# Human Attribute Recognition--
## 6 Discussion
### Critical Discussion and Performance Comparison
As seen, the PETA dataset sounds easier than other datasets, despite the smaller size and lower quality data compared with the RAP dataset. Figure 5: As human, not only we describe the available attributes in occluded images but also we can predict the covered attributes in a negative strategy based on the attribute relations. Figure 6: State-of-the-art mAP results on three well-known PAR datasets. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref., Year, Cat.** & **Taxonomy** & **Dataset** & **mA** & **Acc.** & **prec.** & **rec.** & **F1** \\ \hline
[66], 2011, FAR & Pose-Let & HAT [66] & 53.80 & - & - & - & - \\ \hline
[68], 2011, FAR & Pose-Let & [68] & 82.90 & - & - & - & - \\ \hline
[123], 2012, FAR and CAA & Attribute relation & [123] & - & 84.90 & - & - & - \\ \cline{2-7}  & & D.Fashion [91] & 35.37 (top-5) & - & - & - & - \\ \hline
[79], 2013, FAR & Body-Part & HAT [66] & 69.88 & - & - & - & - \\ \hline
[69], 2013, FAR & Pose-Let & HAT [66] & 59.30 & - & - & - & - \\ \hline
[70], 2013, FAR & Pose-Let & HAT [66] & 59.70 & - & - & - & - \\ \hline
[77], 2015, FAR & Body-Part & DP [68] & 83.60 & - & - & - & - \\ \hline
[128], 2015, FAR & Loss function & PETA [152] & 82.6 & - & - & - & - \\ \hline
[77], 2015, FAR & Body-Part & DP [68] & 83.60 & - & - & - & - \\ \hline
[150], 2015, CAA & Attribute location and relation & Dress [150] & - & 84.30 & 65.20 & 70.80 & 67.80 \\ \hline
[75], 2016, FAR & Pose-Let & WIDER [75] & 92.20 & - & - & - & - \\ \hline
[74], 2016, FAR & Pose-Let & RAP [108] & 81.25 & 50.30 & 57.17 & 78.39 & 66.12 \\ \cline{2-7}  & & PETA [152] & 85.50 & 76.98 & 84.07 & 85.78 & 84.90 \\ \hline
[91], 2016, CAA & Limited data & D.Fashion [91] & 54.61 (top-5) & - & - & - & - \\ \hline
[86], 2017, FAR & Attention & WIDER [75] & 82.90 & - & - & - & - \\ \cline{2-7}  & & Berkeley [68] & 92.20 & - & - & - & - \\ \hline
[88], 2017, PAR & Attention & RAP [108] & 76.12 & 65.39 & 77.33 & 78.79 & 78.05 \\ \cline{2-7}  & & PETA [152] & 81.77 & 76.13 & 84.92 & 83.24 & 84.07 \\ \cline{2-7}  & & PA-100K [88] & 74.21 & 72.19 & 82.97 & 82.09 & 82.53 \\ \hline
[124], 2018, FAR & Grammar & DP [68] & 89.40 & - & - & - & - \\ \hline
[61], 2018, PAR and FAR & Pose Estimation & RAP [108] & 77.70 & 67.35 & 79.51 & 79.67 & 79.59 \\ \cline{2-7}  & & PETA [152] & 83.45 & 77.73 & 86.18 & 84.81 & 85.49 \\ \cline{2-7}  & & WIDER [75] & 82.40 & - & - & - & - \\ \hline
[86], 2017, PAR & Attention & RAP [108] & 78.68 & 68.00 & 80.36 & 79.82 & 80.09 \\ \cline{2-7}  & & PA-100K [88] & 76.96 & 75.55 & 86.99 & 83.17 & 85.04 \\ \hline
[109], 2017, PAR & RNN & RAP [108] & 77.81 & - & 78.11 & 78.98 & 78.58 \\ \cline{2-7}  & & PETA [152] & 85.67 & - & 86.03 & 85.34 & 85.42 \\ \hline
[104], 2017, PAR & Loss Function - Augmentation & PETA [152] & - & 75.43 & - & 70.83 & - \\ \hline
[132], 2017, PAR & Occlusion & RAP [108] & 79.73 & 83.97 & 76.96 & 78.72 & 77.83 \\ \hline
[105], 2017, CAA & Transfer Learning & [105] & 64.35 & - & 64.97 & 75.66 & - \\ \hline
[111], 2017, PAR & Multitask & Market [24] & - & 88.49 & - & - & - \\ \cline{2-7}  & & Duke [152] & - & 87.53 & - & - & - \\ \hline
[192], 2017, CAA & Multiplication & D.Fashion [91] & 30.40 (top-5) & - & - & - & - \\ \hline
[89], 2018, CAA & Attention & D.Fashion [91] & 60.95 (top-5) & - & - & - & - \\ \hline
[112], 2018, PAR & Soft-Multitask & SoBiR [114] & 74.20 & - & - & - & - \\ \cline{2-7}  & & VIPeR [193] & 84.00 & - & - & - & - \\ \cline{2-7}  & & PETA [152] & 87.54 & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of HAR approaches over the last decade for different benchmarks.