Incorporating Copying Mechanism in Image Captioning for Learning Novel Objects
Table 2: Novel, F1 and Accuracy scores of our proposed model and other state-of-the-art methods on ImageNet dataset. All values are reported as percentage (%).
['Model', 'Novel', 'F1', 'Accuracy']
Evaluation on ImageNet. By only adopting the MSCOCO as the training data for the CNN plus RNN system, our LSTM-C (One hot+Glove) makes the relative improvement over NOC (One hot+Glove) by 4.3%, 4.9% and 17.8% in Novel, F1 and Accuracy, respectively. The results basically indicate the advantage of exploiting both generative and copying mechanisms in the CNN plus RNN system for novel object captioning, even when scaling into ImageNet images with hundreds of novel objects.