Knowledge-Enriched Visual Storytelling
Table 2: Direct comparison evaluation of KG-Story model. Numbers indicate average rank given to stories (from 1 to 5, lower is better.) Stories generated by KG-Story using either OpenIE or Visual Genome are on average ranked significantly better (lower) than that of GLAC (unpaired t-test, p<0.05, N=2500).
['Human Evaluation (Story Displayed  [BOLD] with Images)', 'Human Evaluation (Story Displayed  [BOLD] with Images)  [BOLD] GLAC ', 'Human Evaluation (Story Displayed  [BOLD] with Images)  [BOLD] No KG', 'Human Evaluation (Story Displayed  [BOLD] with Images)  [BOLD] OpenIE', 'Human Evaluation (Story Displayed  [BOLD] with Images)  [BOLD] Visual Genome', 'Human Evaluation (Story Displayed  [BOLD] with Images)  [BOLD] Human']
Sx5SSx1SSS0Px1 Ranking Stories with Images We conducted a human evaluation using crowd workers recruited from Amazon Mechanical Turk. Each task displayed one sequence of photos and several generated stories based on the sequence. Five workers were recruited for each task to rank the story quality, “from the Best Story to the Worst Story.” The compensation was $0.10 per task. Four models were included in this evaluation: (i) KG-Story using OpenIE as the knowledge graph, (ii) KG-Story using VisualGenome as the knowledge graph, (iii) KG-Story without any knowledge graphs, and Note that in the first two models, only the knowledge graph for exploring inter-image relations in Stage 2 is different, while Stage 1 and Stage 3 remain identical. As we also included (v) human-written stories for comparison, the ranking number is from 1 (the best) to 5 (the worst). Stories generated by KG-Story using either OpenIE or Visual Genome are on average ranked significantly better (lower) than that of GLAC (unpaired t-test, p<0.05, N = 2500), whereas stories generated by KG-Story without using any knowledge graphs ranked significantly worst (higher) (unpaired t-test, p<0.01, N = 2500).