# Continual Learning for Table Detection in Document Images
## 4 Evaluations
### Results  
Table 2 summarizes the results of these experiments. By taking a close look at the values of FT and ER, it can be observed that the proposed approach effectively prevents the models from forgetting previous datasets. To emphasize the contrast, parenthesized values in the ER rows show the mAP-gain by ER in comparison to FT. In particular, the mAP for the proposed method on the TableBank is about 15 percent higher than FT. We see that the Sparse R-CNN+PVT demonstrates a better performance than the Faster R-CNN+ResNet in almost all experiments.  
The precision-recall curves for ER and FT with the Sparse R-CNN+PVT architecture are presented in Figure 5. It is evident that as IOU thresholds rise, FT curves plummet. This is further illustrated with IOU values of 0.9 and 0.95 (in green and red, respectively). Moreover, the fact that the older datasets are more prone to forgetting is again apparent in the figures. This is evident from the TableBank's curves in Figures 5a,b and in Figures 5g,h that correspond to the most recent dataset.  
Figure 6 presents common pitfalls ahead of FT. In some cases, the model inaccurately detects the bounding boxes, and in others, we see frequent samples of false-positives. In contrast, the ER approach has led to a better performance and prevented the model from forgetting.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **Train-Set/Test-Set** & **Faster R-CNN+ResNet** & **Sparse R-CNN+PVT** \\ \hline IT & \(TB/TB\) & 95.7 & 96.2 \\ JT & \(\{TB\cup PN\cup PT\cup FN\}/\{TB\}\) & 94.1 & 94.7 \\ FT & \(TB\to PN\to PT\to FN/\{TB\}\) & 74.2 & 76.4 \\ ER & \(TB\to PN^{R_{1}}\to PT^{R_{2}}\to FN^{R_{2}}/\{TB\}\) & 89.6(+15.4) & 90.7(+14.3) \\ \hline IT & \(PN/PN\) & 97.6 & 97.4 \\ JT & \(\{TB\cup PN\cup PT\cup FN\}/\{TB\}/PN\) & 97.4 & 97.5 \\ FT & \(TB\to PN\to PT\to FN/PN\) & 90.5 & 90.6 \\ ER & \(TB\to PN^{R_{1}}\to PT^{R_{2}}\to FN^{R_{2}}/PN\) & 93.7(+3.2) & 92.5(+1.9) \\ \hline IT & \(PT/PT\) & 98.9 & 99 \\ JT & \(\{TB\cup PN\cup PT\cup FN\}/\{PT\}\) & 98.4 & 98.7 \\ FT & \(TB\to PN\to PT\to FN/PT\) & 97.2 & 98 \\ ER & \(TB\to PN^{R_{1}}\to PT^{R_{12}}\to FN^{R_{2}}/\{PT\}\) & 97.4(+0.2) & 98.2(+0.2) \\ \hline IT & \(FN/FN\) & 90 & 91.3 \\ JT & \(\{TB\cup PN\cup PT\cup FN\}/\{FN\}\) & 88.4 & 92.7 \\ FT & \(TB\to PN\to PT\to FN/FN\) & 90 & 93.3 \\ ER & \(TB\to PN^{R_{1}}\to PT^{R_{12}}\to FN^{R_{2}}/FN\) & 90 & 93.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The mAP results of different experiments on multiple test-sets. IT is the Independent training, JT is the Joint training, FT is the Fine-tuning, and ER is the Experience replay. the values written in the parentheses in the ER experiment demonstrate the difference in the mAP metrics between the ER and FT approaches. Acronyms TB, PN, PT, and FN denote TableBank, PubLayNet, PubTables-1M, and FinTabNet, respectively. The \(R\) superscripts for ER, demonstrate the index of the previous datasets contributing to the replay memory.  
Figure 5: \(Cont.\)  
Figure 5: Precision–recall (PR) curves for FT (**a**,**c**,**e**,**g**) and ER (**b**,**d**,**f**,**h**) with Sparse R-CNN on four datasets. Each row corresponds to a dataset (from top to bottom): TableBank, PubLayNet, PubTables-1M, FinTabNet. Different IOU threshold are demonstrated with blue, orange, green, and red which correspond to 50%, 75%, 90%, and 95%, respectively.  
Figure 6: The qualitative results using two methods: (**a**) Fine-tuning, (**b**) Experience replay. Blue represents true positive, and red denotes false positive. The samples are from TableBank, PubLayNet, and PubTables-1M datasets, respectively. The Experience replay method maintains the performance but the fine-tuning approach suffers from false detection and inaccurate bounding boxes.