Multimodal Classification for Analysing Social Media
Table 4: Performance of different fusion models and two baselines on two datasets
['[BOLD] Model', '[BOLD] Reddit  [ITALIC] Accuracy', '[BOLD] Reddit  [ITALIC] F-Macro', '[BOLD] Reddit  [ITALIC] F-Micro', '[BOLD] Flickr  [ITALIC] Accuracy', '[BOLD] Flickr  [ITALIC] F-Macro', '[BOLD] Flickr  [ITALIC] F-Micro']
The results show that visual and textual classifiers have similar accuracy on the reddit dataset as their difference in accuracy is only 1%. In addition, all the fusion techniques have better accuracy than the baselines. The traditional fusion approaches improve the accuracy by at least 5%. This clearly demonstrates the benefits of combing visual and textual data. Our proposed techniques perform the best as they improves the accuracy by 8% and 2% in comparison with using single modality and early/late fusion, respectively. Among the proposed techniques, common space fusion has higher accuracy. However, the difference is small as it is only 0.6%.