This thread discusses the comparison of different computer vision concepts. Fully Convolutional Networks for Semantic Segmentation is a very popular deep learning approach for semantic segmentation. What are the popular or state-of-the-art deep learning approaches for object detection? It seems to me that these two problems share quite some similarities. Are there any framework or methodology that study leveraging the result of solving one problem, i.e., semantic segmentation to solve object detection.
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Have a look at this link. It contains a list of networks for different computer vision tasks.
R-FCN: Object Detection via Region-based Fully Convolutional Networks is a new take on Object Detection that uses FCN(which is used for Semantic Segmentation) for Object Detection.
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