FiLM: Visual Reasoning with a General Conditioning Layer
Table 4: CLEVR-Humans test accuracy, before (left) and after (right) fine-tuning on CLEVR-Humans data
['[EMPTY]', 'Train', 'Train CLEVR,']
Before fine-tuning, FiLM outperforms prior methods by a smaller margin. After fine-tuning, FiLM reaches a considerably improved final accuracy. In particular, the gain in accuracy made by FiLM upon fine-tuning is more than 50% greater than those made by other models; FiLM adapts data-efficiently using the small CLEVR-Humans dataset.