Multi‐scale feature combination for person re‐identification

IET Image ProcessingVolume 16, Issue 7 p. 2001-2011 ORIGINAL RESEARCHOpen Access Multi-scale feature combination for person re-identification Bailiang Huang, Bailiang Huang orcid.org/0000-0001-9699-3315 School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, Jilin Province, ChinaSearch for more papers by this authorYan Piao, Corresponding Author Yan Piao [email protected] School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, Jilin Province, China Correspondence Yan Piao, School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130000 Jilin Province, China Email: [email protected]Search for more papers by this authorHao Zhang, Hao Zhang School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, Jilin Province, ChinaSearch for more papers by this authorYanfeng Tang, Yanfeng Tang School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, Jilin Province, ChinaSearch for more papers by this author Bailiang Huang, Bailiang Huang orcid.org/0000-0001-9699-3315 School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, Jilin Province, ChinaSearch for more papers by this authorYan Piao, Corresponding Author Yan Piao [email protected] School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, Jilin Province, China Correspondence Yan Piao, School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130000 Jilin Province, China Email: [email protected]Search for more papers by this authorHao Zhang, Hao Zhang School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, Jilin Province, ChinaSearch for more papers by this authorYanfeng Tang, Yanfeng Tang School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, Jilin Province, ChinaSearch for more papers by this author First published: 09 March 2022 https://doi.org/10.1049/ipr2.12465AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Person re-identification (Re-ID) is an instance-level task of image retrieval, and its identification accuracy depends on the distinguishable features extracted from people. However, most identification methods based on deep learning only mechanically extract distinguishable features of person images, and some important details are frequently overlooked. For scenes with substantial background differences or occlusions, the Re-ID efficiency is not high and the network scalability is not good. Here, the authors propose a multi-scale feature combination network (MFC-Net) model that combines structural feature information with global comprehensive feature information of the person images through a convolution neural network that can effectively retain distinguishing character detail information. The authors also propose a Gaussian stochastic pooling layer to solve the defects of the pooling layer. For the problem of many network parameters and weak performance, the authors propose an attentive feature convolutions layer. The authors perform many comparative experiments on three benchmark datasets. The results prove that our MFC-Net model performs well in person Re-ID and that its identification accuracy is higher than that of other investigated models. 1 INTRODUCTION Recently, person re-identification (Re-ID) has been an important research topic in the fields of artificial intelligence, machine vision, and deep learning, among others [1, 2]. Its purpose is to retrieve images or video sequences of a pre-selected target person from multiple monitoring records. Because of privacy concerns, funding issues, or equipment damage, not all areas can be kept under video surveillance at all times. Person Re-ID technology can perfectly compensate for such problems. When combined with other technologies, such as multi-people detection and people movement tracking technologies, Re-ID technology can be applied to fields such as intelligent monitoring and criminal investigation. Early research on person Re-ID focused on how to design better visual features manually and how to learn better similarity measures [10]. With continual progress in computing capabilities, research on convolutional neural networks has advanced rapidly, and their superior performance in person Re-ID has been widely documented [4-6]. Unlike traditional methods, convolutional neural networks can automatically extract better person image features and obtain better similarity measures [7-9]. Moreover, the availability of large datasets [4, 10–13] has spurred the research on person Re-ID technology [14]. For example, the rank-1 accuracy of Re-ID on Market-1501 has reached 95.6% [15], which is very close to human-level judgment. However, person Re-ID remains one of the most challenging instance-level tasks, because the visual characteristics of the media to be assessed may be significantly different owing to factors such as changes in light intensity in the morning and evening, the view angle of the surveillance camera, the occlusion of people by objects in the surrounding environment, and the walking posture of people. For example, in the four sets of images in Figure 1, due to the aforementioned factors, the impostor (in the red box) and the target person are very similar in appearance, making it challenging to identify the correct person. To overcome this difficulty, the main goal of the proposed study is to ensure that the proposed model extracts enough distinguishable features of people. FIGURE 1Open in figure viewerPowerPoint In the four sets of images, the impostor (in the red box) is similar to the query image under the influence of image resolution and illumination As an important part of the convolutional neural network, the pooling layer typically extracts the underlying information by simplistic calculations. It can expand the receptive field, compress features, simplify network complexity, reduce dimensionality and the amount of computation, prevent overfitting, and perform other important functions [16, 17]. A standard convolutional neural network has multiple pooling layers, which may lead degrade the identification accuracy [18]. Therefore, it is important to design a pooling layer that can retain detailed the original feature information. In this paper, we propose a Gaussian stochastic pooling (GS-pooling) layer that can retain the detailed original feature information and improve the identification performance of the network. Most of the Re-ID methods using convolutional neural networks [15, 19–23] only mechanically allow these networks to learn the information on the image. However, in many cases, these networks do not learn the main distinguishable features of the person. For example, from the visual activation map [25] in Figure 2, we observe that the ResNet network [24] mostly learns the background information of the image. FIGURE 2Open in figure viewerPowerPoint For the person in images, the ResNet network does not extract effective feature information The proposed omnidirectional multi-scale learning method effectively increases the person features captured by the model [5, 26], but the problem of losing effective person feature information persists. To achieve a more powerful Re-ID performance, it is highly desirable to develop Re-ID within a framework that can systematically capture the main distinguishable features of people. Inspired by the human ability to distinguish different people, we argue that for a convolutional neural network to acquire an effective and scalable ability to distinguish people, it must consider the complementary information of human body structure features and clothing colour features. Therefore, we develop a multi-scale feature combination network (MFC-Net) model that combines structural features with global comprehensive features. We also develop an attentive feature convolutions (AFC) layer to generate a correlation between feature channels, thus improving the capability of multi-scale feature information collection. Using three benchmark datasets, we perform various experiments with eight models. The results show that by combining structural features with global comprehensive features, MFC-Net can effectively extract more person feature information, and thus its performance is better than that of other network models. Main contributions We develop an effective GS-pooling layer. Compared with common pooling layers, the GS-pooling layer can retain more detailed original feature information to better fit the Re-ID task. We propose an AFC layer and an MFC-Net model. MFC-Net is a practical model designed to learn the structural feature information and global comprehensive feature information of a person. Compared with other convolutional neural networks, MFC-Net can extract more distinguishable feature information from images of people. We conduct many tests on three benchmark datasets and compare the performance against seven advanced Re-ID models. The results show that the identification performance of MFC-Net is better than that of the other models. We also study a cross-dataset learning environment that is closer to reality. We observe that MFC-Net has good scalability for unseen datasets. The remaining content is organised as follows: First, we briefly introduce related works on person Re-ID in Section 2. Next, in Section 3, we propose and introduce the GS-pooling layer, the AFC layer, and the overall model structure. In Section 4, we describe the dataset, evaluation metrics, and implementation process. We also provide a comprehensive analysis of the experimental results. Finally, we summarise our results in Section 5. 2 RELATED WORK The pooling layer is important in the feature extraction of convolutional neural networks, and it has the characteristics of maintaining translation, rotation, and expansion invariance [27]. Zeiler et al. [28] developed a stochastic pooling method based on probability weights. Their method can effectively prevent overfitting because of the introduction of randomness. Reyes et al. [29] proposed a fusion random pooling method that stochastically selects either max- or mean-pooling each time. Stergiou et al. [30] developed a pooling layer based on exponents and used the Softmax operator to weight sum the elements as the output result. Sharma et al. [31] introduced fuzzy logic into the pooling algorithm and used membership degree to calculate the threshold of the pooling domain. Radenović et al. [32] proposed a generalised-mean pooling method to reduce the performance difference between max-pooling and mean-pooling. We propose a GS-pooling layer for the person Re-ID task and achieve good performance. The lightweight network framework can make deep learning get rid of the dependence on computer hardware conditions. Huang et al. [32] used group convolution to reduce the number of network parameters. Zhang et al. [19] used pointwise group convolution and channel shuffle to considerably reduce the computing cost while ensuring the performance of the network framework. Yu et al. [34] proposed the lightweight high-resolution network, introducing lightweight units and conditional channel weighting to replace 1 × 1 convolution in shuffle blocks. Li et al. [35] proposed the dynamic slimmable network to achieve good hardware efficiency by dynamically adjusting the number of filters in the network. We use the depthwise separable convolution to reduce the parameters of the network framework. Mainstream network frameworks have achieved progressive success in the person Re-ID task by relying on the continuous improvement of hardware computing capabilities [36-42]. The traditional network only learns the global features of a person and uses the extracted feature information as the basis for image retrieval. However, this global extraction method may lose some important part-level distinguishability feature information. Pu et al. [43] achieved a breakthrough by using the adaptive knowledge accumulation framework to obtain recognition cognition of a person and achieved good identification results. Sarfraz et al. [40] used the body joint position map of people as auxiliary information with the training set to enhance the learning ability of the network for person posture. Several recent works have used pedestrian alignment and part matching to ensure more supervision and perform multi-task learning. One of the most commonly used methods is to split the input image or feature map horizontally to use the feature information in local space as auxiliary search clues [44-46]. Part-aligned representations [47] involve dividing the feature map into separate feature blocks and finally connecting the feature vectors of each region as a label of the person. Part-based convolution baseline [41] extracts local easily ignored feature information by equally dividing feature maps horizontally to enhance network performance. Establishing an effective connection between local information and global information can greatly enhance the richness of character features extracted by the network [48]. In [49, 50], and [5], the authors have combined the feature maps obtained from convolutional layers of different depths in the network. In [51, 23], and [4], the authors have introduced the attention mechanism to strengthen the ability of the network to acquire local body feature information. Hou et al. [52] used multiple attention modules to capture different body parts in consecutive frames to perfect the complete feature. Zhou et al. [53] developed a method called omni-scale feature learning, which has a positive effect on global feature extraction. In our scheme, we combine structural features with global comprehensive features for person Re-ID. 3 MATERIALS AND METHODS The schematic of the network framework is shown in Figure 3, it adopts two cooperative branches for feature extraction. To preserve the original features and extract distinguishable features of people in detail, a Gaussian random pooling layer and an attention feature convolution layer are introduced. FIGURE 3Open in figure viewerPowerPoint Architecture of MFC-Net with input image size 256 × 128 GS-pooling layer To improve the performance of convolutional neural networks in the person Re-ID task, we design a new pooling method for downsampling to compensate for the loss of original feature information in traditional pooling methods (max-pooling, mean-pooling, and stochastic pooling). The max-pooling operation is based on the maximum value of the elements in the sliding window as the pooling result of the area. For a pooling window of size n × n, the calculation formula for the sampling value h of the max-pooling operation is as follows: h = max i , j ∈ [ 1 , n ] a i , j \begin{equation}h = \mathop {\max }\limits_{i,j \in [1,{\rm{n}}]} {a_{i,j}}\end{equation} (1) The max-pooling method can retain the texture features in the original information, but it will lose more original information. For example, if the sliding window is a 2 × 2 max-pooling layer, this operation will lose 3/4 of the original information. Moreover, the max-pooling method completely ignores other values in the pooling layer, which may lead to faster overfitting. The mean-pooling method takes the mean value of elements as the output result. For an n × n pooling window, the calculation formula for the sampling value h of the mean-pooling operation is as follows: h = 1 n × n × ∑ i = 1 , j = 1 n a i , j \begin{equation}h = \frac{1}{{{\rm{n}} \times {\rm{n}}}} \times \sum_{i{\rm{ = }}1,j{\rm{ = }}1}^{\rm{n}} {{a_{i,j}}} \end{equation} (2) The mean-pooling method can retain the original background information, but the lower (or negative) value in the pooling window reduces the effect of higher values, which will produce an activation value close to 0, making some important feature information blurry. In addition, the sparse effect of the mean-pooling method is poor [54]. The stochastic pooling operation [28] assigns a corresponding weight to each element in the sliding window and then uses the weight as the probability value of the element for probability sampling. Finally, it takes the sampling result as the pooling result of the area. For a pooling window of size n × n, the calculation formula for the sampling value h of the stochastic pooling operation is as follows: p i , j = a i , j ∑ i = 1 , j = 1 n a i , j \begin{equation}{p_{i,j}} = \frac{{\left| {{a_{i,j}}} \right|}}{{\sum_{i{\rm{ = }}1,j{\rm{ = }}1}^{\rm{n}} {\left| {{a_{i,j}}} \right|} }} \end{equation} (3) h = a i j , l ∼ P ( p 11 , p 12 , … , p nn ) \begin{equation}h = {a_{ij}},l\sim P({p_{11}},{p_{12}}, \ldots ,{p_{{\rm{nn}}}})\end{equation} (4) Stochastic pooling is a simple method with strong generalisation ability, but it still has the problem of original information loss. To obtain more accurate person retrieval results, a new GS-pooling module is proposed based on the traditional pooling operation. The module can retain detailed original feature information, and the stochastic value method enables it to have strong generalisation ability and can prevent overfitting to a certain extent. The specific implementation process is divided into four steps. For an n × n pooling window, the weight Pi,j of the element ai,j in the window is first calculated in turn, where i = 1,2,···,n, j = 1,2,···,n. p i , j = a i , j ∑ i = 1 , j = 1 n a i , j \begin{equation}{p_{i,j}} = \frac{{\left| {{a_{i,j}}} \right|}}{{\sum_{i{\rm{ = }}1,j{\rm{ = }}1}^{\rm{n}} {\left| {{a_{i,j}}} \right|} }}\end{equation} (5) Second, the weight Pi,j is used as the probability value of the element, and the probability sampling of n2 elements in the pooling window is performed to obtain the element ax,y. a x , y = a i j , l ∼ P ( p 11 , p 12 , … , p nn ) \begin{equation}{a_{x,y}} = {a_{ij}},l\sim P({p_{11}},{p_{12}}, \ldots ,{p_{{\rm{nn}}}})\end{equation} (6) In the third step, according to the coordinate position of element ax,y, the corresponding n × n Gaussian kernel is obtained in the Gaussian matrix of size m × m (m = 2n−1), where the position of the element ai,j corresponds to the position of the centre element gn,n of the Gaussian matrix. The weight value Gi,j of each element in the n × n Gaussian kernel is then calculated. G i , j = g n − x + i , n − y + j ∑ i = 1 , j = 1 n g n − x + i , n − y + j \begin{equation}{G_{i,j}} = \frac{{{g_{n - x + i,n - y + j}}}}{{\sum_{i{\rm{ = }}1,j{\rm{ = }}1}^{\rm{n}} {{g_{n - x + i,n - y + j}}} }}\end{equation} (7) Finally, the elements in the pooling window are multiplied by the weight value at the corresponding position in the Gaussian kernel and then accumulated to obtain the pooling result S. S = ∑ i = 1 , j = 1 n a i , j × G i , j \begin{equation}S = \sum_{i = 1,j = 1}^n {{a_{i,j}} \times {G_{i,j}}} \end{equation} (8) Figure 4 is a schematic of the GS-pooling method with the pooling window n = 2. FIGURE 4Open in figure viewerPowerPoint Gaussian Stochastic pooling method example Multi-scale residual feature extraction module We propose an AFC layer to improve the sensitivity of feature channels to important information, reduce the number of network parameters, and improve network performance. The basic idea is to use depthwise separable convolution [55, 56], and then start from the relationship between feature channels to perform fusion among feature channels. Each feature channel is enhanced or suppressed according to its importance. The entire convolutional layer can be expressed as ReLU[u⊙G(u)], where u denotes the depthwise separable convolution, which is composed of the pointwise kernel ( p ∈ R 1 × 1 × c i × c o $p \in {{\rm{R}}^{1 \times 1 \times {c_i} \times {c_o}}}$ ) and the depthwise kernel ( d ∈ R k × k × 1 × c o $d \in {{\rm{R}}^{k \times k \times 1 \times {c_o}}}$ ); ci, co, and k denote input feature tensor width, output feature tensor width, and depthwise kernel size, respectively. To improve the ability of omni-scale feature learning, we follow [53] to adopt the convolution method of pointwise kernel to depthwise kernel. The depthwise separable convolution can reduce the number of parameters in the convolution layer from k2 × ci × co to (k2+ci) × co.⊙ is the Hadamard product. G(u) is the feature channel weight vector of u, which is a channel-aware network composed of the global maximum pooling layer, global average pooling layer, and channel perception layer. We illustrate the implementation of the AFC layer in Figure 5a. FIGURE 5Open in figure viewerPowerPoint (a) Attentive feature convolutions layer. (b) Multi-scale residual feature extraction module To enhance feature acquisition capability, the building block in our architecture is the multi-scale residual module [24], equipped with the AFC layer. Given an input feature tensor x. We use 1 × 1 deep convolution to change the channel of the feature tensor from c to c/4, and the feature tensor after dimensionality reduction is x′. Such an operation can effectively speed up the calculation. Then, it is sent to several feature acquisition channels of different scales Fs(x′). Herein, we set four feature acquisition channels of different scales (s = 4), and the structure is shown in Figure 5b. After obtaining the feature information of each scale, we successively concatenate each feature tensor to obtain x′′. Therefore, we can effectively retain the feature information under different receptive fields to enhance its body structure cues. H1 and H2 are two 1 × 1 layers ( H 1 ∈ R 1 × 1 × c i n × c o u t ${H_1} \in {{\rm{R}}^{1 \times 1 \times {c_{in}} \times {c_{out}}}}$ , H 2 ∈ R 1 × 1 × c i n × c o u t ${H_2} \in {{\rm{R}}^{1 \times 1 \times {c_{in}} \times {c_{out}}}}$ ), which increase the feature channels from cin to cout, and ∗ denotes the convolution operation. The overall operation is as follows: x ′ ′ = F x ′ , F 2 x ′ , F 3 x ′ , F 4 x ′ \begin{equation}x^{\prime\prime} = \left[ {F\left( {x^{\prime}} \right),{F^2}\left( {x^{\prime}} \right),{F^3}\left( {x^{\prime}} \right),{F^4}\left( {x^{\prime}} \right)} \right]\end{equation} (9) y = Re LU H 2 ∗ x + H 1 ∗ x ′ ′ \begin{equation}y = {\mathop{\rm Re}\nolimits} {\rm{LU}}\left[ {\left( {{H_2} * x} \right) + \left( {{H_1} * x^{\prime\prime}} \right)} \right]\end{equation} (10) Network architecture In our implementation, we expect colour information to affect the extraction of human structure features to a certain extent. Therefore, to effectively extract distinguishable features, our network design has two feature extraction branches. One branch takes grey-scale images as input to effectively extract structural features of people, and another branch takes three-channels RGB images as input to extract global comprehensive feature information. By combining structural features and global comprehensive features, the proposed MFC-Net model can effectively extract the distinguishable features of people and enhance identification accuracy. The detailed network architecture of MFC-Net is shown in Figure 3. We progressively compress features as conventional convolutional neural networks. Every time a feature is compressed, the channel number of the feature tensor is doubled. After six GS-pooling modules and six multi-scale feature extraction modules, we obtain 16 × 8 structural features with 256-dim and 16 × 8 global comprehensive features with 512-dim. Then we concatenate the two outputs to yield 16 × 8 features with 768-dim. 4 EXPERIMENTS Datasets and evaluation protocols In this paper, we use three popular benchmark datasets to test the performance of MFC-Net. Market-1501 [10] is the most widely used person Re-ID dataset. The images in the dataset are taken by six dissimilar cameras, one of which is a low-resolution camera. All of the images are collected on the campus of Tsinghua University. In this dataset, each personal image has more than two perspectives. The images are automatically detected and cut by the Deformable Part Model detector [57]; therefore, they contain some detection errors. The dataset provides training and test sets, and they are independent of each other. The dataset also contains a separate set of interference images. DukeMTMC-reID [12] is collected on the campus of Duke University and contains person images with manually marked borders. This dataset comprises 36411 images of 1404 identities, and the images are captured using eight cameras. This dataset was the largest person Re-ID dataset before the ICCV2017 conference, and it provides annotations of person attributes (e.g. gender, sleeve, backpack). It is one of the most challenging person Re-ID datasets to date, because most people are in similar clothes, and the people may be obscured by cars or trees. The MSMT17 [13] dataset contains 126441 images of 4101 identities from 15 cameras, both indoors and outdoors. All of the images are collected at Peking University. It is the largest of all public person Re-ID datasets at present. The dataset is taken in four days with different weather conditions in a month, so the images contain rich scene changes. For a given person query image, models obtain the distinguishable features of the person, then compare these features with those in the images in the candidate set, and rank the person images according to the calculated Euclidean distance value. If the ranking is high, it indicates that the person image and the query image are highly similar. When the correctly matched pedestrian image ranks high in the list, the accuracy of the person Re-ID algorithm tested is proven to be high. We use rank-1 and mean average precision (mAp) as the performance evaluation index of the person Re-ID method. mAP is the average value of average precision (AP), which mainly comprises precision and recall [14]. In the precision and recall calculation, TP denotes the number of positive samples that are correctly identified, FP denotes the number of positive samples that are incorrectly identified, and FN denotes the number of negative samples that are identified incorrectly. Precision = T P T P + F P \begin{equation}{\rm{Precision = }}\frac{{TP}}{{TP + FP}}\end{equation} (11) Recall = T P T P + F N \begin{equation}{\rm{Recall = }}\frac{{TP}}{{TP + FN}}\end{equation} (12) AP represents the average accuracy at different recalls, where M is the total number of correct matches in the candidate set, and i∈{i1,i2,···,iM} is the location index of the M correct matches in the sorted list. The expression is as follows: A P = 1 M ∑ i ∈ { i 1 , i 2 , · · · , i M } Precision ( i ) \begin{equation}AP{\rm{ = }}\frac{1}{M}\sum_{i \in \{ {i_1},{i_2}, \cdot \cdot \cdot ,{i_M}\} } {{\rm{Precision}}} (i)\end{equation} (13) When the query set contains N samples, mAP can be expressed as: m A P = 1 N ∑ j = 1 N A P ( j ) \begin{equation}mAP{\rm{ = }}\frac{1}{N}\sum_{j = 1}^N {AP(j)} \end{equation} (14) Implementation details The server we use to train the model is 64-bit ubuntu20.04.2 LTS, configured with NVIDIA GEFORCE RTX 3090. The code of the MFC-Net model is written in Pytorch 1.7.0 environment, using a single GPU training mode. We uniformly resize the input image of the framework to 256 × 128. For data augmentation, we use stochastic erasure and stochastic flip [32]. We also use the SGD optimiser to assist the MFC-Net model training from scratch. Quantitative results Recently proposed Re-ID methods include ShuffleNetV2 [19], MobileNetV2 [20], DenseNet-121 [21], ResNet-50 [24], BraidNet [22], HAN [23], and OSNet [53]. Table 1 shows the experimental results for Market1501, DukeMTMC-reID, and MSMT17. In the experiment, we observe that pre-training models with ImageNet can effectively enhance the identification performance of networks [32]. Therefore, all models are trained from scratch to further compare network performance fairly and effectively. To measure the performance of each model in the person Re-ID, we use Rank-1 and mAP. From Table 1, we observe that for each dataset, our approach attains the best identification performance. In particular, for DukeMTMC-reID, the mAP score increases from 68.6% to 76.9%, an increase of 8.3% points. TABLE 1. Comparison results of models trained from scratch Market1501 DukeMTMC-reID MSMT17 Methods Rank-1 mAP Rank-1 mAP Rank-1 mAP ShuffleNetV2 79.8 56.8 68.8 48.1 36.8 15.7 MobileNetV2 82 59.2 71.1 50.5 42.5 18.6 DenseNet-121 83.1 63.4 73.2 55.1 46.3 22.5 ResNet-50 84.6 65.3 73.2 56 46.9 22.2 BraidNet 83.7 69.5 76.4 59.5 49.6 24.9 HAN 91.2 75.7 80.5 63.8 52.1 26 OSNet 93.6 81 84.7 68.6 71 43.3 MFC-Net 96.2 88.6 90.4 76.9 73.