More than the Sum of Its Parts: Ensembling Backbone Networks for Few-Shot Segmentation

• URL: http://arxiv.org/abs/2402.06581v1
• Date: Fri, 9 Feb 2024 18:01:15 GMT
• Title: More than the Sum of Its Parts: Ensembling Backbone Networks for Few-Shot Segmentation
• Authors: Nico Catalano, Alessandro Maranelli, Agnese Chiatti, Matteo Matteucci
• Abstract summary: We study whether fusing features from different backbones can improve the ability of acrlongfss models to capture richer visual features. We propose and compare two ensembling techniques-Independent Voting and Feature Fusion. Our approach outperforms the original single-backbone PANet across standard benchmarks even in challenging one-shot learning scenarios.
• Score: 49.090592800481616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic segmentation is a key prerequisite to robust image understanding for applications in \acrlong{ai} and Robotics. \acrlong{fss}, in particular, concerns the extension and optimization of traditional segmentation methods in challenging conditions where limited training examples are available. A predominant approach in \acrlong{fss} is to rely on a single backbone for visual feature extraction. Choosing which backbone to leverage is a deciding factor contributing to the overall performance. In this work, we interrogate on whether fusing features from different backbones can improve the ability of \acrlong{fss} models to capture richer visual features. To tackle this question, we propose and compare two ensembling techniques-Independent Voting and Feature Fusion. Among the available \acrlong{fss} methods, we implement the proposed ensembling techniques on PANet. The module dedicated to predicting segmentation masks from the backbone embeddings in PANet avoids trainable parameters, creating a controlled `in vitro' setting for isolating the impact of different ensembling strategies. Leveraging the complementary strengths of different backbones, our approach outperforms the original single-backbone PANet across standard benchmarks even in challenging one-shot learning scenarios. Specifically, it achieved a performance improvement of +7.37\% on PASCAL-5\textsuperscript{i} and of +10.68\% on COCO-20\textsuperscript{i} in the top-performing scenario where three backbones are combined. These results, together with the qualitative inspection of the predicted subject masks, suggest that relying on multiple backbones in PANet leads to a more comprehensive feature representation, thus expediting the successful application of \acrlong{fss} methods in challenging, data-scarce environments.

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