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Both high-level and high-resolution feature representations are of greatimportance in various visual understanding tasks. To acquire high-resolutionfeature maps with high-level semantic information, one common strategy is toadopt dilated convolutions in the backbone networks to extract high-resolutionfeature maps, such as the dilatedFCN-based methods for semantic segmentation.However, due to many convolution operations are conducted on thehigh-resolution feature maps, such methods have large computational complexityand memory consumption. In this paper, we propose one novel holistically-guideddecoder which is introduced to obtain the high-resolution semantic-rich featuremaps via the multi-scale features from the encoder. The decoding is achievedvia novel holistic codeword generation and codeword assembly operations, whichtake advantages of both the high-level and low-level features from the encoderfeatures. With the proposed holistically-guided decoder, we implement theEfficientFCN architecture for semantic segmentation and HGD-FPN for objectdetection and instance segmentation. The EfficientFCN achieves comparable oreven better performance than state-of-the-art methods with only 1/3 of theircomputational costs for semantic segmentation on PASCAL Context, PASCAL VOC,ADE20K datasets. Meanwhile, the proposed HGD-FPN achieves $>2\%$ higher meanAverage Precision (mAP) when integrated into several object detectionframeworks with ResNet-50 encoding backbones.

A Holistically-Guided Decoder for Deep Representation Learning with Applications to Semantic Segmentation and Object Detection