# Human Attribute Recognition--
## 3 Discussion of Sources
### Localization Methods
#### 3.1.3 Part-Based Methods  
Extracting discriminative fine-grained features often requires first to localize patches of the relevant regions in the input data. Unlike pose-let-based methods that detect the patches from the entire image, part-based methods aim to learn based on accurate body parts (i.e., head, torso, arms, and legs). Optimal part-based models are (1) pose sensitive (i.e., for similar poses, shows strong activations); (2) extendable to all samples; (3) discriminative on extracting features. CNNs can handle all these factors to some extend, and [77] empirical experiments confirm that for deeper networks, accurate body-parts are less significant.  
As one of the first part-based works, inspired by a part detector (i.e., deformable part model [78], which captures viewpoint and pose variations), [PERSON] et al. [79] propose two descriptors that learn based on the part annotations. Their main objective is to localize the semantic parts and obtain a normalized pose representation. To this end, the first descriptor is fed by correlated body parts, while for the second descriptor, the input body splits have no semantic correlation. Intuitively, the first descriptor is based on the inherent semantics of the input image, and the second descriptor learns the cross-component correspondences between the body parts.  
Later, in this context, [PERSON] [77] proposes a model composed of a CNN-based body-part detector, including an SVM classifier (trained on the full-body and body parts, that is, head, torso, and legs) to predict the human attributes and action. Given an input image, a Gaussian pyramid is obtained, each level is fed to several convolutional layers to produce pyramids of feature maps. The convolution of each feature-level with each body-part produces scores correspond to that body-part. Therefore, the final output is a pyramid of part model scores suitable for learning an SVM classifier. The experiments indicate that using body-part analysis and making the network deeper improve the results.  
As earlier part-based methods used separate feature extractors and classifiers, the parts could not be optimized for recognizing the semantic attributes. Moreover, the detectors, at that time, were inaccurate in detection. Therefore, [PERSON] [80] proposed an end-to-end model, in which the body partitions are generated based on the skeleton information. As authors augment a large skeleton estimation dataset (MPII [81]) for human skeleton information (which is less prone to error for annotation in comparison with bounding box annotations for body parts), their body detector is more accurate in detecting the relevant partitions, leading to better performance.  
To encode both global and fine-grained features and implicitly relate them to the specific attributes, [PERSON] [82] proposes to add several branches on top of a ResNet50 network, such that each branch explores particular regions of the input data and learns an exclusive classifier. Meanwhile, before the classifier stage, all branches share a layer, which passes the 6 static regions of features to the attribute classifiers. For example, the head attribute classifier is fed only with the two upper strips of the feature maps. Experimental results on the Market-1501 dataset [24] show that applying a layer that feeds regional features to the related classifiers can improve the \(mA\) from 85.0 to 86.2. Further, repeating the experiments while adding a branch to the architecture of the model for predicting the person ID (as an extra-label) improves the \(mA\) result from 84.9 to 86.1. These experiments show that simultaneous ID prediction without any purpose could slightly diminish the accuracy.