Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Convolutional Neural Network (CNN) has been used successfully in solving different computer vision tasks such as classification, detection, and segmentation.  addresses the problem of estimating object depth from a single RGB image. While stereo depth estimation is a straightforward task, predicting depth map of an object from a single RGB image is a more challenging task due to the lack of information from various image cues. Prior work focuses on exploiting geometric information or hand-crafted features. Also, they proposed the L2 norm to perform the optimization of a CNN during the training process. Using the L2 norm in regression tasks for optimization will bias the model. To address this problem,  propose a regression model with a fully convolutional neural network. To achieve robustness to outliers,  optimize the model using Tukey's biight loss function, which is an M-estimator that is robust against outliers. The predictions are given by a single fully CNN without any post-processing techniques. In our experiments,  show that the quantitative and the qualitative results of using Tukey's biight loss for optimization are better than of using L2 norm.
['Ahmed J. Afifi', 'Olaf Hellwich']
I am writing a research paper on a idea Object Depth Estimation from a Single Image Using Fully Convolutional Neural Network
[{"from": "human", "value": "I am writing a research paper on a idea Object Depth Estimation from a Single Image Using Fully Convolutional Neural Network"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Convolutional Neural Network (CNN) has been used successfully in solving different computer vision tasks such as classification, detection, and segmentation.  addresses the problem of estimating object depth from a single RGB image. While stereo depth estimation is a straightforward task, predicting depth map of an object from a single RGB image is a more challenging task due to the lack of information from various image cues. Prior work focuses on exploiting geometric information or hand-crafted features. Also, they proposed the L2 norm to perform the optimization of a CNN during the training process. Using the L2 norm in regression tasks for optimization will bias the model. To address this problem,  propose a regression model with a fully convolutional neural network. To achieve robustness to outliers,  optimize the model using Tukey's biight loss function, which is an M-estimator that is robust against outliers. The predictions are given by a single fully CNN without any post-processing techniques. In our experiments,  show that the quantitative and the qualitative results of using Tukey's biight loss for optimization are better than of using L2 norm."}]