An Efficient Multi-Scale Focusing Attention Network for Person Re-Identification
Abstract
:1. Introduction
- The Multi-Scale Focusing Attention Network (MSFANet), containing receptive fields of different sizes in each stage as focusing attention (FA) block, is proposed to capture multiple scales and highly focusing attention ReID features. It is a novel efficient network module that achieves lower computational complexity and can be flexibly embedded in a person ReID framework.
- We design a new fusion loss function to address hard sample discrimination problems, which is a combination of softmax loss and hard triplet loss. It achieves faster convergence and better generalization ability of the proposed MSFANet.
2. Related Work
2.1. Person ReID and Attention Mechanism
2.2. Lightweight Network Designs
2.3. Multi-Scale Feature Fusion Learning
3. Methods
3.1. Pipeline
3.2. Data Augmentation
3.3. Multi-Scale Focusing Attention Network
3.4. Loss Function
4. Experiments
4.1. Implementation Details
4.2. Results
- Model complexity analysis. The complexity of ReID model, affecting the training and computational cost, plays a vital role in the edge deployment. From the last column of Table 4, it can be observed that MSFANet outperforms most published methods about the model’s complexity by a clear margin. The parameters and GFLOPs of MSFANet are calculated on resized input images by 256*128, and we can see that the GFLOPs is only 0.82 when the model’s backbone is `1-2-3’ form, decreased by 18.2% from OSNet [27]. This design verifies the effectiveness of the multi-scale focusing attention network for ReID by applying a depthwise separable convolution mechanism. However, as the backbone form is set as `2-2-2’, the GFLOPs will be increased to 0.94, almost equal to OSNet’s. Thus, MSFANet’s backbone’s structural design also plays an important role in the model’s computational complexity. Compared to other models, MSFANet is a lightweight model, with significantly smaller parameters and floating-point computations than other heavyweight models (such as ResNet for the backbone). It performs better even compared with OSNet, regarded as a state-of-the-art lightweight ReID model by far.
- Model Accuracy analysis. It can be seen from the data in Table 4 that the Rank-1 and mAP of MSFANet and other state-of-the-art methods on Market1501 and DukeMTMC datasets are calculated. The top half of the table, training model from scratch, show that the mAP has increased by 1% and 1.3% when the backbone’s structures are `1-2-3’ and `2-2-2’. For the bottom half table, training with ImageNet pre-training, the mAP of MSFANet employing the two backbone structures is 86.8% and 86.9%, respectively, huge improvements over OSNet. The results obtained from the preliminary analysis of MSFANet’s high efficiency are shown in Table 4. The designs of MSFANet contain multi-scale receptive fields, Omni-scale feature learning, and channel-level attention mechanism, which can capture much more robust and meticulous ReID features. MSFANet uses fewer branches and basic modules, integrating the attention module, which helps speed up the inference of ReID.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Dataset | Time | Camera | Resolution | IDs (T-Q-G) | Images (T-Q-G) |
---|---|---|---|---|---|
Market1501 [1] | 2015 | 6 | fixed | 751-750-751 | 12936-3368-15913 |
DukeMTMC [31] | 2017 | 8 | fixed | 702-702-1110 | 16522-2228-17661 |
VIPeR [32] | 2007 | 2 | fixed | 316-316-316 | 632-632-632 |
GRID [33] | 2009 | 8 | vary | 125-125-900 | 250-125-900 |
MSFANet Data Augmentation on Market1501 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
Horizontal flip | Crop | Erase | Patch | Color jitter | Affine | Rotation | Cutout | AutoAugment | mAP (%) | Rank-1 (%) |
Y | Y | Y | 81.3 | 91.0 | ||||||
Y | Y | Y | 81.8 | 91.1 | ||||||
Y | 82.2 | 92.4 | ||||||||
Y | Y | 83.1 | 92.5 | |||||||
Y | Y | Y | Y | Y | 83.5 | 92.6 | ||||
Y | Y | Y | Y | Y | 83.9 | 92.8 | ||||
Y | Y | Y | Y | 84.6 | 93.3 | |||||
Y | Y | Y | Y | 85.0 | 93.9 | |||||
Y | Y | Y | Y | Y | 85.1 | 93.9 | ||||
Y | Y | Y | Y | Y | 86.0 | 94.5 | ||||
Y | Y | Y | Y | Y | 86.8 | 94.6 |
Loss | Dataset | mAP | Rank-1 |
---|---|---|---|
Softmax | Market1501 | 86.2 | 94.3 |
0.5*Triplet + Softmax | Market1501 | 86.8 | 94.6 |
Softmax | DukeMTMC | 74.4 | 85.9 |
0.5*Triplet + Softmax | DukeMTMC | 76.6 | 86.8 |
Method | Publication | Backbone | Market1501 | DukeMTMC | Complexity | |||
---|---|---|---|---|---|---|---|---|
Rank-1 | mAP | Rank-1 | mAP | Param () | GFLOPs | |||
Without ImageNet Pre-training | ||||||||
† ShuffleNet [15] | CVPR’18 | ShuffleNet | 84.8 | 65 | 71.6 | 49.9 | 5.34 | 0.38 |
† MobileNetV2 [14] | CVPR’18 | MobileNetV2 | 87 | 69.5 | 75.2 | 55.8 | 4.32 | 0.44 |
† BraidNet [16] | CVPR’18 | BraidNet | 83.7 | 69.5 | 76.4 | 59.5 | - | - |
†‡ HAN [25] | CVPR’18 | Inception | 91.2 | 75.7 | 80.5 | 63.8 | 4.52 | 0.53 |
† PCB [24] | ECCV’18 | ResNet | 92.4 | 77.3 | 81.9 | 65.3 | 23.53 | 2.74 |
† OSNet [27] | ICCV’19 | OSNet | 93.6 | 81 | 84.7 | 68.6 | 2.23 | 0.98 |
†‡ MSFANet + `1-2-3’ (ours) | - | MSFANet | 92.5 | 82 | 82.6 | 69.4 | 2.52 | 0.82 |
†‡ MSFANet + `2-2-2’ (ours) | - | MSFANet | 92.9 | 82.3 | 82.8 | 69.5 | 2.25 | 0.94 |
With ImageNet Pre-training | ||||||||
DaRe [4] | CVPR’18 | DenseNet | 89 | 76 | 80.2 | 64.5 | 6.95 | 1.85 |
MLFN [26] | CVPR’18 | ResNeXt | 90 | 74.3 | 81 | 62.8 | 22.98 | 2.76 |
‡ Bilinear [51] | ECCV’18 | Inception | 91.7 | 79.6 | 84.4 | 69.3 | 4.5 | 0.5 |
G2G [52] | CVPR’18 | ResNet | 92.7 | 82.5 | 80.7 | 66.4 | 23.5 | 2.7 |
DeepCRF [53] | CVPR’18 | ResNet | 93.5 | 81.6 | 84.9 | 69.5 | 23.5 | 2.7 |
IANet [54] | CVPR’19 | ResNet | 94.4 | 84.5 | 87.1 | 73.4 | 23.5 | 2.7 |
DGNet [55] | CVPR’19 | ResNet | 94.8 | 84.5 | 86.6 | 74.8 | 23.5 | 2.7 |
OSNet [27] | ICCV’19 | OSNet | 94.8 | 84.5 | 88.6 | 73.5 | 2.2 | 0.98 |
‡ MSFANet + `1-2-3’ (ours) | - | MSFANet | 94.3 | 86.8 | 86.8 | 76.6 | 2.52 | 0.82 |
‡ MSFANet + `2-2-2’ (ours) | - | MSFANet | 94.5 | 86.9 | 87.0 | 76.3 | 2.25 | 0.94 |
Comparison to State-of-the-Art Methods on Small ReID Datasets | ||||||
---|---|---|---|---|---|---|
Method | Publication | Backbone | VIPeR | GRID | ||
Rank-1 | mAP | Rank-1 | mAP | |||
JLML [56] | CVPR’18 | ResNet | 50.2 | - | 37.5 | - |
HydraPlus-Net [57] | CVPR’18 | Inception | 56.6 | - | - | - |
OSNet [27] | ICCV’19 | OSNet | 41.1 | 54.5 | 38.2 | 40.5 |
†‡ MSFANet - data_aug (ours) | - | MSFANet | 48.9 | 60.3 | 28.9 | 40.8 |
†‡ MSFANet + data_aug (ours) | - | MSFANet | 52.2 | 64.5 | 30.4 | 41.3 |
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Huang, W.; Li, Y.; Zhang, K.; Hou, X.; Xu, J.; Su, R.; Xu, H. An Efficient Multi-Scale Focusing Attention Network for Person Re-Identification. Appl. Sci. 2021, 11, 2010. https://doi.org/10.3390/app11052010
Huang W, Li Y, Zhang K, Hou X, Xu J, Su R, Xu H. An Efficient Multi-Scale Focusing Attention Network for Person Re-Identification. Applied Sciences. 2021; 11(5):2010. https://doi.org/10.3390/app11052010
Chicago/Turabian StyleHuang, Wei, Yongying Li, Kunlin Zhang, Xiaoyu Hou, Jihui Xu, Ruidan Su, and Huaiyu Xu. 2021. "An Efficient Multi-Scale Focusing Attention Network for Person Re-Identification" Applied Sciences 11, no. 5: 2010. https://doi.org/10.3390/app11052010
APA StyleHuang, W., Li, Y., Zhang, K., Hou, X., Xu, J., Su, R., & Xu, H. (2021). An Efficient Multi-Scale Focusing Attention Network for Person Re-Identification. Applied Sciences, 11(5), 2010. https://doi.org/10.3390/app11052010