Zero-Shot Grounding of Objects from Natural Language Queries
Table 6: Ablation study: BM=Base Model, softmax means we classify only one candidate box as foreground, BCE = Binary Cross Entropy means we classify each candidate box as the foreground or background, FL = Focal Loss, Img-Resize: use images of dimension 600×600
['Model', 'Accuracy on RefClef']
We show the performance of our model with different loss functions using the base model of ZSGNet on the validation set of ReferIt  in Table 6. [CONTINUE] Note that using softmax loss by itself places us higher than the previous methods. [CONTINUE] Further using Binary Cross Entropy Loss and Focal loss  give a significant (7%) performance boost which is expected in a single shot framework. [CONTINUE] Finally, image resizing gives another 4% increase.