Incremental Few-Shot Instance Segmentation

Abstract: Few-shot instance segmentation methods are promising when labeled training data for novel classes is scarce. However, current approaches do not facilitate flexible addition of novel classes. They also require that examples of each class are provided at train and test time, which is memory intensive. In this paper, we address these limitations by presenting the first incremental approach to few-shot instance segmentation: iMTFA. We learn discriminative embeddings for object instances that are merged into class representatives. Storing embedding vectors rather than images effectively solves the memory overhead problem. We match these class embeddings at the RoI-level using cosine similarity. This allows us to add new classes without the need for further training or access to previous training data. In a series of experiments, we consistently outperform the current state-of-the-art. Moreover, the reduced memory requirements allow us to evaluate, for the first time, few-shot instance segmentation performance on all classes in COCO jointly.

Incremental Few-Shot Instance Segmentation: An Overview

The paper "Incremental Few-Shot Instance Segmentation" introduces a novel approach for addressing the challenges related to few-shot instance segmentation under an incremental learning paradigm. Traditional few-shot learning methods are limited by their inability to incorporate new classes efficiently without access to previous training data and without undergoing extensive retraining. The authors propose an incremental method, termed iMTFA, that aims to mitigate these limitations by learning discriminative embeddings for object instances, which can be used to integrate new classes flexibly and efficiently.

Key Contributions

1. Incremental Few-Shot Learning Framework: The paper presents iMTFA, the first framework designed explicitly for incremental few-shot instance segmentation. This methodology allows for the addition of new instance classes without requiring further access to the original training data or conducting significant retraining.

2. Discriminative Embeddings: iMTFA leverages discriminative embeddings for object instances, which are merged into class representatives. This approach is designed to tackle memory overhead problems associated with storing images, favoring embedding vectors instead. The embeddings are matched at the Region of Interest (RoI) level via cosine similarity, enabling seamless incorporation of new classes.

3. Class-Agnostic Components: The method includes a class-agnostic approach for mask prediction, eliminating the need for mask labels in the addition of novel classes. This is a significant departure from existing methods that require class-specific components and lengthy retraining processes.

4. Two-Stage Training Strategy: The authors describe a two-stage training process. The first stage involves training the Mask R-CNN on a set of base classes. The second stage fine-tunes the model using a few examples of novel classes, ensuring that new classes can be added incrementally with minimal computational overhead.

5. Performance and Efficacy: Experimental results demonstrate that iMTFA consistently outperforms the current state-of-the-art in few-shot instance segmentation. The model is evaluated on all classes in the COCO dataset jointly, showcasing its reduced memory requirements and robust performance.

Implications and Future Directions

• Theoretical and Practical Implications: This research contributes significantly to the theoretical framework of incremental learning and few-shot segmentation. By adopting a cosine-similarity-based metric learning approach, iMTFA introduces a methodologically sound way to deal with the practical limitations of memory overhead and retraining costs.

• Enhanced Model Adaptability: The proposed model enhances adaptability by storing minimal information (embedding vectors), making it highly efficient for real-world applications with evolving data landscapes.

• Potential for Cross-Domain Application: While developed for vision tasks, the principles underlying iMTFA could be adapted for incremental learning challenges in other domains, such as natural language processing or auditory scene analysis.

• Future AI Developments: This research lays the groundwork for further investigations into class-agnostic methods and embedding-based approaches, potentially influencing the development of future AI systems capable of dynamic learning and adaptation akin to human learning processes.

In summary, the approach outlined in "Incremental Few-Shot Instance Segmentation" represents a significant advancement in the area of few-shot learning by addressing key challenges of memory efficiency and model adaptability without retraining. The methodology and findings offer promising directions for both ongoing academic research and practical applications in dynamic and data-rich environments.