The present disclosure provides an image segmentation apparatus and method, and a related computing device. The image segmentation apparatus comprises: a feature extraction module, configured to extract, from an inputted image, N image semantic features with different scales, N being an integer not less than three; and a feature processing module, including a plurality of cascaded dense refinement networks and configured to perform feature processing on the N image semantic features to obtain a binarized mask image of the inputted image. Each dense refinement network is configured to generate, from a plurality of semantic features inputted thereto, low-frequency semantic features in the following manner: performing dense connection convolution processing on the plurality of semantic features to obtain a plurality of corresponding global image features, performing feature fusion on the plurality of global image features to obtain fused global image features, and performing pooling processing on the fused global image features to generate and output low-frequency semantic features. The plurality of semantic features are selected from a group formed by the N image semantic features and the low-frequency semantic features generated by the dense refinement network. The feature processing module is configured to obtain the binarized mask image on the basis of the low-frequency semantic features generated by the dense refinement network.
IMAGE SEGMENTATION APPARATUS AND METHOD, AND RELATED COMPUTING DEVICE