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. 2023 Mar 22;10:158. doi: 10.1038/s41597-023-02036-y

Table 5.

performance comparison of the variants of VSL models on MedVidQA dataset.

Models IoU = 0.3 IoU = 0.5 IoU = 0.7 mIoU
Random Mode 8.38 1.93 1.21 6.89
Random Guess 7.74 3.22 0.64 5.96
VSL-BASE FPL 400 19.35 6.45 3.22 18.08
FPL 600 19.35 10.96 4.51 19.20
FPL 800 21.93 12.25 5.80 20.15
FPL 1000 21.93 7.74 3.87 18.86
FPL 1200 22.58 9.67 5.16 19.97
FPL 1400 25.16 8.38 4.51 19.33
VSL-QGH 25.81 14.20 6.45 20.12

Here FPL refers to the frame position length considered during training the respective models.