Cognitive resilience: Unraveling the proficiency of image-captioning models to interpret masked visual content
This addresses the problem of understanding model robustness for researchers in computer vision and AI, but it is incremental as it builds on existing image captioning methods.
The study investigated how well image captioning models can interpret masked visual content, finding that they generate captions closely resembling original content and even produce descriptive text beyond observable masked areas, though performance declines with larger masked regions.
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.