MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning
Table 4: Model ablation on ActivityNet Captions ae-val split. Re. indicates whether sentence-level recurrence is used. mem. len. indicates the length of the memory state. MART w/o re. denotes a MART variant without recurrence. Top two scores are highlighted.
['[EMPTY]', '#hidden layers', 'mem. len.', 'Re.', 'B@4', 'M', 'C', 'R@4\xa0↓']
MART models with recurrence have better overall performance than the variant without, suggesting the effectiveness of our recurrent memory design. We choose to use the model with 2 hidden layers and memory state length 1 as it shows a good balance between performance and computation.