SEMEDA: Enhancing Segmentation Precision with Semantic Edge Aware Loss

6 May 2019Yifu ChenArnaud DapognyMatthieu Cord

While nowadays deep neural networks achieve impressive performances on semantic segmentation tasks, they are usually trained by optimizing pixel-wise losses such as cross-entropy. As a result, the predictions outputted by such networks usually struggle to accurately capture the object boundaries and exhibit holes inside the objects... (read more)

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