Encoder-Decoder Interactive Image Segmentation with Inception-Atrous-Collation Blocks

We propose a modular encoder-decoder model framework, NEED (NAS-Enhanced Encoder-Decoder), for interactive image segmentation. As part of our solution, we introduce a general-purpose convolutional module, Inception-Atrous-Collation (IAC) blocks. IAC structures collate multiple kernel and dilation sizes with residual information from previous network layers to generate information-rich image features. Our network additionally incorporates “context-aware” skip connections, which serve to amplify the influence of the user-provided interactive features throughout the model. With this general structure in place, we optimize our final network architecture using a Neural Architecture Search (NAS) procedure. The resultant model is a lightweight network that outperforms state-of-the-art algorithms with respect to both overall segmentation accuracy (IOU) and fine-grain boundary segmentation accuracy (bIOU) - a metric we introduce. We provide experimental results across several standard datasets, in addition to ablation studies to validate the effectiveness of our algorithm.