Few-Shot Segmentation involves the task of segmenting objects or regions in images with the assistance of only a few annotated examples. Unlike traditional image segmentation methods that may require a large amount of labeled data, few-shot segmentation leverages a small set of labeled examples to train a model. This is particularly useful in scenarios where obtaining extensive labeled training data for every object or class is challenging.


MSI: Maximize Support-Set Information for Few-Shot Segmentation

MSI: Maximize Support-Set Information for Few-Shot Segmentation FSS (Few-shot segmentation) aims to segment a target class using a small number of labeled images (support set). To extract information relevant to the target class, a dominant approach in best performing FSS methods removes background features using a support mask. We observe that this feature excision through a limiting support mask introduces an information bottleneck in several challenging FSS cases, e.g., for small targets and/or inaccurate target boundaries. To this end, we present a novel method (MSI), which maximizes the support-set information by exploiting two complementary sources of features to generate super correlation maps. We validate the effectiveness of our approach by instantiating it into three recent and strong FSS methods. Experimental results on several publicly available FSS benchmarks show that our proposed method consistently improves performance by visible margins and leads to faster convergence.