# Human Attribute Recognition--
## 3 Discussion of Sources
### Localization Methods
#### 3.1.2 Pose-Let-Based Methods  
The main idea of pose-let based methods is to provide a bag-of-features from the input data using different patching technique. As earlier methods lacked accurate body part detectors, overlapping patches of the input images were used to extract local features. Reference [66] is one of the first techniques in this group that uses Spatial Pyramid Representation (SPR) [67] to divide the images into grids. Unlike a standard bag-of-features method that extracts the features from a uniform patching distribution, they suggest a recursive splitting technique, in which each grid has a parameter that is jointly learned with the weight vector. Intuitively, the spatial grids are varying for each class, which leads to better feature extraction. In Reference [68], hundreds of pose-lets are detected from the input data; a classifier is trained for each pose-let and semantic attribute. Then, another classifier aggregates the body-part information, with emphasis on the pose-lets taken from usual viewpoints that have discriminative features. A third classifier is then used to consider the relationship between the attributes. This way, by using the obtained feature representation, the body pose and viewpoint are implicitly decomposed. Noticing the importance of accurate body-part detection when dealing with clothing appearance variations, Reference [69] proposes to learn a comprehensive dictionary that considers various appearance part types (e.g., representing the lower-body in different appearances from bare legsto long skirts). To this end, all the input images are divided into static overlapping cells, each of which is represented by a feature descriptor. Then, as a result of feature clustering into \(K\) clusters, they represent \(k\) types of appearance parts. In Reference [70], the authors targeted the human attributes and action recognition from still images. To this end, supposing that the available human bounding boxes are located in the center of the image, the model learns the scale and positions of a series of image partitions. Later, the model predicts the labels based on the reconstructed image from the learned partitions. To address the large variation in articulation, angle, and body-pose [71] proposes a CNN-based features extractor, in which each pose-let is fed to an independent CNN. Then, a linear SVM classifier learns to distinguish the human attributes based on the aggregation between the full-body and pose-let features. References [72; 73] showed that not only CNNs can yield a high-quality feature representation from the input, but also they are better at classification than SVM classifiers. In this context, [PERSON] et al. propose to predict multiple attributes at-once, by implicit regard to the attribute dependencies. Therefore, the authors divide the image into 15 static patches and analyze each one with a separate CNN. To consider the relationship between attributes and patches, they connect the output of some specific CNNs to the relevant static patches. For example, the upper splits of the images are connected to the head and shoulder's attributes. Reference [74] claims that in previous pose-let works, the location information of the attributes is ignored. For example, to recognize whether a person wears a hat or not, knowing that this feature is related to the upper regions of the image can guide the model to extract more relevant features. To implement this idea, the authors used an Inception [62] structure, in which the features of three different levels (low, middle, and high levels) are fed to three identical modules. These modules extract different patches from the whole and part of the input feature maps. The aggregation of the three branches yields the final feature representation. By following this architectural design, the model implicitly learns the regions related to each attribute in a weakly supervised method.