Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present here some transmedia similarity measures that  recently designed by adopting some "intermediate level" fusion approaches. The main idea is to use some principles coming from pseudo-relevance feedback and, more specifically, transmedia pseudo-relevance feedback for enriching the mono-media representation of an object with features coming from the other media. One issue that arises when adopting such a strategy is to determine how to compute the mono-media similarity beten an aggregate of objects coming from a first (pseudo-)feedback step and one single multimodal object.  propose two alternative ways of adressing this issue, that result in what  called the "transmedia document reranking" and "complementary feedback" methods respectively.#R##N##R##N#For the ImageCLEF - Photo Retrieval Task, it appears that mono-media retrieval performance is more or less equivalent for pure image and pure text content (around 20% MAP). Using our transmedia pseudofeedback-based similarity measures allod us to dramatically increase the performance by ~50% (relative). From a cross-lingual perspective, the use of domain-specific, corpus-adapted probabilistic dictionaries seems to offer better results than the use of a broader, more general standard dictionary. With respect to the monolingual baselines, multilingual runs show a slight degradation of retrieval performance ( ~6 to 10% relative).
['Stéphane Clinchant', 'Jean-Michel Renders', 'Gabriela Csurka']
I am writing a research paper on a idea Trans-Media Pseudo-Relevance Feedback Methods in Multimedia Retrieval
[{"from": "human", "value": "I am writing a research paper on a idea Trans-Media Pseudo-Relevance Feedback Methods in Multimedia Retrieval"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present here some transmedia similarity measures that  recently designed by adopting some \"intermediate level\" fusion approaches. The main idea is to use some principles coming from pseudo-relevance feedback and, more specifically, transmedia pseudo-relevance feedback for enriching the mono-media representation of an object with features coming from the other media. One issue that arises when adopting such a strategy is to determine how to compute the mono-media similarity beten an aggregate of objects coming from a first (pseudo-)feedback step and one single multimodal object.  propose two alternative ways of adressing this issue, that result in what  called the \"transmedia document reranking\" and \"complementary feedback\" methods respectively.#R##N##R##N#For the ImageCLEF - Photo Retrieval Task, it appears that mono-media retrieval performance is more or less equivalent for pure image and pure text content (around 20% MAP). Using our transmedia pseudofeedback-based similarity measures allod us to dramatically increase the performance by ~50% (relative). From a cross-lingual perspective, the use of domain-specific, corpus-adapted probabilistic dictionaries seems to offer better results than the use of a broader, more general standard dictionary. With respect to the monolingual baselines, multilingual runs show a slight degradation of retrieval performance ( ~6 to 10% relative)."}]