Task Consistent Prototype Learning for Incremental Few-shot Semantic Segmentation
This addresses the challenge of incremental learning in semantic segmentation for computer vision applications, but it is incremental as it builds on existing meta-learning and prototype methods.
The paper tackles the problem of incremental few-shot semantic segmentation, where models must continually learn new classes with few examples while minimizing forgetting of old ones, and introduces a meta-learning-based prototype approach that improves performance on PASCAL and COCO benchmarks.
Incremental Few-Shot Semantic Segmentation (iFSS) tackles a task that requires a model to continually expand its segmentation capability on novel classes using only a few annotated examples. Typical incremental approaches encounter a challenge that the objective of the base training phase (fitting base classes with sufficient instances) does not align with the incremental learning phase (rapidly adapting to new classes with less forgetting). This disconnect can result in suboptimal performance in the incremental setting. This study introduces a meta-learning-based prototype approach that encourages the model to learn how to adapt quickly while preserving previous knowledge. Concretely, we mimic the incremental evaluation protocol during the base training session by sampling a sequence of pseudo-incremental tasks. Each task in the simulated sequence is trained using a meta-objective to enable rapid adaptation without forgetting. To enhance discrimination among class prototypes, we introduce prototype space redistribution learning, which dynamically updates class prototypes to establish optimal inter-prototype boundaries within the prototype space. Extensive experiments on iFSS datasets built upon PASCAL and COCO benchmarks show the advanced performance of the proposed approach, offering valuable insights for addressing iFSS challenges.