 We show that explanation methods , firstly, correlate to object locations with higher precisionthan attention, secondly, are able to identify object wordsthat are unsupported by image content, and thirdly, provide guidance to improve and de-bias the model.
<meaning-changed>  We show that explanation methods , firstly, correlate to object locations with higher precisionthan attention, secondly, are able to identify object wordsthat are unsupported by image content, and thirdly, provide guidance to improve and de-bias the model.
 We show that explanation methods provide simultaneously pixel-wise image explanation (supporting and opposing pixels of the input image) and linguistic explanation (supporting and opposing words of the preceding sequence) for each word in the predicted captions. We demonstrate with extensive experiments that explanation methods can 1) reveal more related evidence used by the model to make decisions than attention; 2) correlate to object locations with higher precisionthan attention, secondly, are able to identify object wordsthat are unsupported by image content, and thirdly, provide guidance to improve and de-bias the model.