Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. Hover, these multi-scale convolutional features are pooled into a single scale-invariant representation.  argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a Bag of Deep Features framework. #R##N#Our approach encodes multi-scale information explicitly during the image encoding stage.  propose two strategies to encode multi-scale information explicitly in the final image representation.  validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state-of-the-art.
['Fahad Shahbaz Khan', 'Joost van de Weijer', 'Rao Muhammad Anwer', 'Andrew D. Bagdanov', 'Michael Felsberg', 'Jorma Laaksonen']
I am writing a research paper on a idea Scale Coding Bag of Deep Features for Human Attribute and Action Recognition.
[{"from": "human", "value": "I am writing a research paper on a idea Scale Coding Bag of Deep Features for Human Attribute and Action Recognition."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. Hover, these multi-scale convolutional features are pooled into a single scale-invariant representation.  argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a Bag of Deep Features framework. #R##N#Our approach encodes multi-scale information explicitly during the image encoding stage.  propose two strategies to encode multi-scale information explicitly in the final image representation.  validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state-of-the-art."}]