Few-Shot Segmentation Propagation with Guided Networks (1806.07373v1)

Abstract: Learning-based methods for visual segmentation have made progress on particular types of segmentation tasks, but are limited by the necessary supervision, the narrow definitions of fixed tasks, and the lack of control during inference for correcting errors. To remedy the rigidity and annotation burden of standard approaches, we address the problem of few-shot segmentation: given few image and few pixel supervision, segment any images accordingly. We propose guided networks, which extract a latent task representation from any amount of supervision, and optimize our architecture end-to-end for fast, accurate few-shot segmentation. Our method can switch tasks without further optimization and quickly update when given more guidance. We report the first results for segmentation from one pixel per concept and show real-time interactive video segmentation. Our unified approach propagates pixel annotations across space for interactive segmentation, across time for video segmentation, and across scenes for semantic segmentation. Our guided segmentor is state-of-the-art in accuracy for the amount of annotation and time. See http://github.com/shelhamer/revolver for code, models, and more details.

• The paper introduces a guided network architecture that extracts latent task representations from sparse annotations for efficient few-shot segmentation.
• The model dynamically switches between segmentation tasks, supporting interactive video and semantic segmentation with minimal annotation.
• Empirical results show state-of-the-art accuracy and reduced computation time across various segmentation challenges.

Few-Shot Segmentation Propagation with Guided Networks

This paper presents an innovative approach to visual segmentation that addresses the limitations posed by traditional fully-supervised methods, specifically the heavy annotation requirements, fixed task definitions, and lack of correction mechanisms during inference. The research primarily introduces a framework for few-shot segmentation, wherein minimal image and pixel supervision is utilized to segment images efficiently. The authors propose a guided network architecture capable of extracting latent task representations from the given supervision and performing end-to-end optimization for swift and precise few-shot segmentation.

Key Contributions

The guided networks introduced in this work can dynamically switch between tasks without additional optimization and adapt quickly with further guidance. A notable achievement of the research is the demonstration of segmentation from just one pixel per concept, alongside real-time interactive video segmentation. The guided segmentor improves the state-of-the-art accuracy in scenarios with minimal annotation and limited computation time.

The proposed architecture excels in several segmentation tasks, providing a unified framework that propagates pixel annotations spatially in images, temporally in videos, and across scenes in semantic segmentation. This represents a significant step forward in interactive segmentation systems.

Technical Approaches

A central element of the proposed system is how it encodes guidance through task representations, which are extracted from sparse annotations. This method focuses on answering three core questions:

1. Summarization of Task Representations: How to derive a meaningful latent representation from a set of sparse, structured support annotations.
2. Guided Pixelwise Inference: How to condition the segmentation process on the task representation.
3. Synthesizing Segmentation Tasks: Strategies for achieving both high accuracy and generality.

The architecture is built on a branched fully convolutional network, where one branch extracts the task representation and the other performs pixelwise segmentation conditioned on this representation. This design supports efficient incorporation of new annotations and allows for quick adjustments to segmentations as more data becomes available.

Late-stage fusion is utilized for combining visual features and annotation-derived masks, which not only enhances data efficiency and learning time but also improves inference speeds. The paper compares various modes of guided inference, such as feature fusion and parameter regression, ultimately favoring feature fusion due to its superior performance.

Empirical Results

The guided networks have been tested across different segmentation problems, including interactive image segmentation, few-shot semantic segmentation, and video object segmentation, using metrics such as intersection-over-union (IU) for evaluation. The few-shot segmentor displayed remarkable performance across these tasks, with notorious adaptability to new tasks and significant improvements in the context of sparse annotation regimes.

For instance, within video object segmentation tasks, the method achieved competitive accuracy in scenarios traditionally requiring long optimization times by other methods, such as OSVOS. Moreover, the proposed techniques linked closely with few-shot learning methods were adapted successfully for structured output across complex datasets.

Implications and Future Directions

The implications of this research are both practical and theoretical. Practically, the reduction in annotation burden can significantly benefit domains where acquiring large annotated datasets is impractical, such as medical imaging or graphic design. Theoretically, this approach contributes to a more versatile understanding of task-adaptive neural networks and interactive machine learning by integrating guidance and learning from sparse annotations.

Future work may explore further optimization of the task representation extraction processes, potentially incorporating more sophisticated machine learning techniques to enhance the robustness of the task representations. Moreover, extending the capabilities of such architectures to handle even more varied and complex datasets can continue to push the boundaries of interactive AI systems. The research laid out in this paper provides a compelling foundation for further advancements in few-shot learning and interactive segmentation technologies.