Length-Controllable Image Captioning
Table 2: Performance of LaBERT on MS COCO Karpathy’s test split. R represents ROUGE. The results of AoANet and VLP are obtained from their papers.
['Metrics', 'S', 'C', 'M', 'R', 'B@1', 'B@2', 'B@3', 'B@4']
We further apply the length level embedding to our proposed non-autoregressive image captioning model, LaBERT. To make comparisons, we implement a single-level version of LaBERT whose length range is [1,25]. Moreover, the number of refine steps for 4-level LaBERT is set to 10, 15, 20 and 25, for level 1-4, respectively, so that we can compare LaBERT with the autoregressive image captioning models under roughly the same decoding complexity. Analogously, The total decoding steps of the single-level LaBERT is set to 25. Moreover, 4-level LaBERT yields significantly higher SPICE scores on level 3 and level 4. These results demonstrate the advantage of the length level embedding in our non-autoregressive image caption model. Moreover, under the same computational complexity, 4-level LaBERT performs comparably with 4-level AoANet and VLP as well as the SOTA results, which demonstrates the effectiveness of length-level embedding in non-autoregressive image caption decoding.