Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduce the dense captioning task, which requires a computer vision system to both localize and describe salient regions in images in natural language. The dense captioning task generalizes object detection when the descriptions consist of a single word, and Image Captioning when one predicted region covers the full image. To address the localization and description task jointly  propose a Fully Convolutional Localization Network (FCLN) architecture that processes an image with a single, efficient forward pass, requires no external regions proposals, and can be trained end-to-end with a single round of optimization. The architecture is composed of a Convolutional Network, a novel dense localization layer, and Recurrent Neural Network language model that generates the label sequences.  evaluate our network on the Visual Genome dataset, which comprises 94,000 images and 4,100,000 region-grounded captions.  observe both speed and accuracy improvements over baselines based on current state of the art approaches in both generation and retrieval settings.
['Justin Johnson', 'Andrej Karpathy', 'Li Fei-Fei']
I am writing a research paper on a idea DenseCap: Fully Convolutional Localization Networks for Dense Captioning
[{"from": "human", "value": "I am writing a research paper on a idea DenseCap: Fully Convolutional Localization Networks for Dense Captioning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduce the dense captioning task, which requires a computer vision system to both localize and describe salient regions in images in natural language. The dense captioning task generalizes object detection when the descriptions consist of a single word, and Image Captioning when one predicted region covers the full image. To address the localization and description task jointly  propose a Fully Convolutional Localization Network (FCLN) architecture that processes an image with a single, efficient forward pass, requires no external regions proposals, and can be trained end-to-end with a single round of optimization. The architecture is composed of a Convolutional Network, a novel dense localization layer, and Recurrent Neural Network language model that generates the label sequences.  evaluate our network on the Visual Genome dataset, which comprises 94,000 images and 4,100,000 region-grounded captions.  observe both speed and accuracy improvements over baselines based on current state of the art approaches in both generation and retrieval settings."}]