567 research outputs found
Learning Discriminative Features with Multiple Granularities for Person Re-Identification
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 has robustly achieved
state-of-the-art performances and outperformed any existing approaches by a
large margin. For example, on Market-1501 dataset in single query mode, we
achieve a state-of-the-art result of Rank-1/mAP=96.6%/94.2% after re-ranking.Comment: 9 pages, 5 figures. To appear in ACM Multimedia 201
Learning large margin multiple granularity features with an improved siamese network for person re-identification
Person re-identification (Re-ID) is a non-overlapping multi-camera retrieval task to match different images of the same person, and it has become a hot research topic in many fields, such as surveillance security, criminal investigation, and video analysis. As one kind of important architecture for person re-identification, Siamese networks usually adopt standard softmax loss function, and they can only obtain the global features of person images, ignoring the local features and the large margin for classification. In this paper, we design a novel symmetric Siamese network model named Siamese Multiple Granularity Network (SMGN), which can jointly learn the large margin multiple granularity features and similarity metrics for person re-identification. Firstly, two branches for global and local feature extraction are designed in the backbone of the proposed SMGN model, and the extracted features are concatenated together as multiple granularity features of person images. Then, to enhance their discriminating ability, the multiple channel weighted fusion (MCWF) loss function is constructed for the SMGN model, which includes the verification loss and identification loss of the training image pair. Extensive comparative experiments on four benchmark datasets (CUHK01, CUHK03, Market-1501 and DukeMTMC-reID) show the effectiveness of our proposed method and its performance outperforms many state-of-the-art methods
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