Acquiring Common Sense Spatial Knowledge through Implicit Spatial Templates
Table 1: Results on the Raw test data.
['Implicit', 'REG [ITALIC] EMB', 'R2 0.704', 'acc [ITALIC] y 0.751', 'F1 [ITALIC] y 0.750', 'r [ITALIC] x 0.892', 'r [ITALIC] y 0.835', 'IoU 0.117', 'px-acc -']
Especially noteworthy is the finding that relative locations can be predicted from implicit spatial language approximately as accurately as from explicit spatial language. Interestingly, unlike the other metrics, the IoU (which only counts a prediction as correct if the overlap between true and predicted boxes is larger than 50%) is clearly higher in explicit than in implicit language, which suggests that implicit templates exhibit more flexibility on the Object’s location. Hence, “blurrier” predictions such as those of PIX can be a good choice in some applications, e.g., computing the soft fit between images and templates to perform (spatially informed) image retrieval. We also observe that models with 1H embeddings tend to perform better, yet differences are generally only significant against RND.