Bridging Category-level and Instance-level Semantic Image Segmentation

Understand

We propose an approach to instance-level image segmentation that is built on top of category-level segmentation.

• Specifically, for each pixel in a semantic category mask, its corresponding instance bounding box is predicted using a deep fully convolutional regression network.
• Thus it follows a different pipeline to the popular detect-then-segment approaches that first predict instances' bounding boxes, which are the current state-of-the-art in instance segmentation.
• We show that, by leveraging the strength of our state-of-the-art semantic segmentation models, the proposed method can achieve comparable or even better results to detect-then-segment approaches.