sun et al, 2018) 93.8 81.6 83.3 69.2 HPM (Fu et al, 2018) 94.2 82.7 86.6 74.3 MGN (Wang et al, 2018b) 95.7 86.9 88.7 78.4 VMRFANet(Ours) 95.5 88.1 88.9 80.0 Table 3: Comparison of results on CUHK03-labeled (CUHK03-L) and CUHK03-detected (CUHK03-D) with new protocol (Zhong et al, 2017a). The best results are in bold, while the numbers with underlines denote the second best.…”

“…Model CUHK03-L CUHK03-D rank 1 mAP rank 1 mAP SVDNet 40.9 37.8 41.5 37.3 MLFN (Chang et al, 2018) 54.7 49.2 52.8 47.8 HA-CNN (Li et al, 2018) 44.4 41.0 41.7 38.6 PCB+RPP (Sun et al, 2018) --63.7 57.5 MGN (Wang et al, 2018b) 68.0 67.4 68.0 66.0 MRFANet (Ours) 81.1 78.8 78.9 75.3…”

An Empirical Study of Person Re-Identification with Attributes

“…Broadly, the methods can be divided into two categories: deep representation learning and deep metric learning. The first aims at creating a discriminative feature representation for the images [8], [24], [25]. In [24], a robust feature embedding is learnt by training the model in multiple domains with domain guided dropout.…”