Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Given a video and associated text,  propose an automatic annotation scheme in which  employ a latent topic model to generate topic distributions from ighted text and then modify these distributions based on visual similarity.  apply this scheme to location annotation of a television series for which transcripts are available. The topic distributions allow us to avoid explicit classification, which is useful in cases where the exact number of locations is unknown. Moreover, many locations are unique to a single episode, making it impossible to obtain representative training data for a supervised approach. Our method first segments the episode into scenes by fusing cues from both images and text.  then assign location-oriented ights to the text and generate topic distributions for each scene using Latent Dirichlet Allocation. Finally,  update the topic distributions using the distributions of visually similar scenes.  formulate our visual similarity beten scenes as an Earth Mover’s Distance problem.  quantitatively validate our multi-modal approach to segmentation and qualitatively evaluate the resulting location annotations. Our results demonstrate that  are able to generate accurate annotations, even for locations only seen in a single episode.
['Chris Engels', 'Koen Deschacht', 'Jan Hendrik Becker', 'Tinne Tuytelaars', 'Sien Moens', 'Luc J. Van Gool']
I am writing a research paper on a idea Automatic annotation of unique locations from video and text
[{"from": "human", "value": "I am writing a research paper on a idea Automatic annotation of unique locations from video and text"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Given a video and associated text,  propose an automatic annotation scheme in which  employ a latent topic model to generate topic distributions from ighted text and then modify these distributions based on visual similarity.  apply this scheme to location annotation of a television series for which transcripts are available. The topic distributions allow us to avoid explicit classification, which is useful in cases where the exact number of locations is unknown. Moreover, many locations are unique to a single episode, making it impossible to obtain representative training data for a supervised approach. Our method first segments the episode into scenes by fusing cues from both images and text.  then assign location-oriented ights to the text and generate topic distributions for each scene using Latent Dirichlet Allocation. Finally,  update the topic distributions using the distributions of visually similar scenes.  formulate our visual similarity beten scenes as an Earth Mover\u2019s Distance problem.  quantitatively validate our multi-modal approach to segmentation and qualitatively evaluate the resulting location annotations. Our results demonstrate that  are able to generate accurate annotations, even for locations only seen in a single episode."}]