Attentional Prototype Inference for Few-Shot Segmentation
This addresses the problem of few-shot segmentation for computer vision applications, offering an incremental improvement over existing prototype-based methods.
The paper tackles uncertainty and ambiguity in few-shot segmentation by proposing a probabilistic latent variable framework called attentional prototype inference (API), which models prototypes as distributions and uses attention maps to focus on foreground objects, achieving competitive or better performance than state-of-the-art methods on four benchmarks.
This paper aims to address few-shot segmentation. While existing prototype-based methods have achieved considerable success, they suffer from uncertainty and ambiguity caused by limited labeled examples. In this work, we propose attentional prototype inference (API), a probabilistic latent variable framework for few-shot segmentation. We define a global latent variable to represent the prototype of each object category, which we model as a probabilistic distribution. The probabilistic modeling of the prototype enhances the model's generalization ability by handling the inherent uncertainty caused by limited data and intra-class variations of objects. To further enhance the model, we introduce a local latent variable to represent the attention map of each query image, which enables the model to attend to foreground objects while suppressing the background. The optimization of the proposed model is formulated as a variational Bayesian inference problem, which is established by amortized inference networks. We conduct extensive experiments on four benchmarks, where our proposal obtains at least competitive and often better performance than state-of-the-art prototype-based methods. We also provide comprehensive analyses and ablation studies to gain insight into the effectiveness of our method for few-shot segmentation.