FiLM: Visual Reasoning with a General Conditioning Layer
Table 1: CLEVR accuracy (overall and per-question-type) by baselines, competing methods, and FiLM. (*) denotes use of extra supervision via program labels. (†) denotes use of data augmentation. (‡) denotes training from raw pixels.
['Model', '[BOLD] Overall', 'Count', 'Exist', 'Compare Numbers', 'Query Attribute', 'Compare Attribute']
For methods not using extra supervision, FiLM roughly halves state-of-the-art error (from 4.5% to 2.3%). Note that using pre-trained image features as input can be viewed as a form of data augmentation in itself but that FiLM performs equally well using raw pixel inputs. Interestingly, the raw pixel model seems to perform better on lower-level questions (i.e. querying and comparing attributes) while the image features model seems to perform better on higher-level questions (i.e. compare numbers of objects).