## 4 Settings, Results and Discussion
### Comparisons with State-of-the-Arts
#### 4.3.2 Performance on VQA-CP v1  
**Settings.** The authors of this paper compare the state-of-the-art methods to LBSD-LMH and VQA-CP v1. According to the principle and method of reducing language bias, the authors of this paper divide them into groups: (1) Strengthening visual information [24, 25]. (2) Weakening language priors [29, 31, 32]. (3) Using various data enhancement and data balance [36, 68]. Moreover, the authors of this paper conducted another experiment based on the official codes of the methods, as the results of some methods on VQA-CP v1 were not shown.  
**Results.** As shown in Table 7, compared with the methods in group 1 and group 2, LBSD realizes the best performance on VQA-CP v1. In particular, LBSD improves the performance of LMH and Reweight by 0.66% and 2.2%, respectively. The results show that the proposed method is effective for different datasets and is effective for different types of debiased methods. The results verify the effectiveness of LBSD.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Venue**} & \multicolumn{4}{c}{**VQA-CP v2 Test \(\uparrow\)**} & \multicolumn{4}{c}{**VQA v2 val \(\uparrow\)**} & \multicolumn{4}{c}{**Gap\(\Delta\)\(\downarrow\)**} \\  & & **All** & **Yes/No Num** & **Other** & **All** & **Yes/No Num** & **Other** & **All** & **Other** \\ \hline GVQA [67] & _CVPR’18_ & 31.30 & 57.99 & 13.68 & 22.14 & 48.24 & 72.03 & 31.17 & 34.65 & 16.94 & 12.51 \\ UpDn [56] & _CVPR’18_ & 39.74 & 42.27 & 11.93 & 46.05 & 63.48 & 81.18 & 42.14 & 55.66 & 23.74 & 9.61 \\ \hline \multicolumn{10}{l}{_methods based on strengthening visual information_} \\ \hline AttAlign [24] & _ICCV’19_ & 39.37 & 43.02 & 11.89 & 45.00 & 63.24 & 80.99 & 42.55 & 55.22 & 23.87 & 10.22 \\ HINT [24] & _ICCV’19_ & 46.73 & 67.27 & 10.61 & 45.88 & 63.38 & 81.18 & 42.99 & 55.56 & 16.55 & 9.68 \\ ReGAT [26] & _ICCV’19_ & 40.42 & – & – & – & 67.18 & – & – & – & 26.76 & – \\ VGQE [28] & _ECCV’20_ & 48.75 & – & – & – & 64.04 & – & – & – & 15.29 & – \\ ESR [27] & _ACL’20_ & 48.9 & 69.8 & 11.3 & 47.8 & 62.6 & – & – & – & 13.70 & – \\ KAN [69] & _TNNLS’20_ & 42.60 & 42.12 & 15.52 & 50.28 & – & – & – & – & – & – \\ \hline \multicolumn{10}{l}{_methods based on weakening language priors:_} \\ \hline AReg [29] & _NeurIPS’18_ & 41.17 & 65.49 & 15.48 & 35.48 & 62.75 & 79.84 & 42.35 & 55.16 & 21.58 & 19.68 \\ GRL [30] & _ACL’19_ & 42.33 & 59.74 & 14.78 & 40.76 & 51.92 & – & – & 9.59 & – \\ RUBi [31] & _NeurIPS’19_ & 45.23 & 64.85 & 11.83 & 44.11 & 50.56 & 49.45 & 41.02 & 53.95 & 5.33 & 9.84 \\ LM [32] & _EMNLP’19_ & 48.78 & 72.78 & 14.61 & 45.58 & 63.26 & 81.16 & 42.22 & 55.22 & 14.48 & 9.64 \\ LMH [32] & _EMNLP’19_ & 52.05 & 69.81 & 44.46 & 45.54 & 61.64 & 77.85 & 40.03 & 55.04 & 9.59 & 9.50 \\ CF-VQA [34] & _CVPR’21_ & 53.55 & 91.15 & 13.03 & 44.97 & 63.54 & 82.51 & 43.96 & 54.30 & 9.99 & 9.33 \\
**LBSD-LMH** & _Ours_ & **54.27** & 75.49 & 44.02 & **45.96** & 57.95 & 69.22 & 38.19 & 54.63 & **3.68** & **8.67** \\ \hline \multicolumn{10}{l}{_methods based on data argumentation_} \\ \hline RandImg [70] & _NeurIPS’20_ & 55.37 & 83.89 & 41.60 & 44.20 & 57.24 & 76.53 & 33.87 & 48.57 & 1.87 & 4.37 \\ CVL [68] & _CVPR’20_ & 42.12 & 45.72 & 12.45 & 48.34 & – & – & – & – & – & – \\ LMH+CSS [36] & _CVPR’20_ & 58.95 & 84.37 & 49.42 & 48.21 & 59.91 & 73.25 & 39.77 & 55.11 & 0.96 & 6.90 \\ LMH+CSS+CL [37] & _EMNLP’20_ & 59.18 & 86.99 & 49.89 & 47.16 & 57.29 & 67.27 & 38.40 & 54.71 & 1.89 & 7.55 \\ Unshuffling [42] & _ICCV’21_ & 42.39 & 47.72 & 14.43 & 47.24 & 61.08 & 78.32 & 42.16 & 52.81 & 18.69 & 5.57 \\ X-GGM [44] & _ACM_ & 45.71 & 43.48 & 27.65 & 52.34 & – & – & – & – & – & – \\ MM’21 & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison (Accuracies (%)) on the VQA-CP v2 test set and the VQA v2 val set of state-of-the-art models. The gap means the performance difference between VQA v2 and VQA-CP v2.