# Human Attribute Recognition--
## 3 Discussion of Sources  
As depicted in Figure 2, we identified five major challenges frequently addressed by the literature on HAR--localization, limited learning data, attribute relation, body-part occlusion, and data class imbalance. HAR datasets only provide the labels for a bounding box of person, but the locations related to each attribute are not annotated. Finding which features are related to which parts of the body is not a trivial task (mainly because body posture is always changing), and not fulfilling it may cause an error in prediction. For example, recognizing the 'wearing sunglasses' attribute in a full-body image of a person without considering the eyeglasses' location may lead to omitting the sunglasses feature information due to extensive pooling layers and a small region of the eyeglasses, compared to the whole image. This challenge is known as localization (Section 3.1), as in which we attempt to extract features of different spatial locations of the image to be certain no information is lost, and we can extract distant features from the input data. Earlier methods used to work with limited data as the mathematical calculations were computationally expensive, and increasing the amount of data could not justify the exponential computational cost and the amount of improvement in the accuracy. After the deep learning breakthrough, more data proved to be effective in the generalization ability of the models. However, collecting and annotating very large datasets is prohibitively expensive. This issue is known as limited data challenge, which has been the subject of many studies in the deep neural network fields of study, including deep-based HAR, addressed in Section 3.2. In the context of HAR, dozens of attributes are often analyzed together. As humans, we know that some of these attributes are highly correlated, and knowing one can improve the recognition probability of the other attributes. For example, for a person wearing a 'tie,' it is less likely to wear a 'Pyjama' and more likely to wear a'shirt' and'suit'. Studies that address the relationship between attributes as their main contribution are categorized in the 'attribute relation' taxonomy and discussed in Section 3.3. Body parts occlusion is another challenge when dealing with HAR data that has not yet been addressed by many studies. The challenge in occluded body parts is not only about the missing information of the body parts, but also the presence of some misleading features of other persons or objects. Further, because in HAR, some attributes are related to specific regions, considering the occluded parts before the prediction is important. For example, for a person with an occluded lower body, yielding predictions about the attributes located in the lower body region is questionable. In Section 3.4, we discuss the methods and ideas that have particularly addressed the occlusion in HAR data. Another critical challenge in HAR is the imbalanced number of samples in each class of data. Naturally, an observer sees fewer persons wearing long coats, while there are many persons in the community that appear with a pair of jeans. That is why the HAR datasets are intrinsically imbalanced and cause the model to be biased/over-fitted on some classes of data. Many studies address this challenge in HAR data, which have been discussed in Section 3.5. Among the major challenges in HAR, considering attribute correlation and extracting fine-grained features from local regions of the given data have attracted the most attention, such that recent works [58; 59] attempt to develop some models that could address both challenges at the same time. Data class imbalance is another contribution of many HAR methods which is often handled by applying weighted loss functions to increase the importance of the minority samples and decrease the effect of the samples from classes with many samples.