# Human Attribute Recognition--
## 3 Discussion of Sources
### Attributes Relationship
#### 3.3.1 Network-Oriented Attribute Correlation Consideration  
(A) Multi-task Learning. In [55], [PERSON] et al. discuss that the intuition-based design of multi-task models is not an optimal solution for sharing the relevant information over multiple tasks, and they propose to gradually widen the structure of the model (add new branches) using an iterative algorithm. Consequently, in the final architecture, correlated tasks share most of the convolutional blocks together, while uncorrelated tasks will use different branches. Evaluation of the model on the fashion dataset [91] shows that by widening the network to 32 branches, the accuracy of the model cannot compete with other counterparts; however, the speed increases (from 34 ms to 10 ms) and the number of parameters decreases from 134 million to 10.5 million. In a multi-task attribute recognition problem, each task may have a different convergence rate. To alleviate this problem and jointly learn multiple tasks, [PERSON] [111] proposes a weighted loss function that updates the weights for each task in the course of learning. The experimental evaluation on the Market-1501 dataset [24] shows an improvement in accuracy from 86.8% to 88.5%. In [112; 113], the authors study the multi-task nature of PAR and attempt to build an optimal grouping of the correlated tasks, based on which they share the knowledge between tasks. The intuition is that, similar to the human brain, the model should learn more manageable tasks first and then uses them for solving more complex tasks. The authors claim that learning correlated tasks needs less effort, while uncorrelated tasks require specific feature representations. Therefore, they apply a curriculum learning schedule to transfer the knowledge of the easier tasks (strongly correlated) to the harder ones (weakly correlated). The baseline results show that learning the tasks individually yields 71.0% accuracy on the SoBiR dataset [114], while this number for learning multiple tasks at once is 71.3% and for a curriculum-based multi-task model is 74.2%. Considering HAR as a multi-task problem, Reference [54] proposes to improve the model architecture in terms of feature sharing between tasks. Authors claim that by learning a linear combination of features, the inter-dependency of the channels is ignored, and the model cannot exchange spatial information. Therefore, after each convolutional block in the model, they insert a shared module between tasks to share the information. This module considers three aspects: (1) fusing the features of each two tasks together, (2) generating attention maps regarding the location of the attributes [115], and (3) keeping the effect of the original features of each task. Ablation studies over this module's positioning indicate that adding it at the end of the convolutional blocks yields the best results. However, the performance is approximately stable when different branches of the module (one at a time) are ablated. (B) RNN. In [116], authors discuss that person re-id focuses on the global features, while attribute recognition relies on local aspects of individuals. Therefore, [PERSON] et al. [116] propose a network consisted of three parts that work together to learn the person's attributes and re-identification (re-id). Further, to capture the contextual spatial relationships and focus to the location of each attribute, they use the RNN-CNN backbone feature extractor followed by an attention model. To mine the relation of attributes, Reference [117] uses a model based on Long Short Term Memory (LSTM). Intuitively, using several successive stages of LSTM preserves the necessary information along the pipeline and forgets the uncorrelated features. In this work, the authors first detect three-body pose-lets based on the skeleton information. They consider the full-body as another pose-let followed by several fully connected layers to produce several groups of features (for each attribute, one group of features). Each group of features is passed to an LSTM block, followed by a fully-connected layer.