Probabilistic framework for solving Visual Dialog
Table 1: Aleatoric loss variant in VisDial-v1.0 in test-std dataset.
['[BOLD] Loss', '[BOLD] R1', '[BOLD] R5', '[BOLD] R10', '[BOLD] MRR', '[BOLD] Mean']
Aleatoric Cross Entropy consists of distorted (Gaussian Cross Entropy (GCE)), undistorted (Cross Entropy (CE)) loss, and Variance Equalizer (VE) loss.. We use these models as our baseline and compare other variations of our model with the best single loss function. The GCE loss performs best among all the 3 losses. This is reasonable as GCE can guide the loss function to minimize the variance in the data. We see an improvement of around 4% in R@1, 8% in R@5 score, 9% in R@10 score and 5% in MRR score from the baseline score. The combination of GCE loss and CE performs best among all the 3 cases. The third block takes into consideration all the loss functions ACE (GCE+CE+VE) and we see an improvement of around 6% in R@1, 10% in R@5 score, 10% in R@10 score and 7% in MRR score from the baseline score. The behaviour of dialog turn for As dialog progress the width of the dialog turn decreases. There is an eventual decreasing trend. The second part of the figure shows how variance equalizer loss varies over different turns in visual dialog. There is an eventual increasing trend. We can observe the third and fourth turn is more uncertain. This indicates that to have a successful dialog, it basically depend on the central part of the dialog not only start and end tuns of the dialog.