Zero-Shot Grounding of Objects from Natural Language Queries
Table 4: Accuracy across various unseen splits. For Flickr-Split-0,1 we use Accuracy with IoU threshold of 0.5. Since Visual Genome annotations are noisy we additionally report Accuracy with IoU threshold of 0.3. The second row denotes the IoU threshold at which the Accuracy is calculated. “B” and “UB” denote the balanced and unbalanced sets.
['Method', 'Net', 'Flickr- Split-0', 'Flickr- Split-1', 'VG-2B 0.3', 'VG-2B 0.5', 'VG-2UB 0.3', 'VG-2UB 0.5', 'VG-3B 0.3', 'VG-3B 0.5', 'VG-3UB 0.3', 'VG-3UB 0.5']
Table 4 shows the performance of our ZSGNet model compared to QRG on the four unseen splits [CONTINUE] Across all splits, ZSGNet shows 4−8% higher performance than QRG even though the latter has seen more data [CONTINUE] we observe that the accuracy obtained on Flickr-Split-0,1 are higher than the VG-split likely due to larger variation of the referred objects in Visual Genome. [CONTINUE] Finally, the accuracy remains the same across the balanced and unbalanced sets indicating the model performs well across all clusters as our training set is balanced.