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Learning from Exemplars for Interactive Image Segmentation by Li, Kun; Cheng, Hao; Vosselman, George; Yang, Michael Ying is a scholarly article available to read on EtoBox.

What is Learning from Exemplars for Interactive Image Segmentation about?

Interactive image segmentation enables users to interact minimally with a machine, facilitating the gradual refinement of the segmentation mask for a target of interest. Previous studies have demonstrated impressive performance in extracting a single target mask through interactive segmentation. However, the information cues of previously interacted objects have been overlooked in the existing methods, which can be further explored to speed up interactive segmentation for multiple targets in the same category. To this end, we introduce novel interactive segmentation frameworks for both a single object and multiple objects in the same category. Specifically, our model leverages transformer backbones to extract interaction-focused visual features from the image and the interactions to obtain a satisfactory mask of a target as an exemplar. For multiple objects, we propose an exemplar-informed module to enhance the learning of similarities among the objects of the target category. To combine attended features from different modules, we incorporate cross-attention blocks followed by a feature fusion module. Experiments conducted on mainstream benchmarks demonstrate that our models achie

Author

Li, Kun; Cheng, Hao; Vosselman, George; Yang, Michael Ying

Published

2024

Language

EN