Task-Adaptive Feature Transformer for Few-Shot Segmentation
This work addresses the problem of semantic segmentation with few labeled samples for novel classes, representing an incremental improvement by adding a plug-in module to existing methods.
The authors tackled few-shot segmentation by proposing a task-adaptive feature transformer (TAFT) module that linearly transforms features to improve segmentation with limited data, achieving state-of-the-art performance on the PASCAL-5^i dataset in key cases.
Few-shot learning allows machines to classify novel classes using only a few labeled samples. Recently, few-shot segmentation aiming at semantic segmentation on low sample data has also seen great interest. In this paper, we propose a learnable module for few-shot segmentation, the task-adaptive feature transformer (TAFT). TAFT linearly transforms task-specific high-level features to a set of task-agnostic features well-suited to the segmentation job. Using this task-conditioned feature transformation, the model is shown to effectively utilize the semantic information in novel classes to generate tight segmentation masks. The proposed TAFT module can be easily plugged into existing semantic segmentation algorithms to achieve few-shot segmentation capability with only a few added parameters. We combine TAFT with Deeplab V3+, a well-known segmentation architecture; experiments on the PASCAL-$5^i$ dataset confirm that this combination successfully adds few-shot learning capability to the segmentation algorithm, achieving the state-of-the-art few-shot segmentation performance in some key representative cases.