# Rethink Motion Information for Occluded Person Re-Identification
## 4 Experiments
### Ablation Studies
** We conduct supplement experiments (Table 7) regarding the different latent query lengths of the visual adapter module. In contrast to previous methods [12], the query length can be adjusted to fit different datasets. If we increase the query length to 256, the accuracy of the video task outperforms the method [12]. However, if the query length (512) is too long, the model will be over-fitted, thus reducing the model accuracy. **Experiments on the length of learnable queries in motion-aware transformer. ** We change the length of the learnable queries in the motion-aware transformer since the length of the queries corresponds to the degree of refinement of human parts. In other words, the length of the query determines how many parts the human body will be split into. We evaluate different lengths of queries on Market-1501, Occluded-REID, Partial-REID, and MARS. As shown in Table 8, the ablation experiments show the same trend: as the length increases, the Rank-1 index always increases, then decreases, and reaches a peak at 10. Obviously, on the occluded and partial datasets, when the length is set greater than 10, our method experiences a sudden decline in Rank-1. This phenomenon proves our method is sensitive to the length of learnable queries. It is obvious that when our model needs to focus on more human parts, there will be many unneec \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ID** & **MCT** & **VA** & **MAT** & **Market-1501** & **O-REID** & **P-REID** & **MARS** \\ \hline \#1 & ✗ & ✗ & ✗ & 92.7 & 81.2 & 81.0 & - \\ \#2 & ✓ & ✗ & ✗ & 94.0 & 84.3 & 84.9 & - \\ \#3 & ✗ & ✓ & ✗ & 92.9 & 81.5 & 81.2 & 89.1 \\ \#4 & ✗ & ✗ & ✓ & 94.5 & 83.2 & 83.4 & - \\ \#5 & ✓ & ✓ & ✗ & 94.5 & 83.5 & 83.1 & 90.1 \\ \#6 & ✓ & ✗ & ✓ & 95.4 & 86.3 & 88.9 & - \\ \#7 & ✗ & ✓ & ✓ & 94.9 & 83.9 & 83.6 & 90.5 \\ \#8 & ✓ & ✓ & ✓ & **95.7** & **86.7** & **89.2** & **91.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablations of key modules of MAF on Market-1501, Occluded-REID, Partial-REID, and MARS. Here, O-REID is the abbreviation of Occluded-REID and P-REID is the abbreviation of Partial-REID. MCT refers to the motion consistency task. VA refers to the visual adapter. MAT refers to the motion-aware transformer. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Length** & \begin{tabular}{c} **MARS** \\ **Rank-1** \\ \end{tabular} & \begin{tabular}{c} **LS-VID** \\ **Rank-1** \\ \end{tabular} & \
\begin{tabular}{c} **iLIDS-VID** \\ **Rank-1** \\ \end{tabular} &
\begin{tabular}{c} **PRID-2011** \\ **Rank-1** \\ \end{tabular} \\ \hline
128 & 91.1 & 87.6 & 92.4 & 96.4 \\
256 & **91.2** (91.0) & **87.7** (87.4) & **92.8** (92.5) & **96.7** (96.5) \\
512 & 91.0 & 87.4 & 92.3 & 96.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The experiments of latent query length in visual adapter on MARS, LS-VID, iLIDS-VID, and PRID-2011. The gray denotes the previous SOTA. which will directly increase the difficulty of modeling the human body. On the contrary, on a holistic REID task, the Rank-1 is not particularly dramatic. This phenomenon proves two things. Firstly, occlusions rely more on refined human part features, but holistic situations rely more on global features. The suitable length of the queries setting will help the model learn the body parts in sufficient detail. Secondly, the fusion encoder is able to successfully aggregate local and global features into the hidden state \(h_{m\_cls}\). Reviewing previous human part-based methods, their part-aware masks may not benefit from suppressing the disturbance and further grouping human part features, which may be the main reason why some part-aware masks maintain high confidence scores in the background area. It is worth mentioning that their design achieves great performance, but still pays more attention to human appearance features, which shows limitations in occluded situations.