# Human Attribute Recognition--
## 3 Discussion of Sources
### Localization Methods
#### 3.1.4 Attention Based Methods  
By focusing on the most relevant regions of the input data, human beings recognize the objects and their attributes without the background's interference. For example, when recognizing the head-accessories attributes of an individual, special attention is given to the facial region. Therefore, many HAR methods have attempted to implement an attention module to be inserted at multiple levels of CNN. Attention heat maps (also called localization score map [83, 84] or class activation map [85]) are colorful localization score maps that make the model interpretable and are usually faded over the original image to show the model's ability to focus on the relevant regions. In order to eliminate the need for body-part detection and prior correspondence among the patches, [PERSON] [86] proposed to refine the Class Activation Map network [85], in which the relevant regions of the image to each attribute are highlighted. The model comprises a CNN feature extraction backbone with several branches on its top, which yield the scores for all the attributes and their regional heat maps. The fitness of the attention heat maps is measured using an exponential loss function, while the score of the attributes is derived from a classification loss function. The evaluation of the model is performed using two different convolutional backbones (i.e., VGG16 [87] and AlexNet [13]), and the result for the deeper network (VGG16) is better than the other one. To extract more distinctive global and local features, [PERSON] et al. [88] propose an attention module that fuses several feature layers of the relevant regions and yields attention maps. To take full advantage of the attention mechanism, they apply the attention module to different model levels. Obtaining the attentive feature maps from various layers of the network means that the model has captured multiple levels of the input sample's visual patterns so that the attention maps from higher blocks can cover more extensive regions, and the lower blocks focus on smaller regions of the input data. Considering the problem of cloth classification and landmark detection, [PERSON] [89] proposes an attentive fashion grammar network, in which both the symmetry of the cloths and effect of body motion is captured. To enhance the clothing classification, authors suggest to (1) develop supervised attention using the ground truth landmarks to learn the functional parts of the clothes and (2) use a bottom-up, top-down network [90], in which a successive down and up-sampling are performed on the attention maps to learn the global attention. The evaluation results of their model for clothing attribute prediction improved the counterpart methods by a large margin (30% to 60% top-5 accuracy on the DeepFashion-C dataset [91]). With a view to select the discriminative regions of the input data, [PERSON] [92] proposes a model considering three aspects: (1) Using the parsing technique [93], they split features of each body-part and help the model learns the location-oriented features by pixel-to-pixel supervision. (2) Multiple attention maps are assigned to each label due to empowering the features from the relevant regions to that label and suppressing the other features. Different from the previous step, the supervision in this module is performed on the image-level. (3) Another module learns the relevant regions for all the attributes and learns from a global perspective. The quantitative results on several datasets show that the full version of the model improves the plain model's performance slightly (e.g., for the RAP dataset, the F1 metric improves from 79.15 to 79.98). Reference [94] is another research that has focused on localizing the human attributes engaging multi-level attention mechanisms in full-frame images. First, supervised coarse learning is performed on the target person, in which the extracted features of each residual block is multiplied by the ground truth mask.