Image Captioning: Transforming Objects into Words
Table 3: Ablation Study. All metrics are reported for the validation and the test split, after training with softmax cross-entropy for 30 epochs. The Transformer (Transf) and the Object Relational Transformer (ObjRel Transf) is described in detail in Section 3
['Algorithm', '[EMPTY]', 'CIDEr-D', 'SPICE', 'BLEU-1', 'BLEU-4', 'METEOR', 'ROUGE-L']
We show the Bottom-Up and Top-Down algorithm Anderson et al. The second row replaces the LSTM with a Transformer network. The third row includes the proposed geometric attention. The last row includes beam search with beam size 2. The contribution of the Object Relation Transformer is small for METEOR, but significant for CIDEr-D and the BLEU metrics. Overall we can see the most improvements on the CIDEr-D and BLEU-4 scores.