Adaptively Aligned Image Captioning via Adaptive Attention Time
Table 3: Ablation study of attention type and number of attention heads. For all models, we set λ=1e-4. The results are reported by a single model after self-critical training stage.
['Type', 'Head(s)', 'avg. steps', 'BLEU-1', 'BLEU-2', 'BLEU-3', 'BLEU-4', 'ROUGE', 'CIDEr-D', 'METEOR', 'SPICE']
We compare the two attention functions and explore the impact of using different numbers of attention heads. 2) as the number of attention increases, the number of attention steps reduces and the performance first goes up then down. 4 and 8 bring better performance than other choices.