Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Activity recognition has shown impressive progress in recent years. Hover, the challenges of detecting fine-grained activities and understanding how they are combined into composite activities have been largely overlooked. In this work  approach both tasks and present a dataset which provides detailed annotations to address them. The first challenge is to detect fine-grained activities, which are defined by low inter-class variability and are typically characterized by fine-grained body motions.  explore how human pose and hands can help to approach this challenge by comparing two pose-based and two hand-centric features with state-of-the-art holistic features. To attack the second challenge, recognizing composite activities,  leverage the fact that these activities are compositional and that the essential components of the activities can be obtained from textual descriptions or scripts.  show the benefits of our hand-centric approach for fine-grained activity classification and detection. For composite activity recognition  find that decomposition into attributes allows sharing information across composites and is essential to attack this hard task. Using script data  can recognize novel composites without having training data for them.
['Marcus Rohrbach', 'Anna Rohrbach', 'Michaela Regneri', 'Sikandar Amin', 'Mykhaylo Andriluka', 'Manfred Pinkal', 'Bernt Schiele']
I am writing a research paper on a idea Recognizing Fine-Grained and Composite Activities Using Hand-Centric Features and Script Data
[{"from": "human", "value": "I am writing a research paper on a idea Recognizing Fine-Grained and Composite Activities Using Hand-Centric Features and Script Data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Activity recognition has shown impressive progress in recent years. Hover, the challenges of detecting fine-grained activities and understanding how they are combined into composite activities have been largely overlooked. In this work  approach both tasks and present a dataset which provides detailed annotations to address them. The first challenge is to detect fine-grained activities, which are defined by low inter-class variability and are typically characterized by fine-grained body motions.  explore how human pose and hands can help to approach this challenge by comparing two pose-based and two hand-centric features with state-of-the-art holistic features. To attack the second challenge, recognizing composite activities,  leverage the fact that these activities are compositional and that the essential components of the activities can be obtained from textual descriptions or scripts.  show the benefits of our hand-centric approach for fine-grained activity classification and detection. For composite activity recognition  find that decomposition into attributes allows sharing information across composites and is essential to attack this hard task. Using script data  can recognize novel composites without having training data for them."}]