Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Scaling up visual category recognition to large numbers of classes remains challenging. A promising research direction is zero-shot learning, which does not require any training data to recognize new classes, but rather relies on some form of auxiliary information describing the new classes. Ultimately, this may allow to use textbook knowledge that humans employ to learn about new classes by transferring knowledge from classes they know ll. The most successful zero-shot learning approaches currently require a particular type of auxiliary information – namely attribute annotations performed by humans – that is not readily available for most classes. Our goal is to circumvent this bottleneck by substituting such annotations by extracting multiple pieces of information from multiple unstructured text sources readily available on the b. To compensate for the aker form of auxiliary information,  incorporate stronger supervision in the form of semantic part annotations on the classes from which  transfer knowledge.  achieve our goal by a joint embedding framework that maps multiple text parts as ll as multiple semantic parts into a common space. Our results consistently and significantly improve on the state-of-the-art in zero-short recognition and retrieval.
['Zeynep Akata', 'Mateusz Malinowski', 'Mario Fritz', 'Bernt Schiele']
I am writing a research paper on a idea Multi-cue Zero-Shot Learning with Strong Supervision
[{"from": "human", "value": "I am writing a research paper on a idea Multi-cue Zero-Shot Learning with Strong Supervision"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Scaling up visual category recognition to large numbers of classes remains challenging. A promising research direction is zero-shot learning, which does not require any training data to recognize new classes, but rather relies on some form of auxiliary information describing the new classes. Ultimately, this may allow to use textbook knowledge that humans employ to learn about new classes by transferring knowledge from classes they know ll. The most successful zero-shot learning approaches currently require a particular type of auxiliary information \u2013 namely attribute annotations performed by humans \u2013 that is not readily available for most classes. Our goal is to circumvent this bottleneck by substituting such annotations by extracting multiple pieces of information from multiple unstructured text sources readily available on the b. To compensate for the aker form of auxiliary information,  incorporate stronger supervision in the form of semantic part annotations on the classes from which  transfer knowledge.  achieve our goal by a joint embedding framework that maps multiple text parts as ll as multiple semantic parts into a common space. Our results consistently and significantly improve on the state-of-the-art in zero-short recognition and retrieval."}]