Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Action Unit (AU) detection from facial images is an important classification task in affective computing. Hover most existing approaches use carefully engineered feature extractors along with off-the-shelf classifiers. There has also been less focus on how ll classifiers generalize when tested on different datasets. In our paper,  propose a multi-label convolutional neural network approach to learn a shared representation beten multiple AUs directly from the input image. Experiments on three AU datasets- CK+, DISFA and BP4D indicate that our approach obtains competitive results on all datasets. Cross-dataset experiments also indicate that the network generalizes ll to other datasets, even when under different training and testing conditions.
['Sayan Ghosh', 'Eugene Laksana', 'Stefan Scherer', 'Louis-Philippe Morency']
I am writing a research paper on a idea A multi-label convolutional neural network approach to cross-domain action unit detection
[{"from": "human", "value": "I am writing a research paper on a idea A multi-label convolutional neural network approach to cross-domain action unit detection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Action Unit (AU) detection from facial images is an important classification task in affective computing. Hover most existing approaches use carefully engineered feature extractors along with off-the-shelf classifiers. There has also been less focus on how ll classifiers generalize when tested on different datasets. In our paper,  propose a multi-label convolutional neural network approach to learn a shared representation beten multiple AUs directly from the input image. Experiments on three AU datasets- CK+, DISFA and BP4D indicate that our approach obtains competitive results on all datasets. Cross-dataset experiments also indicate that the network generalizes ll to other datasets, even when under different training and testing conditions."}]