Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   aim to recognize scenes in images without using any scene images as training data. Different from attribute based approaches,  do not carefully select the training classes to match the unseen scene classes. Instead,  propose a pooling over ten thousand of off-the-shelf object classifiers. To steer the knowledge transfer beten objects and scenes  learn a semantic embedding with the aid of a large social multimedia corpus. Our key contributions are:  are the first to investigate pooling over ten thousand object classifiers to recognize scenes without examples;  explore the ontological hierarchy of objects and analyze the influence of object classifiers from different hierarchy levels;  exploit object positions in scene images and  demonstrate new scene retrieval scenarios with complex queries. Finally,  outperform attribute representations on two challenging scene datasets, SUNAttributes and Places2.
['Svetlana Kordumova', 'Thomas Mensink', 'Cees G. M. Snoek']
I am writing a research paper on a idea Pooling Objects for Recognizing Scenes without Examples
[{"from": "human", "value": "I am writing a research paper on a idea Pooling Objects for Recognizing Scenes without Examples"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   aim to recognize scenes in images without using any scene images as training data. Different from attribute based approaches,  do not carefully select the training classes to match the unseen scene classes. Instead,  propose a pooling over ten thousand of off-the-shelf object classifiers. To steer the knowledge transfer beten objects and scenes  learn a semantic embedding with the aid of a large social multimedia corpus. Our key contributions are:  are the first to investigate pooling over ten thousand object classifiers to recognize scenes without examples;  explore the ontological hierarchy of objects and analyze the influence of object classifiers from different hierarchy levels;  exploit object positions in scene images and  demonstrate new scene retrieval scenarios with complex queries. Finally,  outperform attribute representations on two challenging scene datasets, SUNAttributes and Places2."}]