Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data
Table 1: We compare DCC before transfer (No Transfer) to DCC with delta transfer (ΔT) and DCC with direct transfer (DT). We also compare to another competitive caption generation model (LRCN). We measure our models ability to insert new words into a generated sentence with the F1-score. We also report Bleu-1 and METEOR, which indicates overall sentence quality. DCC successfully incorporates new words and improves sentence quality. (Values in %)
['[EMPTY]', 'LRCN\xa0', 'No Transfer', 'ΔT', 'DT']
Direct Transfer Versus Delta Transfer. across the eight held-out training classes for the delta transfer and direct transfer methods. We also report the BLEU-1 score, which measures the overlap between generated words and words in reference sentences. By measuring the METEOR score, we ensure that our model maintains sentence fluency when inserting new objects. DCC consistently increases METEOR scores indicating that overall sentence quality improves with DCC. The direct transfer method improves F1-scores, BLEU, and METEOR scores by a larger amount than the delta transfer method and is thus used for the remainder of our experiments.