  This study explores the ability of Image Captioning (IC) models to decode
masked visual content sourced from diverse datasets. Our findings reveal the IC
model's capability to generate captions from masked images, closely resembling
the original content. Notably, even in the presence of masks, the model adeptly
crafts descriptive textual information that goes beyond what is observable in
the original image-generated captions. While the decoding performance of the IC
model experiences a decline with an increase in the masked region's area, the
model still performs well when important regions of the image are not masked at
high coverage.
