SMArT: Training Shallow Memory-aware Transformers for Robotic Explainability
TABLE IV: Object coverage analysis on the ACVR Robotic Vision Challenge dataset, when varying the minumum object area threshold.
['Method', 'Coverage >1%', 'Coverage >3%', 'Coverage >5%', 'Coverage >10%']
As it can be observed, our approach is capable of mentioning most of the objects on the scene which cover at least 10% of the image, and achieves a 0.76 coverage score when thresholding at 1% of the area, thus taking into account also small objects. Reported results also confirm the findings on the effectiveness of the persistent memory vectors observed on COCO.