Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Relative (comparative) attributes are promising for thematic ranking of visual entities, which also aids in recognition tasks. Hover, attribute rank learning often requires a substantial amount of relational supervision, which is highly tedious, and apparently impractical for real-world applications. ,  introduce the Semantic Transform, which under minimal supervision, adaptively finds a semantic feature space along with a class ordering that is related in the best possible way. Such a semantic space is found for every attribute category. To relate the classes under ak supervision, the class ordering needs to be refined according to a cost function in an iterative procedure. This problem is ideally NP-hard, and  thus propose a constrained search tree formulation for the same. Driven by the adaptive semantic feature space representation, our model achieves the best results to date for all of the tasks of relative, absolute and zero-shot classification on two popular datasets.
['Sukrit Shankar', 'Joan Lasenby', 'Roberto Cipolla']
I am writing a research paper on a idea Semantic Transform: Weakly Supervised Semantic Inference for Relating Visual Attributes
[{"from": "human", "value": "I am writing a research paper on a idea Semantic Transform: Weakly Supervised Semantic Inference for Relating Visual Attributes"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Relative (comparative) attributes are promising for thematic ranking of visual entities, which also aids in recognition tasks. Hover, attribute rank learning often requires a substantial amount of relational supervision, which is highly tedious, and apparently impractical for real-world applications. ,  introduce the Semantic Transform, which under minimal supervision, adaptively finds a semantic feature space along with a class ordering that is related in the best possible way. Such a semantic space is found for every attribute category. To relate the classes under ak supervision, the class ordering needs to be refined according to a cost function in an iterative procedure. This problem is ideally NP-hard, and  thus propose a constrained search tree formulation for the same. Driven by the adaptive semantic feature space representation, our model achieves the best results to date for all of the tasks of relative, absolute and zero-shot classification on two popular datasets."}]