MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation

TL;DR

MIANet introduces a novel approach for few-shot semantic segmentation by integrating semantic word embeddings and instance features through specialized modules, achieving state-of-the-art results on standard benchmarks.

Contribution

The paper proposes MIANet, which effectively combines general semantic knowledge and instance information using a triplet loss, hierarchical prior, and information fusion modules for improved segmentation accuracy.

Findings

Achieves superior performance on PASCAL-5i and COCO-20i datasets.

Sets new state-of-the-art in few-shot semantic segmentation.

Demonstrates effective transfer of semantic similarities from language to visual space.

Abstract

Existing few-shot segmentation methods are based on the meta-learning strategy and extract instance knowledge from a support set and then apply the knowledge to segment target objects in a query set. However, the extracted knowledge is insufficient to cope with the variable intra-class differences since the knowledge is obtained from a few samples in the support set. To address the problem, we propose a multi-information aggregation network (MIANet) that effectively leverages the general knowledge, i.e., semantic word embeddings, and instance information for accurate segmentation. Specifically, in MIANet, a general information module (GIM) is proposed to extract a general class prototype from word embeddings as a supplement to instance information. To this end, we design a triplet loss that treats the general class prototype as an anchor and samples positive-negative pairs from local features in the support set. The calculated triplet loss can transfer semantic similarities among language identities from a word embedding space to a visual representation space. To alleviate the model biasing towards the seen training classes and to obtain multi-scale information, we then introduce a non-parametric hierarchical prior module (HPM) to generate unbiased instance-level information via calculating the pixel-level similarity between the support and query image features. Finally, an information fusion module (IFM) combines the general and instance information to make predictions for the query image. Extensive experiments on PASCAL-5i and COCO-20i show that MIANet yields superior performance and set a new state-of-the-art. Code is available at https://github.com/Aldrich2y/MIANet.