Diverse beam Search: Decoding Diverse Solutions from Neural Sequence Models
Table 2: Top: Oracle SPICE@k and distinct n-grams on the VQG task at B=20. Bottom: Results by difficulty class, highlighting the relative improvement over BS.
['[EMPTY]', '[BOLD] Method', '[BOLD] SPICE', '[BOLD] Oracle SPICE@k @5', '[BOLD] Oracle SPICE@k @10', '[BOLD] Oracle SPICE@k @20', '[BOLD] Distinct n-Grams n = 1', '[BOLD] Distinct n-Grams 2', '[BOLD] Distinct n-Grams 3', '[BOLD] Distinct n-Grams 4']
We find DBS significantly outperforms the baseline methods on this task both on standard metrics (SPICE) and measure of diversity. We also observe that gap between DBS and the baseline methods is more pronounced than in the captioning task and attribute this to the increased variety of possible visually grounded questions compared to captions which often describe only a few major salient objects. The general trend that more complex images benefit more from diverse decoding also persists in this setting.